%****************************** AVERAGE ***************************** %Function: Smoothing with binary output. %TemLibName: Smoothing %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where black (white) pixels correspond to the locations in P where the average of pixel intensities over the r=1 feedback convolution window is above (under) a given threshold. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 AVERAGE_A = [ 0 1 0 1 2 1 0 1 0]; %****************************** AVERTRS1 ***************************** %Function: Smoothing with binary output. %TemLibName: Smoothing %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where black (white) pixels correspond to the locations in P where the average of pixel intensities over the r=1 feedback convolution window is above (under) a given threshold. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 AVERTRS1_A = [ 0 1.2 0 1.2 1.8 1.2 0 1.2 0]; AVERTRS1_B = [ 0 0 0 0 0 0 0 0 0]; AVERTRS1_I = 0; %****************************** AVERTRS2 ***************************** %Function: Smoothing with binary output. %TemLibName: Smoothing %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where black (white) pixels correspond to the locations in P where the average of pixel intensities over the r=1 feedback convolution window is above (under) a given threshold. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 AVERTRS2_A = [ 0 0.9 0 0.9 1.8 0.9 0 0.9 0]; AVERTRS2_B = [ 0 0 0 0 0 0 0 0 0]; AVERTRS2_I = 0; %****************************** AVERTRSH ***************************** %Function: Smoothing with binary output. %TemLibName: Smoothing %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where black (white) pixels correspond to the locations in P where the average of pixel intensities over the r=1 feedback convolution window is above (under) a given threshold. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 AVERTRSH_A = [ 0 1 0 1 2 1 0 1 0]; AVERTRSH_I = 0; %****************************** CCDMASKL ***************************** %Function: Masked (right-to-left) connected component detection. %TemLibName: MaskedCCD %CNNModel: Single-layer, CT-CNN %Given: Static binary images P1 (mask) and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary image that is the result of CCD type shifting P2 from right to left. Shifting is controlled by the mask P1. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CCDMASKL_A = [ 0 0 0 1 2 -1 0 0 0]; CCDMASKL_B = [ 0 0 0 0 -3 0 0 0 0]; CCDMASKL_I = -3; %****************************** CCDMASKR ***************************** %Function: Masked (left-to-right) connected component detection. %TemLibName: MaskedCCD %CNNModel: Single-layer, CT-CNN %Given: Static binary images P1 (mask) and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary image that is the result of CCD type shifting P2 from left to right. Shifting is controlled by the mask P1. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CCDMASKR_A = [ 0 0 0 -1 2 1 0 0 0]; CCDMASKR_B = [ 0 0 0 0 -3 0 0 0 0]; CCDMASKR_I = -3; %****************************** CCD_DIAG ***************************** %Function: Diagonal connected component detection. %TemLibName: DiagonalCCD %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image that shows the number of diagonally connected components in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CCD_DIAG_A = [ 1 0 0 0 2 0 0 0 -1]; %****************************** CCD_HOR ***************************** %Function: Horizontal connected component detection. %TemLibName: HorizontalCCD %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image that shows the number of horizontally connected components in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CCD_HOR_A = [ 0 0 0 1 2 -1 0 0 0]; %****************************** CCD_VERT ***************************** %Function: Vertical connected component detection. %TemLibName: VerticalCCD %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image that shows the number of vertically connected components in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 CCD_VERT_A = [ 0 1 0 0 2 0 0 -1 0]; %****************************** CENTER1 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER1_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER1_B = [ 1.0000 0.0000 0.0000 1.0000 4.0000 -1.0000 1.0000 0.0000 0.0000 ]; CENTER1_I = -1.0000; %****************************** CENTER2 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER2_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER2_B = [ 1.0000 1.0000 1.0000 1.0000 6.0000 0.0000 1.0000 0.0000 -1.0000 ]; CENTER2_I = -1.0000; %****************************** CENTER3 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER3_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER3_B = [ 1.0000 1.0000 1.0000 0.0000 4.0000 0.0000 0.0000 -1.0000 0.0000 ]; CENTER3_I = -1.0000; %****************************** CENTER4 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER4_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER4_B = [ 1.0000 1.0000 1.0000 0.0000 6.0000 1.0000 -1.0000 0.0000 1.0000 ]; CENTER4_I = -1.0000; %****************************** CENTER5 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER5_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER5_B = [ 0.0000 0.0000 1.0000 -1.0000 4.0000 1.0000 0.0000 0.0000 1.0000 ]; CENTER5_I = -1.0000; %****************************** CENTER6 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER6_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER6_B = [ -1.0000 0.0000 1.0000 0.0000 6.0000 1.0000 1.0000 1.0000 1.0000 ]; CENTER6_I = -1.0000; %****************************** CENTER7 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER7_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER7_B = [ 0.0000 -1.0000 0.0000 0.0000 4.0000 0.0000 1.0000 1.0000 1.0000 ]; CENTER7_I = -1.0000; %****************************** CENTER8 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CENTER8_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CENTER8_B = [ 1.0000 0.0000 -1.0000 1.0000 6.0000 0.0000 1.0000 1.0000 1.0000 ]; CENTER8_I = -1.0000; %****************************** CLDILA ***************************** %Function: Dilation (algo#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 CLDILA_A = [ 0 0 0 0 2 0 0 0 0]; CLDILA_B = [ 0 0 0 1 1 0 1 1 0]; CLDILA_I = 