Mumford-Shah regularizer with contextual feedback
Journal of Mathematical Imaging and Vision
IEEE Transactions on Signal Processing
Adaptive Contextual Energy Parameterization for Automated Image Segmentation
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy
IEEE Transactions on Image Processing
Adaptive regularization for image segmentation using local image curvature cues
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Image segmentation using local spectral histograms and linear regression
Pattern Recognition Letters
Multidimensional Systems and Signal Processing
Hi-index | 0.15 |
In this work we approach the analysis and segmentation of natural textured images by combining ideas from image analysis and probabilistic modeling. We rely on AM-FM texture models and specifically on the Dominant Component Analysis (DCA) paradigm for feature extraction. This method provides a low-dimensional, dense and smooth descriptor, capturing essential aspects of texture, namely scale, orientation, and contrast. Our contributions are at three levels of the texture analysis and segmentation problems: First, at the feature extraction stage we propose a Regularized Demodulation Algorithm that provides more robust texture features and explore the merits of modifying the channel selection criterion of DCA. Second, we propose a probabilistic interpretation of DCA and Gabor filtering in general, in terms of Local Generative Models. Extending this point of view to edge detection facilitates the estimation of posterior probabilities for the edge and texture classes. Third, we propose the Weighted Curve Evolution scheme that enhances the Region Competition/ Geodesic Active Regions methods by allowing for the locally adaptive fusion of heterogeneous cues. Our segmentation results are evaluated on the Berkeley Segmentation Benchmark, and compare favorably to current state-of-the-art methods.