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- 1. Image Texture Analysis Lalit Gupta, Scientist, Philips Research
- 2. Texture Analysis Region based texture segmentation Textured image + Texture Edge Detection
- 3. Region Based Texture Segmentation
- 4. Image histograms R1 R2 R3 R4 R1 R2 R3 R4
- 5. Classification using Proposed Methodology Image DWT: Discrete wavelet transform DCT: Discrete cosine transform Ref: [Randen99] A1 V1 H1 D1 1 ST level Decomposition DWT (Daubechies) D j D j Filtering FCM Unsupervised classification DCT (9 masks) DCT (9 masks) . . Gaussian filtering G j G j Smoothing . . Mean F j F j Feature extraction . .
- 6. Input Image Steps of Processing DWT A1 V1 H1 D1 FCM .. .. .. DCT . . . .. .. .. Smoothing . . . .. .. .. Mean 36 Feature images . . .
- 7. Results using various Filtering Techniques (a) Input Image <ul><ul><li>Ref: [Ng92], [Rao2004], [Cesmeli2001] </li></ul></ul>(b) DWT (c) Gabor filter (b) DWT+Gabor (d) GMRF (e) DWT + MRF (f) DCT (f) DWT+DCT
- 8. Results (Cont.) I1 I2 I3 I4 I5 Input images I6 I7 I8 I9 I10
- 9. Results (Cont.)
- 10. Texture Edge Detection
- 11. Proposed Methodology Input image Ref: [Liu99], [Canny86], [Yegnanarayana98] Filtering using 1-D Discrete Wavelet Transform and 1-D Gabor filter bank 16 dimensional feature vector is mapped onto one dimensional feature map Self-Organizing feature Map (SOM) Smoothed image Smoothing using 2-D symmetric Gaussian filter Edge map Edge detection using Canny operator Final edge map Edge Linking Smoothed images Smoothing using 2-D asymmetric Gaussian filter . . . 16 filtered images, 8 each along horizontal and vertical parallel lines of image . . .
- 12. Steps of Processing Input image Filtered images ... ... Smoothed images Feature map Smoothed images Edge map
- 13. Results Input image Edge map Input image Edge map Input image Edge map
- 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. +
- 15. Dynamic Window Image Window
- 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
- 19. Algorithm <ul><li>Phase 1: </li></ul><ul><ul><li>Initialize the CSNN neurons using fuzzy c-means results. </li></ul></ul><ul><ul><ul><li>The probability values obtained from FCM are assigned to the nodes of CSNN. Ranks for each neuron are also computed on the basis of initial class probabilities. </li></ul></ul></ul>FCM output 0.2 0.2 0.8 0.3 0.6 0.2 0.6 0.3 0.6 0.8 0.8 0.2 0.7 0.4 0.8 0.4 0.7 0.4 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 Rank Probability CSNN-CII Layer-1 Layer-2
- 20. H ijk : sum of inputs from all neighboring neurons. O ijk : the probability of ( i , j ) th pixel having a label k (Probability value assigned to the U ijk neuron) . N ij : a set of neurons in the 3D neighborhood of ( i,j ) th neuron (considering Dynamic window). <ul><ul><li>Iterate and update the probabilities, edge map and determine the winner label </li></ul></ul>Algorithm (Cont.) U ijk H ijk i j k
- 21. CSNN-CII Layer-1 Layer-2 Algorithm (Cont.) Edge information 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 For neurons with rank=1 For neurons with rank=2 1 0 0 1 0 0 1 0 0
- 22. Algorithm (Cont.) CSNN-CII Layer-1 Layer-2 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
- 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:
- 25. <ul><ul><li>Check the convergence condition, i.e., the number of pixels updated in Y , at each iteration. If there is any update go to second step. </li></ul></ul>Algorithm (Cont.) Edge map at each iteration is computed as: B Y Updated edge map ( E ) E M
- 26. <ul><li>Phase 2 </li></ul><ul><ul><li>Iterate, and update edge map E, by removing extra edge pixels and by adding new edge pixels. </li></ul></ul>Algorithm (Cont.) L ij is considered as: Edge map E is updated as: Y
- 27. <ul><ul><li>Merge Edge map and Segmented map to get final output. </li></ul></ul>Finally, new edge pixels are added where E ij = 0 and min( L ij ) max( L ij ) Algorithm (Cont.) E Y Updated edge map ( E ) E Y Updated edge map (E)
- 28. <ul><ul><li>Merge Edge map and Segmented map to get final output. </li></ul></ul>Final Output Segmented map Edge map
- 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

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