3.5; %****************************** CLERO ***************************** %Function: Erosion (algo#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 CLERO_A = [ 0 0 0 0 2 0 0 0 0]; CLERO_B = [ 0 1 1 0 1 1 0 0 0]; CLERO_I = -3.5; %****************************** CNTR1 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR1_A = [ 1 0 0 1 4 -1 1 0 0]; CNTR1_I = -1; %****************************** CNTR2 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR2_A = [ 1 1 1 1 6 0 1 0 -1]; CNTR2_I = -1; %****************************** CNTR3 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR3_A = [ 1 1 1 0 4 0 0 -1 0]; CNTR3_I = -1; %****************************** CNTR4 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR4_A = [ 1 1 1 0 6 1 -1 0 1]; CNTR4_I = -1; %****************************** CNTR5 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR5_A = [ 0 0 1 -1 4 1 0 0 1]; CNTR5_I = -1; %****************************** CNTR6 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR6_A = [ -1 0 1 0 6 1 1 1 1]; CNTR6_I = -1; %****************************** CNTR7 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR7_A = [ 0 -1 0 0 4 0 1 1 1]; CNTR7_I = -1; %****************************** CNTR8 ***************************** %Function: Center point detection. %TemLibName: CenterPointDetection (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CNTR8_A = [ 1 0 -1 1 6 0 1 1 1]; CNTR8_I = -1; %****************************** CONCCONT ***************************** %Function: Concentric contour detection. %TemLibName: ConcentricContourDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image where a black pixel indicates the approximated center point of the object in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CONCCONT_A = [ 0 -1 0 -1 3.5 -1 0 -1 0]; CONCCONT_B = [ 0 0 0 0 4 0 0 0 0]; CONCCONT_I = -4; %****************************** CONCEROS ***************************** %Function: Erosion (algo#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 CONCEROS_A = [ 0 0 0 0 2 0 0 0 0]; CONCEROS_B = [ 1 1 1 1 2 1 1 1 1]; CONCEROS_I = -0.5; %****************************** CONCHOLL ***************************** %Function: Hollow (algo#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 CONCHOLL_A = [ 0.5 0.5 0.5 0.5 2 0.5 0.5 0.5 0.5]; CONCHOLL_B = [ 0 0 0 0 2 0 0 0 0]; CONCHOLL_I = 3.5; %****************************** CONCTRES ***************************** %Function: Thresholding (algo#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 CONCTRES_A = [ 0 0 0 0 2 0 0 0 0]; CONCTRES_I = 0.0; %****************************** CONNECTI ***************************** %Function: Deletes marked objects. %TemLibName: Connectivity %CNNModel: Single-layer, CT-CNN %Given: Static binary images P1 (mask) and P2 (marker) %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary image containing only the unmarked objects in P1. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CONNECTI_A = [ 0 0.5 0 0.5 3 0.5 0 0.5 0]; CONNECTI_B = [ 0 -0.5 0 -0.5 3 -0.5 0 -0.5 0 ]; CONNECTI_I = -4.5; %****************************** CORNCH_1 ***************************** %Function: Convex corner detection. %TemLibName: CornerDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image where black pixels represent the convex corners of objects in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CORNCH_1_A = [ 0.0000 0.0000 0.0000 0.0000 1 0.0000 0.0000 0.0000 0.0000]; CORNCH_1_B = [ -1.0000 -1.0000 -1.0000 -1.0000 3.900 -1.0000 -1.0000 -1.0000 -1.0000]; CORNCH_1_I = -5.0000; %****************************** CORNCH_2 ***************************** %Function: Convex corner detection. %TemLibName: CornerDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image where black pixels represent the convex corners of objects in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CORNCH_2_A = [ 0.0000 0.0000 0.0000 0.0000 0.5000 0.0000 0.0000 0.0000 0.0000]; CORNCH_2_B = [ -1.0000 -1.0000 -1.0000 -1.0000 3.60000 -1.0000 -1.0000 -1.0000 -1.0000]; CORNCH_2_I = -5.0000; %****************************** CORNER ***************************** %Function: Convex corner detection. %TemLibName: CornerDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image where black pixels represent the convex corners of objects in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 CORNER_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; CORNER_B = [ -1.0000 -1.0000 -1.0000 -1.0000 4.0000 -1.0000 -1.0000 -1.0000 -1.0000 ]; CORNER_I = -5.0000; %****************************** DEADENDH ***************************** %Function: Finds the endings of horizontal (1-pixel wide) objects. %TemLibName: DeadEndH %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image of the endings of the horizontal (1-pixel wide) objects. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 DEADENDH_A = [ 0 0 0 0 3 0 0 0 0]; DEADENDH_B = [ -0.25 -0.25 -0.25 -0.25 0.5 -0.25 -0.25 -0.25 -0.25]; DEADENDH_I = -5.8; %****************************** DEADENDV ***************************** %Function: Finds the endings of vertical (1-pixel wide) objects. %TemLibName: DeadEndV %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image of the endings of the vertical (1-pixel wide) objects. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 DEADENDV_A = [ 0 0 0 0 3 0 0 0 0]; DEADENDV_B = [ -0.25 -0.25 -0.25 -0.25 0.5 -0.25 -0.25 -0.25 -0.25]; DEADENDV_I = -5.8; %****************************** DELDIAG1 ***************************** %Function: Deletes one pixel wide diagonal lines. %TemLibName: DiagonalLineRemover %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(-1) %Output: Binary image where black pixels have no black neighbors in diagonal directions in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 DELDIAG1_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; DELDIAG1_B = [ -1.0000 0.0000 -1.0000 0.0000 1.0000 0.0000 -1.0000 0.0000 -1.0000 ]; DELDIAG1_I = -4.0000; %****************************** DELVERT1 ***************************** %Function: Deletes vertical lines. %TemLibName: VerticalLineRemover %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(-1) %Output: Binary image representing P without vertical lines. Those parts of the objects that could be interpreted as vertical lines will also be deleted. