The document describes techniques for image texture analysis and segmentation. It proposes a methodology using constraint satisfaction neural networks to integrate region-based and edge-based texture segmentation. The methodology initializes a CSNN using fuzzy c-means clustering, then iteratively updates the neuron probabilities and edge maps to refine the segmentation. Experimental results demonstrate improved segmentation by combining region and edge information.

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 . . .

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

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:

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