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Extraction of Salient Contours in Color Images Vonikakis Vasilios, Ioannis Andreadis and Antonios Gasteratos Democritus University of Thrace 2006 2006.

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Presentation on theme: "Extraction of Salient Contours in Color Images Vonikakis Vasilios, Ioannis Andreadis and Antonios Gasteratos Democritus University of Thrace 2006 2006."— Presentation transcript:

1 Extraction of Salient Contours in Color Images Vonikakis Vasilios, Ioannis Andreadis and Antonios Gasteratos Democritus University of Thrace 2006 2006

2 Presentation Overview  Problem definition  Biological background  Description of the model  Results  Conclusions

3 Definitions: Salient Contours Salient contours: The most evident contours that draw the attention of an observer Problem definition

4 Applications of salient contours  Create the ‘primal sketch’ of the image  Filter the optical data and keep only the significant information  Reduce the amount of visual information that a visual system processes Problem definition

5 The Human Visual System Biological background Retina Visual Cortex V1, V2… Optic nerve light (ganglion cells)

6 Double opponent cells Biological background  They are located in area V1  Two chromatic and one achromatic  They have a center-surround receptive field  They receive opposite signal to center and surround  They respond only to changes between center and surround – edges detectors R+G-B B-R-G G-R R-G -R-G-B R+G+B Blue-Yellow Red-Green Black-White

7 The primary visual cortex V1 Biological background  The visual cortex analyses the retinal output in 3 different maps: 1.Color (double-opponent cells) 2.motion-depth 3.orientation of edges  At every position of the visual field, the V1 has cells (orientation filters) of all possible orientations

8 Favorite connections of a horizontal orientation cell Biological background  Orientation cells prefer to be connected with others that create co-circular paths  This favors the smooth continuity of contours Connection of orientation cells

9 Block diagram of the model Description of the model Input Image Extraction of color edges Salient Contours network

10 Extracting color edges Description of the model Center Surround 9x9 mask  Similar to the double- opponent cells of V1

11 Extracting color edges Description of the model max { RG, BY, BW } RG BY BW

12 Orientation filters Description of the model 60 kernels 10×10 pixel size 12 orientations all possible positions within every orientation  The image is divided to 10×10 non-overlapping regions  For every region all 60 kernels are convolved  The higher response defines the kernel that best describes the orientation of the region Objective: to encode the orientation of the edges

13 Encoded edges Description of the model Color edge image Image with oriented filters Kernel 24:75° Kernel 19:135°

14 Computing the connection matrix Description of the model  We have calculated the connection matrix of all the 60 kernels, in a 5×5 kernel neighborhood Kernel 6 Kernel 17 Kernel 54 Connection matrix: weight [60] [5] [5] [60] weight [m] [ i ] [ j ] [n] : connection from kernel ‘m’ in the center of the to kernel ‘n’ in the (i,j) position weight [m] [ i ] [ j ] [n] : connection from kernel ‘m’ in the center of the 5×5 region, to kernel ‘n’ in the (i,j) position Connection matrix: weight [60] [5] [5] [60] weight [m] [ i ] [ j ] [n] : connection from kernel ‘m’ in the center of the to kernel ‘n’ in the (i,j) position weight [m] [ i ] [ j ] [n] : connection from kernel ‘m’ in the center of the 5×5 region, to kernel ‘n’ in the (i,j) position i j m n n n n n n n n n n n

15 Influence between kernels Description of the model Basic influence equation of kernel m (i,j) to kernel n out n (t) = out n (t-1) + weight m(i,j)→n × out m (t)  If weight m(i,j)→n >0 (kernel m is in the favorite curves of n) the influence is excitatory (out n (t)>out n (t-1))  If weight m(i,j)→n <0 (kernel m is not in the favorite curves of n) the influence is inhibitory (out n (t)<out n (t-1))

16 Activation function of kernel k Description of the model  Only the kernels with equal excitation in both lobes achieve high output  This favors the good continuation of salient contours F Lobe 1 Lobe 2  L1: total excitatory influence to Lobe 1  L2: total excitatory influence to Lobe 2  inh: total inhibitory influence k

17 Iterations Description of the model Oriented filters t=0 t=1 t=9 t=19  Salient contour kernels gradually increase their values  Kernels of non-salient contours gradually decrease their values  Usually 10 iterations are necessary

18 Results Original image Color edges Salient contours 700×700: 2.7 sec 700×576: 2.3 sec

19 More results Results Original image Color edges Salient contours 1000×768: 4.8 sec

20 More results Results Original image Color edges Salient contours 500×750: 2.1 sec 672×496: 1.8 sec

21 Conclusions  The proposed extraction of edges exhibits better results, especially for isoluminant areas, than the gradient of R,G and B  The proposed kernel set is an adequate way of coding the orientation of edges  The proposed method successfully extracts some of the most salient contours of the image  The execution time of the method when executed by a conventional PC is small compared to other saliency algorithms in the field

22 Thank you!


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