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 DELVERT1_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; DELVERT1_B = [ 0.0000 -1.0000 0.0000 0.0000 1.0000 0.0000 0.0000 -1.0000 0.0000 ]; DELVERT1_I = -2.0000; %****************************** DIAG ***************************** %Function: Detects approximately diagonal lines. %TemLibName: ApproxDiagonalLineDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image representing the locations of approximately diagonal lines in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 2 DIAG_A = [ 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0]; DIAG_I = -13; DIAG_B = [ -1 -1 -1 0.5 1 -1 -1 1 1 0.5 -1 1 5 1 -1 0.5 1 1 -1 -1 1 0.5 -1 -1 -1]; %****************************** DIAG1LIU ***************************** %Function: Diagonal line-detector. %TemLibName: DiagonalLineDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image representing the locations of diagonal lines in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 DIAG1LIU_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; DIAG1LIU_B = [ -1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 -1.0000 ]; DIAG1LIU_I = -4.0000; %****************************** DIFFUS ***************************** %Function: Filtering-reconstruction with heat-diffusion. %TemLibName: HeatDiffusion. %CNNModel: Single-layer, CT-CNN %Given: Static (noisy) gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Grayscale image representing the result of the heat diffusion operation. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 DIFFUS_A = [ 0.1 0.15 0.1 0.15 0 0.15 0.1 0.15 0.1]; DIFFUS_I = 0; %****************************** DILATION ***************************** %Function: Binary dilation. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image representing the result of the dilation operation. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 DILATION_A = [ 0 0 0 0 0 0 0 0 0]; DILATION_B = [ 0 0 0 1 1 0 0 1 0]; DILATION_I = 2; %****************************** EDGE ***************************** %Function: Binary edge detection. %TemLibName: EdgeDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image showing all edges of P in black. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 EDGE_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; EDGE_B = [ -1.0000 -1.0000 -1.0000 -1.0000 8.0000 -1.0000 -1.0000 -1.0000 -1.0000 ]; EDGE_I = -1.0000; %****************************** EDGEGRAY ***************************** %Function: Gray-scale edge detection. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Gray-scale image showing an edge map of P in black. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 EDGEGRAY_A = [ 0 0 0 0 2 0 0 0 0]; EDGEGRAY_I = -0.5; EDGEGRAY_B = [ -1.0 -1.0 -1.0 -1.0 8.0 -1.0 -1.0 -1.0 -1.0]; %****************************** ERASMASK ***************************** %Function: Masked erase. %TemLibName: MaskedObjectExtractor %CNNModel: Single-layer, CT-CNN %Given: Static binary image P1 (mask) and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary image that is the result of erasing P2 from left to right. Erasure is stopped by the black walls on the mask (P1) image. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 ERASMASK_A = [ 0 0 0 1.5 3 0 0 0 0]; ERASMASK_B = [ 0 0 0 0 1.5 0 0 0 0]; ERASMASK_I = -1.5; %****************************** EROSION ***************************** %Function: Binary erosion. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image representing the result of the erosion operation. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 EROSION_A = [ 0 0 0 0 0 0 0 0 0]; EROSION_B = [ 0 1 0 0 1 1 0 0 0]; EROSION_I = -2; %****************************** FIGDEL ***************************** %Function: Extracts isolated black pixels. %TemLibName: FigureRemover %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image representing all isolated black pixels in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 FIGDEL_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; FIGDEL_B = [ -1.0000 -1.0000 -1.0000 -1.0000 1.0000 -1.0000 -1.0000 -1.0000 -1.0000 ]; FIGDEL_I = -8.0000; %****************************** FIGEXTR ***************************** %Function: Deletes isolated black pixels. %TemLibName: FigureExtractor %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image showing all connected components in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 FIGEXTR_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; FIGEXTR_B = [ 1.0000 1.0000 1.0000 1.0000 8.0000 1.0000 1.0000 1.0000 1.0000 ]; FIGEXTR_I = -1.0000; %****************************** FIGREC ***************************** %Function: Reconstructs marked figures. %TemLibName: FigureReconstructor %CNNModel: Single-layer, CT-CNN %Given: Two static binary images P1 (mask) and P2 (marker). P2 contains just a part of P1. %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary image representing those objects of P1 which are marked by P2. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 FIGREC_A = [ 0.5 0.5 0.5 0.5 4.0 0.5 0.5 0.5 0.5]; FIGREC_I = 3; FIGREC_B = [ 0 0 0 0 4 0 0 0 0]; %****************************** FILBLACK ***************************** %Function: Drives the whole network into black. %TemLibName: BlackFiller %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary (black) image. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 FILBLACK_A = [ 0 0 0 0 2 0 0 0 0]; FILBLACK_I = 4; %****************************** FILWHITE ***************************** %Function: Drives the whole network into white. %TemLibName: WhiteFiller %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary (white) image. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 FILWHITE_A = [ 0 0 0 0 2 0 0 0 0]; FILWHITE_I = -4; %****************************** FINDAREA ***************************** %Function: Finds solid black framed areas. %TemLibName: FramedAreasFinder %CNNModel: Single-layer, CT-CNN %Given: Two static binary images P1 (mask) and P2 (marker). %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary image representing those objects of P1 which are marked by P2. