Bayesian Clustering for Unsupervised Estimation of Surface and Texture Models

Authors:
Judith F. Silverman;David B. Cooper
Affiliations:
Brown Univ., Providence, RI;Brown Univ., Providence, RI
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1988

Citing 3
Cited 13

On Optimally Combining Pieces of Information, with Application to Estimating 3-D Complex-Object Position from Range Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple Parallel Hierarchical and Relaxation Algorithms for Segmenting Noncausal Markovian Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence

Toward a Model-Based Bayesian Theory for Estimating and Recognizing Parameterized 3-D Objects Using Two or More Images Taken from Different Positions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Planar Curves, Surfaces, and Nonplanar Space Curves Defined by Implicit Equations with Applications to Edge and Range Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive image segmentation for radiation treatment planning

IBM Systems Journal
A Bayesian Segmentation Methodology for Parametric Image Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Practical Reliable Bayesian Recognition of 2D and 3D Objects Using Implicit Polynomials and Algebraic Invariants

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data clustering: a review

ACM Computing Surveys (CSUR)
Deformable Shape Detection and Description via Model-Based Region Grouping

IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape-Guided Split and Merge of Image Regions

IWVF-4 Proceedings of the 4th International Workshop on Visual Form
Image segmentation based on the integration of colour-texture descriptors-A review

Pattern Recognition
On considering uncertainty and alternatives in low-level vision

UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Birds of a feather surf together: using clustering methods to improve navigation prediction from internet log files

MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Unsupervised resolution independent based natural plant leaf disease segmentation approach for mobile devices

Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop

Quantified Score

Hi-index	0.14

Visualization

Abstract

A method of calculating the maximum-likelihood clustering for the unsupervised estimation of polynomial models for the data in images of smooth surfaces or for range data for such surfaces is presented. An image or a depth map of a region of smooth 3-D surface is modeled as a polynomial plus white noise. A region of physically meaningful textured-image such as the image of foliage, grass, or road in outdoor scenes or conductor or lintburn on a thick-film substrate is modeled as a colored Gaussian-Markov random field (MRF) with a polynomial mean-value function. Unsupervised-model parameter-estimation is accomplished by determining the segmentation and model parameter values that maximize the likelihood of the data or a more general Bayesian performance functional. Agglomerative clustering is used for this purpose.