A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image processing and data analysis: the multiscale approach
Image processing and data analysis: the multiscale approach
On some detection and estimation problems in heavy-tailed noise
Signal Processing - Signal processing with heavy-tailed models
Time-frequency-based detection in impulsive noise environments using α-stable noise models
Signal Processing - Signal processing with heavy-tailed models
IEEE Transactions on Information Theory
Analysis of multiscale products for step detection and estimation
IEEE Transactions on Information Theory
Image compression via joint statistical characterization in the wavelet domain
IEEE Transactions on Image Processing
Wavelet domain image restoration with adaptive edge-preserving regularization
IEEE Transactions on Image Processing
Multiscale image segmentation using wavelet-domain hidden Markov models
IEEE Transactions on Image Processing
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Wavelet and curvelet moments for image classification: Application to aggregate mixture grading
Pattern Recognition Letters
Bayesian inference for multiband image segmentation via model-based cluster trees
Image and Vision Computing
| Hi-index | 0.11 |
We are concerned with the optimal selection of multiple thresholds in image analysis. We propose the use of the Bayes information criterion, a minimal information measure, for this and illustrate its use in practical cases. Applications of multiple threshold selection of interest to us include the closely related problems of (i) quantization for lossy encoding, and (ii) segmentation. Our examples relate to segmentation as a post-processing phase in edge detection.