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 FINDAREA_A = [ 0 1 0 1 5 1 0 1 0]; FINDAREA_B = [ 0 0 0 0 2 0 0 0 0]; FINDAREA_I = -5.25; %****************************** HLF3 ***************************** %Function: 3x3 image halftoning. %TemLibName: 3x3Halftoning %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image preserving the main features of P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 HLF3_A = [ -0.07 -0.1 -0.07 -0.1 1.03 -0.1 -0.07 -0.1 -0.07]; HLF3_I = 0.0000; HLF3_B = [ 0.07 0.1 0.07 0.1 0.32 0.1 0.07 0.1 0.07]; %****************************** HLF5 ***************************** %Function: 5x5 image halftoning. %TemLibName: 5x5Halftoning2 %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image preserving the main features of P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 2 HLF5_A = [ -0.0245 -0.07 -0.099 -0.07 -0.0245 -0.07 -0.324 -0.46 -0.324 -0.07 -0.099 -0.46 1.05 -0.46 -0.099 -0.07 -0.324 -0.46 -0.324 -0.07 -0.0245 -0.07 -0.099 -0.07 -0.0245]; HLF5_I = 0; HLF5_B = [ 0.0245 0.07 0.099 0.07 0.0245 0.07 0.324 0.46 0.324 0.07 0.099 0.46 0.81 0.46 0.099 0.07 0.324 0.46 0.324 0.07 0.0245 0.07 0.099 0.07 0.0245]; %****************************** HLF5KC ***************************** %Function: 5x5 image halftoning. %TemLibName: 5x5Halftoning1 %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image preserving the main features of P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 2 HLF5KC_A = [ -0.030 -0.086 -0.130 -0.086 -0.030 -0.086 -0.359 -0.604 -0.359 -0.086 -0.130 -0.604 1.050 -0.604 -0.130 -0.086 -0.359 -0.604 -0.359 -0.086 -0.030 -0.086 -0.130 -0.086 -0.030]; HLF5KC_I = 0.0; HLF5KC_B = [ 0.000 0.000 0.068 0.000 0.000 0.000 0.355 0.756 0.355 0.000 0.068 0.756 2.122 0.756 0.068 0.000 0.355 0.756 0.355 0.000 0.000 0.000 0.068 0.000 0.000]; %****************************** HOLE ***************************** %Function: Performs hole filling. %TemLibName: HoleFiller %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:1, Boundary:Fixed(0) %Output: Binary image representing P with holes filled. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 HOLE_A = [ 0 1 0 1 3 1 0 1 0]; HOLE_I = -1; HOLE_B = [ 0 0 0 0 4 0 0 0 0]; %****************************** HOLLOW ***************************** %Function: Fills the concave locations of objects %TemLibName: ConcaveLocationFiller %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image in which the concave locations of objects are black. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 HOLLOW_A = [ 0.5 0.5 0.5 0.5 2 0.5 0.5 0.5 0.5]; HOLLOW_B = [ 0 0 0 0 2 0 0 0 0]; HOLLOW_I = 3.25; %****************************** HORLINE ***************************** %Function: Horizontal line detector. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Zero-flux %Output: Binary image, representing the horizontal lines in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 HORLINE_A = [ 0.0000 0.0000 0.0000 1.0000 2.0000 1.0000 0.0000 0.0000 0.0000]; HORLINE_B = [ 0 0 0 1 2 1 0 0 0]; HORLINE_I = -1; %****************************** HORSKELL ***************************** %Function: Horizontal skeleton from the left. %TemLibName: HorSkelL %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image, peeling the black pixels from the left of the object. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 HORSKELL_A = [ 0 0 0 0 3 0 0 0 0]; HORSKELL_B = [ 0.5 0 0.125 0.5 0.5 -0.5 0.5 0 0.125]; HORSKELL_I = -1; %****************************** HORSKELR ***************************** %Function: Horizontal skeleton from the right. %TemLibName: HorSkelR %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image, peeling the black pixels from the right of the object. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 HORSKELR_A = [ 0 0 0 0 3 0 0 0 0]; HORSKELR_B = [ 0.125 0 0.5 -0.5 0.5 -0.5 0.125 0 0.5]; HORSKELR_I = -1; %****************************** INCREASE ***************************** %Function: Increases the object by one pixel. %TemLibName: ObjectIncreasing %CNNModel: Single-layer, DT-CNN %Given: Static binary image P %Setting: Input:Arbitrary(0), IniState:P, Boundary:Zero-flux %Output: Binary image representing the objects of P increased by 1 pixel in all direction. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 INCREASE_A = [ .5 .5 .5 .5 .5 .5 .5 .5 .5]; INCREASE_I = 4; %****************************** INTERP ***************************** %Function: Interpolates a smooth surface through given points. %TemLibName: SurfaceInterpolation %CNNModel: Single-layer, CT-CNN %Given: A static grayscale image P1 and a static binary image P2 %Setting: Input:-, IniState:P1, Boundary:Fixed(0), BiasMap:-, FixedStateMap:P2 %Output: Grayscale image representing an interpolated surface that fits the given points and is as smooth as possible. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 2 INTERP_A = [ 0 0 -2 0 0 0 -4 16 -4 0 -2 16 -39 16 -2 0 -4 16 -4 0 0 0 -2 0 0]; %****************************** INVHLF3 ***************************** %Function: Inverts the halftoned image. %TemLibName: 3x3InverseHalftoning %CNNModel: Single-layer, CT-CNN %Given: Static binary image P obtained by using 3x3Halftoning %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Gray-scale image representing P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 INVHLF3_A = [ 0 0 0 0 0 0 0 0 0]; INVHLF3_I = 0.0000; INVHLF3_B = [ 0.07 0.1 0.07 0.1 0.32 0.1 0.07 0.1 0.07]; %****************************** INVHLF5 ***************************** %Function: Inverts the halftoned image. %TemLibName: 5x5InverseHalftoning %CNNModel: Single-layer, CT-CNN %Given: Static binary image P obtained by using 5x5Halftoning %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Gray-scale image representing P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 2 INVHLF5_A = [ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]; INVHLF5_I = 0; INVHLF5_B = [ 0.0049 0.014 0.0198 0.014 0.0049 0.014 0.0648 0.092 0.0648 0.014 0.0198 0.092 0.162 0.092 0.0198 0.014 0.0648 0.092 0.0648 0.014 0.0049 0.014 0.0198 0.014 0.0049]; %****************************** JUNCTION ***************************** %Function: Extracts the junctions of a skeleton. %TemLibName: JunctionExtractor %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image showing the junctions of a skeleton. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 JUNCTION_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; JUNCTION_B = [ 1.0000 1.0000 1.0000 1.0000 6.0000 1.0000 1.0000 1.0000 1.0000 ]; JUNCTION_I = -3; %****************************** LAPLACE ***************************** %Function: Solves the Laplace PDE (Dx = 0). %TemLibName: LaplacePDESolver %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:Arbitrary(0), IniState:P, Boundary:Zero-flux %Output: Gray-scale image - the solution of the Laplace equation. