Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
International Journal of Computer Vision
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
An empirical approach to grouping and segmentation
An empirical approach to grouping and segmentation
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
An edge-weighted centroidal Voronoi tessellation model for image segmentation
IEEE Transactions on Image Processing
Turbo Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Adaptive scale fixing for multiscale texture segmentation
IEEE Transactions on Image Processing
Image and Texture Segmentation Using Local Spectral Histograms
IEEE Transactions on Image Processing
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow
IEEE Transactions on Image Processing
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
Texture segmentation using Gaussian-Markov random fields and neural oscillator networks
IEEE Transactions on Neural Networks
Hi-index | 0.11 |
We present a novel method for segmenting images with texture and nontexture regions. Local spectral histograms are feature vectors consisting of histograms of chosen filter responses, which capture both texture and nontexture information. Based on the observation that the local spectral histogram of a pixel location can be approximated through a linear combination of the representative features weighted by the area coverage of each feature, we formulate the segmentation problem as a multivariate linear regression, where the solution is obtained by least squares estimation. Moreover, we propose an algorithm to automatically identify representative features corresponding to different homogeneous regions, and show that the number of representative features can be determined by examining the effective rank of a feature matrix. We present segmentation results on different types of images, and our comparison with other methods shows that the proposed method gives more accurate results.