14. Integrating Region and Edge Information for Texture Segmentation We have used a modified constraint satisfaction neural networks termed as Constraint Satisfaction Neural Network for Complementary Information Integration (CSNN-CII), which integrates the region and edge based approaches. +
16. Constraint Satisfaction Neural Networks For Image Segmentation 1 < i < n 1 < j < n 1 < k < m Size of image: n x n No. of labels/classes: m Ref: [Lin92] i j k
17. Constraint Satisfaction Neural Network for Complementary Information Integration (CSNN-CII) Each neuron in CSNN-CII contains two fields: Probability and Rank. Probability: probability that the pixel belongs to the segment represented by the corresponding layer. Rank: Rank field stores the rank of the probability in a decreasing order, for that neuron. 0.1 0.5 0.4 Probabilities 3 1 2 Rank
18. The weight between k th layer’s ( i, j ) th , U ijk , neuron and l th layer’s ( q, r ) th , U qrl , neuron is computed as: Weights in the CSNN can be interpreted as constraints. Weights are determined based on the heuristic that a neuron excites other neurons representing the labels of similar intensities and inhibits other neurons representing labels of quite different intensities. Where, p : number of neurons in 2D neighborhood (dynamic window). m : number of layers (classes). U ijk : represents k th layer’s ( i , j ) th neuron. R ijk : Rank for ( i, j ) th neuron in k th layer or U ijk neuron. Ref: [Lin 92] U ijk U qrl W ij,qr,k,l
23. Where, Algorithm (Cont.) Labels to each pixel of an image are assigned as: Where, l l m Updated probability values: 0.2, 2 0.2, 2 0.8, 1 0.3, 2 0.6, 1 0.2, 2 0.6, 1 0.3, 2 0.6, 1 0.8, 1 0.8, 1 0.2, 2 0.7, 1 0.4, 2 0.8, 1 0.4, 2 0.7, 1 0.4, 2 2 2 1 2 1 2 1 2 1 Layer-1 Layer-2 Y
24. Updating Edge Map: B : Edge map obtained using lower threshold. E : Edge map obtained using higher threshold. M ij : the set of pixels in the neighborhood of pixel ( i , j ) in the output image Y of size 2 v+ 1 , excluding edge pixels in E. Algorithm (Cont.) Y E Edge map at each iteration is computed as:
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29. Input Image Segmented map before integration ( Ref: [Rao2004] ) Edge map before integration ( Ref: [Lalit2006] ) Segmented map and Edge map after integration Results
30. Results Input Image Segmented map before integration ( Ref: [Rao2004] ) Edge map before integration ( Ref: [Lalit2006] ) Segmented map and Edge map after integration