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 LAPLACE_A = [ 0 1 0 1 -3 1 0 1 0]; %****************************** LCP ***************************** %Function: Local concave place detector. %TemLibName: LocalConcavePlaceDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image showing the local concave places of P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 LCP_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; LCP_B = [ 0.0000 0.0000 0.0000 2.0000 2.0000 2.0000 1.0000 -2.0000 1.0000 ]; LCP_I = -7.0000; %****************************** LINCUT7H ***************************** %Function: Deletes horizontal lines not longer than 7 pixels. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(-1) %Output: Binary image where black pixels identify the horizontal lines with a length of 8 or more pixels in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 2 LINCUT7H_A = [ 0 0 0 0 0 0 0 0 0 0 1 0.5 2 1 0.5 0 0 0 0 0 0 0 0 0 0]; LINCUT7H_I = -5.5; LINCUT7H_B = [ 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0]; %****************************** LINCUT7V ***************************** %Function: Deletes vertical lines not longer than 7 pixels. %TemLibName: LE7pixelVerticalLineRemover %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(-1) %Output: Binary image where black pixels identify the vertical lines with a length of 8 or more pixels in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 2 LINCUT7V_A = [ 0 0 1 0 0 0 0 0.5 0 0 0 0 2 0 0 0 0 0.5 0 0 0 0 1 0 0]; LINCUT7V_I = -5.5; LINCUT7V_B = [ 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0]; %****************************** LINEXTR3 ***************************** %Function: Lines-not-longer-than-3-pixels detector . %TemLibName: LE3pixelLineDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image representing only lines not longer than 3 pixels in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % New version of the template % Length tuning in several directions: detection of lines with length % less or equal to 3 pixels %neighborhood: 2 LINEXTR3_A = [ 0 0 0 0 0 0 0.3 0.3 0.3 0 0 0.3 3 0.3 0 0 0.3 0.3 0.3 0 0 0 0 0 0]; LINEXTR3_B = [ -1 0 -1 0 -1 0 -1 -1 -1 0 -1 -1 4 -1 -1 0 -1 -1 -1 0 -1 0 -1 0 -1]; LINEXTR3_I = -2; %****************************** LOGAND ***************************** %Function: Logic AND (and Set Intersection). %TemLibName: LogicAND %CNNModel: Single-layer, CT-CNN %Given: Two static binary images P1 and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary output of the logic operation AND between P1 and P2 (Set Intersection). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 LOGAND_A = [ 0 0 0 0 2 0 0 0 0]; LOGAND_I = -1; LOGAND_B = [ 0 0 0 0 1 0 0 0 0]; %****************************** LOGDIF ***************************** %Function: Logic Difference (alt: Relative Set Complement). %TemLibName: LogicDifference1 %CNNModel: Single-layer, CT-CNN %Given: Two static binary images P1 and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary image representing the set-theoretic, or logic complement of P2 relative to P1. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 LOGDIF_A = [ 0 0 0 0 2 0 0 0 0]; LOGDIF_I = -1; LOGDIF_B = [ 0 0 0 0 -1 0 0 0 0]; %****************************** LOGNOT ***************************** %Function: Logic NOT (alt: Set Complementation) %TemLibName: LogicNOT %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image where each black pixel in P becomes white, and vice versa. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 LOGNOT_A = [ 0 0 0 0 1 0 0 0 0]; LOGNOT_I = 0; LOGNOT_B = [ 0 0 0 0 -2 0 0 0 0]; %****************************** LOGOR ***************************** %Function: Logic OR (alt: Set Union). %TemLibName: LogicOR %CNNModel: Single-layer, CT-CNN %Given: Two static binary images P1 and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary output of the logic operation OR between P1 and P2 (Set Union). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 LOGOR_A = [ 0 0 0 0 2 0 0 0 0]; LOGOR_I = 1; LOGOR_B = [ 0 0 0 0 1 0 0 0 0]; %****************************** LOGORN ***************************** %Function: Logic OR function of the initial state and logic NOT function of the input. %TemLibName: LogicORwithNOT %CNNModel: Single-layer, CT-CNN %Given: Two static binary images P1 and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(0) %Output: Binary output of the logic operation OR between NOT P1 and P2. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 LOGORN_A = [ 0 0 0 0 2 0 0 0 0]; LOGORN_I = 1; LOGORN_B = [ 0 0 0 0 -1 0 0 0 0]; %****************************** LSE ***************************** %Function: Local southern element detector. %TemLibName: LocalSouthernElementDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image representing local southern elements of objects in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 LSE_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; LSE_B = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 -1.0000 -1.0000 -1.0000 ]; LSE_I = -3.0000; %****************************** MAJVOT1 ***************************** %Function: Majority vote-taker. %TemLibName: MajorityVoteTaker (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image (Phase 1). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 MAJVOT1_A = [ 0 0 0 1 0 0 0 0 0]; MAJVOT1_B = [ 0 0 0 0 0.05 0 0 0 0]; MAJVOT1_I = 0; %****************************** MAJVOT3 ***************************** %Function: Majority vote-taker (compares the sum in a local neigborhood to the specified threshold). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image - black pixels mark those locations where the sum in local neighborhood (r=1) exceeds the specified threshold. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 MAJVOT3_A = [ 0 0 0 0 1 0 0 0 0]; MAJVOT3_B = [ 1 1 1 1 1 1 1 1 1]; MAJVOT3_I = -6.5; %****************************** MATCH ***************************** %Function: Finds matching patterns. %TemLibName: PatternMatchingFinder %CNNModel: Single-layer, CT-CNN %Given: Static binary image P possessing the 3x3 pattern prescribed by the template. %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image representing the locations of the 3x3 pattern prescribed by the template. The pattern having a black/white pixel where the template value is +1/-1, respectively, is detected. