A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Game-Theoretic Integration for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Image Segmentation by Unifying Region and Boundary Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Segmentation using 2-D Gabor Elementary Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
A level set algorithm for minimizing the Mumford-Shah functional in image processing
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Texture Edge Detection using Multi-resolution Features and SOM
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Texture edge detection by feature encoding and predictive model
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Indoor versus outdoor scene classification using probabilistic neural network
EURASIP Journal on Applied Signal Processing
Image segmentation that merges together boundary and region information
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Segmentation of Gabor-filtered textures using deterministic relaxation
IEEE Transactions on Image Processing
Wavelet-based rotational invariant roughness features for texture classification and segmentation
IEEE Transactions on Image Processing
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
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In this paper, we propose an approach for texture segmentation by integrating region and edge information. The algorithm uses a constraint satisfaction neural network for texture segmentation with additional edge constraints. Initial class probabilities and edge maps are computed using multi-channel, multi-resolution filters to obtain image segmented map and edge map. The complementary information of the segmented map and the edge map are iteratively updated using a modified CSNN to satisfy a set of constraints to obtain superior segmentation results.The proposed methodology is tested on simulated as well as natural textures and it produces satisfactory results. The proposed methodology is also tested on a synthetic aperture radar (SAR) image.