The representation, recognition, and locating of 3-d objects
International Journal of Robotics Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation and Classification of Range Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Clustering for Unsupervised Estimation of Surface and Texture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmentation through Variable-Order Surface Fitting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling sensors: toward automatic generation of object recognition program
Computer Vision, Graphics, and Image Processing
Boundary Detection by Constrained Optimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture feature performance for image segmentation
Pattern Recognition
Texture Segmentation Using Voronoi Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrating Region Growing and Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian modeling of uncertainty in low-level vision
International Journal of Computer Vision
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum likelihood unsupervised textured image segmentation
CVGIP: Graphical Models and Image Processing
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Segmentation of 3D range images using pyramidal data structures
CVGIP: Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmenting Images Corrupted by Correlated Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Class of Discrete Multiresolution Random Fields and Its Application to Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context. Our approach does not require parameter estimation and is therefore particularly beneficial for cases in which estimation-based methods are most prone to error: when little information is contained in some of the regions and, therefore, parameter estimates are unreliable. We apply this formulation to three distinct parametric model families that have been used in past segmentation schemes: implicit polynomial surfaces, parametric polynomial surfaces, and Gaussian Markov random fields. We present results on a variety of real range and intensity images.