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 MATCH_A = [ 0 0 0 0 1 0 0 0 0]; MATCH_I = -6.5; MATCH_B = [ 1 -1 1 0 1 0 1 -1 1]; %****************************** MULLER ***************************** %Function: Simulates the Müller-Lyer illusion. %TemLibName: MüllerLyerIllusion %CNNModel: Single-layer, CT-CNN %Given: Static binary image P representing two horizontal lines between arrows. The arrows are black, the background is white. %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image showing that the horizontal line on the top in P seems to be longer than the other one. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Center-surround template simulating the Muller-Lyer illusion %neighborhood: 1 MULLER_A = [ 0 0 0 0 1.3 0 0 0 0]; MULLER_I = -2.8; %neighborhood: 2 MULLER_B = [ -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 1.3 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1 -.1]; %****************************** PATCHMAK ***************************** %Function: Patch maker. %TemLibName: PatchMaker %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Zero-flux %Output: Binary image with enlarged objects of P obtained after a certain time. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 PATCHMAK_A = [ 0 1 0 1 2 1 0 1 0]; PATCHMAK_B = [ 0 0 0 0 1 0 0 0 0]; PATCHMAK_I = 4.5; %****************************** PEEL1PIX ***************************** %Function: Peel one pixel from all directions (#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 PEEL1PIX_A = [ 0 0.4 0 0.4 1.4 0.4 0 0.4 0]; PEEL1PIX_I = -7.2; PEEL1PIX_B = [ 4.6 -2.8 4.6 -2.8 1 -2.8 4.6 -2.8 4.6]; %****************************** PEELHOR ***************************** %Function: Peels one pixel from the left. %TemLibName: LeftPeeler %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Zero-flux %Output: Binary image representing the objects of P peeled with one pixel from the left. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 PEELHOR_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; PEELHOR_B = [ 0.0000 0.0000 0.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 ]; PEELHOR_I = -1.0000; %****************************** POISSON ***************************** %Function: Solves the Poisson PDE (Dx = -f(x)). %TemLibName: PoissonPDESolver %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale images P1 and P2 = -f(x) %Setting: Input:Arbitrary(0), IniState:P1, Boundary:Zero-flux, BiasMap:P2 %Output: Gray-scale image - the solution of the Poisson equation. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 POISSON_A = [ 0 1 0 1 -3 1 0 1 0]; %****************************** PROP1 ***************************** %Function: Trigger-wave generator (expands the black regions). %TemLibName: - %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Zero-flux %Output: Binary image with enlarged objects of P obtained after a certain time. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 PROP1_A = [ 0.25 0.25 0.25 0.25 3.0 0.25 0.25 0.25 0.25 ]; %****************************** PROP2 ***************************** %Function: Trigger-wave generator (expands the white regions). %TemLibName: - %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Zero-flux %Output: Binary image with reduced objects of P obtained after a certain time. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 PROP2_A = [ 0.25 0.25 0.25 0.25 3.0 0.25 0.25 0.25 0.25 ]; %****************************** RECALL ***************************** %Function: Figure reconstruction from markers. %TemLibName: FigureReconstructor %CNNModel: Single-layer, CT-CNN %Given: Two static binary images P1 (mask) and P2 (marker) %Setting: Input:P1, IniState:P2, Boundary:Zero-flux %Output: Binary image representing those objects of P1 which are marked by P2. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 RECALL_B = [ 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 0.0]; RECALL_A = [ 0.5 0.5 0.5 0.5 4.0 0.5 0.5 0.5 0.5]; RECALL_I = 2.5; %****************************** RIGHTBC ***************************** %Function: Right (diagonal) contour detection (#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 RIGHTBC_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; RIGHTBC_B = [ 1.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 -1.0000]; RIGHTBC_I = -2.0000; %****************************** RIGHTCON ***************************** %Function: Right contour detector. %TemLibName: RightContourDetector %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Binary image representing the right edges of objects in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 RIGHTCON_A = [ 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000]; RIGHTCON_B = [ 0.0000 0.0000 0.0000 1.0000 1.0000 -1.0000 0.0000 0.0000 0.0000 ]; RIGHTCON_I = -2.0000; %****************************** SHADMASK ***************************** %Function: Creates a masked right shadow of the object. %TemLibName: MaskedShadow %CNNModel: Single-layer, CT-CNN %Given: Static binary images P1 and P2 %Setting: Input:P1, IniState:P2, Boundary:Fixed(-1) %Output: Binary image representing the result of pattern propagation of P2 in a particular direction. The propagation goes from the direction of the non-zero off-center feedback template entry and is halted by the mask P1. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 SHADMASK_A = [ 0 0 0 0 1.8 1.5 0 0 0]; SHADMASK_I = 0; SHADMASK_B = [ 0 0 0 0 -1.2 0 0 0 0]; %****************************** SHADOW ***************************** %Function: Creates the left shadow of the object. %TemLibName: LeftShadow %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:1, Boundary:Fixed(0) %Output: Binary image representing the left shadow of the objects in P. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 SHADOW_A = [ 0 0 0 0 2 2 0 0 0]; SHADOW_I = 0; SHADOW_B = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHADSIM ***************************** %Function: Creates a vertical shadow of the object (in both directions). %TemLibName: VerticalShadow %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:P, Boundary:Zero-flux %Output: Binary image representing the vertical shadow of the objects in P taken upward and downward simultaneously. