A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of digital image processing
Fundamentals of digital image processing
A Markov random field approach to data fusion and colour segmentation
Image and Vision Computing
The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A robust Markovian segmentation based on highest confidence first (HCF)
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Mean Shift Analysis and Applications
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Holonic based approach to machine vision
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Markov random field modeled level sets method for object tracking with moving cameras
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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Segmentation is an important research area in image processing and computer vision. The essential purpose of research work is to achieve two goals: (i) partition the image into homogeneous regions based on certain properties, and (ii) accurately track the boundary for each region. In this study, we will present a novel framework that is designed to fulfill these requirements. Distinguished from most existing approaches, our method consists of three steps in the segmentation processes: global region segmentation, control points searching and object boundary tracking. In step one, we apply Markov Random Field (MRF) modeling to multi-channel images and propose a robust energy minimization approach to solve the multi-dimensional Markov Random Field. In step two, control points are found along the target region boundary by using a maximum reliability criterion and deployed to automatically initialize a Minimum Path Approach (MPA). Finally, the active contour evolves to the optimal solution in the fine-tuning process. In this study, we have applied this framework to color images and multi-contrast weighting magnetic resonance image data. The experimental results show encouraging performance. Moreover, the proposed approach also has the potential to deal with topology changing and composite object problems in boundary tracking.