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 SHADSIM_A = [ 0 1 0 0 2 0 0 1 0]; SHADSIM_I = 2; %****************************** SHIFTE ***************************** %Function: Shifts the image toward eastern direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the eastern direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTE_B = [ 0 0 0 1 0 0 0 0 0]; SHIFTE_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHIFTN ***************************** %Function: Shifts the image toward northern direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the northern direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTN_B = [ 0 0 0 0 0 0 0 1 0]; SHIFTN_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHIFTNE ***************************** %Function: Shifts the image toward north-estern direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the north-estern direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTNE_B = [ 0 0 0 0 0 0 1 0 0]; SHIFTNE_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHIFTNW ***************************** %Function: Shifts the image toward north-western direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the north-western direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTNW_B = [ 0 0 0 0 0 0 0 0 1]; SHIFTNW_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHIFTS ***************************** %Function: Shifts the image toward southern direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the southern direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTS_B = [ 0 1 0 0 0 0 0 0 0]; SHIFTS_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHIFTSE ***************************** %Function: Shifts the image toward south-estern direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the south-estern direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTSE_B = [ 1 0 0 0 0 0 0 0 0]; SHIFTSE_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHIFTSW ***************************** %Function: Shifts the image toward south-western direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the south-western direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTSW_B = [ 0 0 1 0 0 0 0 0 0]; SHIFTSW_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SHIFTW ***************************** %Function: Shifts the image toward western direction. %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:0, Boundary:Zero-flux %Output: Binary image - P is shifted toward the western direction by one pixel. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SHIFTW_B = [ 0 0 0 0 0 1 0 0 0]; SHIFTW_A = [ 0 0 0 0 2 0 0 0 0]; %****************************** SKELBW1 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 1 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW1_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW1_B = [ 1 1 0 1 7 -1 0 -1 0 ]; SKELBW1_I = -3; %****************************** SKELBW2 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 2 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW2_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW2_B = [ 1 1 1 0 7 0 -0.5 -1 -0.5 ]; SKELBW2_I = -3.4; %****************************** SKELBW3 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 3 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW3_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW3_B = [ 0 1 1 -1 7 1 0 -1 0 ]; SKELBW3_I = -3; %****************************** SKELBW4 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 4 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW4_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW4_B = [ -0.5 0 1 -1 7 1 -0.5 0 1 ]; SKELBW4_I = -3.4; %****************************** SKELBW5 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 5 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW5_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW5_B = [ 0 -1 0 -1 7 1 0 1 1 ]; SKELBW5_I = -3; %****************************** SKELBW6 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 6 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW6_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW6_B = [ -0.5 -1 -0.5 0 7 0 1 1 1 ]; SKELBW6_I = -3.4; %****************************** SKELBW7 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 7 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW7_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW7_B = [ 0 -1 0 1 7 -1 1 1 0 ]; SKELBW7_I = -3; %****************************** SKELBW8 ***************************** %Function: The algorithm finds the skeleton of a black-and-white object. %TemLibName: BlackandWhiteSkeletonization (Algorithm!) %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Zero-flux %Output: Binary image (Phase 8 of the skeletonization algorithm). %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 SKELBW8_A = [ 0 0 0 0 1 0 0 0 0]; SKELBW8_B = [ 1 0 -0.5 1 7 -1 1 0 -0.5 ]; SKELBW8_I = -3.4; %****************************** SMKILLER ***************************** %Function: Deletes small objects. %TemLibName: SmallObjectRemover %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image representing P without small objects. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 SMKILLER_A = [ 1 1 1 1 2 1 1 1 1]; %****************************** T1_RACC3 ***************************** %Function: Textures detection. %TemLibName: 3x3TextureSegmentation %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P representing textures having the same flat grayscale histograms %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Nearly binary image where the detected texture becomes darker than the others. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 T1_RACC3_A = [ 2.2656 1.7969 3.3594 -0.7031 -4.4531 1.4063 3.2031 3.9844 -0.3125 ]; T1_RACC3_I = -1.6406; T1_RACC3_B = [ -3.9063 1.2500 3.0469 0.8594 -3.0469 3.3594 1.7188 -0.6250 -4.6094 ]; %****************************** T2_RACC3 ***************************** %Function: Textures detection. %TemLibName: 3x3TextureSegmentation %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P representing textures having the same flat grayscale histograms %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Nearly binary image where the detected texture becomes darker than the others. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 T2_RACC3_A = [ 1.5625 4.3750 2.4219 4.6875 -3.1250 1.4063 2.1875 -5.0000 0.8594 ]; T2_RACC3_I = -3.2031; T2_RACC3_B = [ -2.8125 2.4219 -3.7500 -5.0000 -0.3906 -5.0000 3.6719 4.2188 3.1250 ]; %****************************** T3_RACC3 ***************************** %Function: Textures detection. %TemLibName: 3x3TextureSegmentation %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P representing textures having the same flat grayscale histograms %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Nearly binary image where the detected texture becomes darker than the others. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 T3_RACC3_A = [ 1.6406 -1.0156 1.3281 1.8750 -4.6094 2.8906 3.2813 2.0313 3.7500 ]; T3_RACC3_I = -2.4219; T3_RACC3_B = [ -3.9063 -2.6563 -3.1250 0.9375 1.4844 -3.1250 1.3281 0.5469 2.3438 ]; %****************************** T4_RACC3 ***************************** %Function: Textures detection. %TemLibName: 3x3TextureSegmentation %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P representing textures having the same flat grayscale histograms %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Nearly binary image where the detected texture becomes darker than the others. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 T4_RACC3_A = [ 3.1250 4.2969 2.1875 -2.8125 3.1250 0.1563 1.8750 4.9219 4.5313 ]; T4_RACC3_I = -2.4219; T4_RACC3_B = [ -3.5156 4.3750 -5.0000 -0.9375 -3.0469 -3.6719 1.4063 -0.6250 -4.3750 ]; %****************************** TEXTUDIL ***************************** %Function: Dilation (algo#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 TEXTUDIL_A = [ 0 0 0 0 0 0 0 0 0]; TEXTUDIL_B = [ 0 1 0 1 1 1 0 1 0]; TEXTUDIL_I = 4; %****************************** TEXTUERO ***************************** %Function: Erosion (algo#). %TemLibName: - ? %CNNModel: Single-layer, CT-CNN %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 TEXTUERO_A = [ 0 0 0 0 0 0 0 0 0]; TEXTUERO_B = [ 0 1 0 1 1 1 0 1 0]; TEXTUERO_I = -4; %****************************** THRES ***************************** %Function: Grayscale to binary threshold template %TemLibName: Threshold %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where black pixels correspond to pixels in P with grayscale intensity above the given threshold. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 THRES_A = [ 0.000 0.000 0.000 0.000 2.000 0.000 0.000 0.000 0.000]; THRES_I = -0.4; %****************************** TRESHOLD ***************************** %Function: Grayscale to binary threshold template %TemLibName: Threshold %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P %Setting: Input:-, IniState:P, Boundary:Fixed(0) %Output: Binary image where black pixels correspond to pixels in P with grayscale intensity above the given threshold. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %Neighborhood: 1 TRESHOLD_A = [ 0 0 0 0 2 0 0 0 0]; TRESHOLD_I = -0.4; %****************************** TX_HCLC ***************************** %Function: Segmentation of four textures. %TemLibName: 5x5TextureSegmentation1 %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P representing four textures %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Nearly binary image representing four patterns that differ in average gray-levels. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 2 TX_HCLC_A = [ -3.4375 0.8594 -1.6406 -0.1563 -1.0156 -1.0938 0.1563 -2.1875 -3.2031 3.5156 2.5000 1.5625 3.9063 2.6563 2.4219 0.5469 2.8906 -0.6250 0.4688 3.6719 -1.7969 -0.5469 2.5000 -0.2344 2.3438 ]; TX_HCLC_I = 3.2813; TX_HCLC_B = [ -2.1875 -0.2344 0.1563 -0.6250 -0.7813 1.6406 2.2656 -3.2031 1.0938 2.0313 0.0781 0.5469 0.8594 3.5156 0.0781 0.3906 -3.8281 -3.1250 -2.3438 -2.1094 0.7813 -2.6563 -1.1719 -1.4063 1.0156 ]; %****************************** TX_RACC3 ***************************** %Function: Segmentation of four textures. %TemLibName: 3x3TextureSegmentation %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P representing four textures %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Nearly binary image representing four patterns that differ in average gray-levels. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 1 TX_RACC3_A = [ 0.8594 0.9375 3.7500 2.1094 -2.8125 3.7500 -1.3281 -2.5781 -1.0156 ]; TX_RACC3_I = 1.7969; TX_RACC3_B = [ 0.1563 -1.5625 1.2500 -2.8906 1.0938 -3.2031 4.0625 4.6875 3.7500 ]; %****************************** TX_RACC5 ***************************** %Function: Segmentation of four textures. %TemLibName: 5x5TextureSegmentation2 %CNNModel: Single-layer, CT-CNN %Given: Static gray-scale image P representing four textures %Setting: Input:P, IniState:P, Boundary:Fixed(0) %Output: Nearly binary image representing four patterns that differ in average gray-levels. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %neighborhood: 2 TX_RACC5_A = [ 4.2188 -1.5625 1.5625 3.3594 0.6250 -2.8906 4.5313 -0.2344 3.1250 -2.8906 2.6563 2.1875 -4.6875 -3.4375 -2.8125 3.9844 1.5625 -1.1719 -3.1250 -3.2031 -3.7500 -2.1875 3.2813 2.1875 -0.6250 ]; TX_RACC5_I = -5.0000; TX_RACC5_B = [ 4.0625 -5.0000 0.3906 2.1094 -1.8750 3.9063 0.3125 -1.9531 4.8438 -0.3125 0.0000 -4.0625 0.9375 -0.3125 0.4688 -0.6250 -5.0000 2.3438 0.6250 -1.8750 3.5938 -0.9375 0.1563 2.8125 -1.8750 ]; %****************************** VERSKELB ***************************** %Function: Vertical skeleton from the bottom. %TemLibName: VerSkelB %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image, peeling the black pixels from the bottom of the object. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 VERSKELB_A = [ 0 0 0 0 3 0 0 0 0]; VERSKELB_B = [ 0.125 -0.5 0.125 0 0.5 0 0.5 -0.5 0.5]; VERSKELB_I = -1; %****************************** VERSKELT ***************************** %Function: Vertical skeleton from the top. %TemLibName: VerSkelT %CNNModel: Single-layer, CT-CNN %Given: Static binary image P %Setting: Input:P, IniState:Arbitrary(0), Boundary:Fixed(0) %Output: Binary image, peeling the black pixels from the top of the object. %Timing: - %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %NEIGHBORHOOD: 1 VERSKELT_A = [ 0 0 0 0 3 0 0 0 0]; VERSKELT_B = [ 0.5 0.5 0.5 0 0.5 0 0.125 -0.5 0.125]; VERSKELT_I = -1;