A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A variational level set approach to multiphase motion
Journal of Computational Physics
Color image processing and applications
Color image processing and applications
A Level Set Model for Image Classification
International Journal of Computer Vision
Deformable Shape Detection and Description via Model-Based Region Grouping
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model Based Contour Searching Method
BIBE '00 Proceedings of the 1st IEEE International Symposium on Bioinformatics and Biomedical Engineering
A level set algorithm for minimizing the Mumford-Shah functional in image processing
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Region Segmentation via Deformable Model-Guided Split and Merge
Region Segmentation via Deformable Model-Guided Split and Merge
Automatic visual recognition of deformable objects for grasping and manipulation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmentation of color images using multiscale clustering and graph theoretic region synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A topology preserving level set method for geometric deformable models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Toward a generic evaluation of image segmentation
IEEE Transactions on Image Processing
A faster converging snake algorithm to locate object boundaries
IEEE Transactions on Image Processing
Deformable shape finding with models based on kernel methods
IEEE Transactions on Image Processing
Unsupervised multiscale color image segmentation based on MDL principle
IEEE Transactions on Image Processing
Adaptive Segmentation of Textured Images by Using the Coupled Markov Random Field Model
IEEE Transactions on Image Processing
Multigrid Geometric Active Contour Models
IEEE Transactions on Image Processing
Efficient image segmentation for region-based motion estimation and compensation
IEEE Transactions on Circuits and Systems for Video Technology
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
3D information extraction using Region-based Deformable Net for monocular robot navigation
Journal of Visual Communication and Image Representation
Image segmentation using fuzzy logic, neural networks and genetic algorithms: survey and trends
Machine Graphics & Vision International Journal
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This paper introduces a new color image segmentation framework that unifies contour deformation and region-based segmentation. Instead of deforming a single or multiple contours, typically used with classical deformable contour methods, the proposed framework deforms a single planar net that represents the contours of all the objects in the image. The net consists of a group of vertices connected by edges without crossing each other. The connected edges form polygons that represent the segmented regions boundaries. During the deformation process, the algorithm changes the location and the number of vertices as well as the number of polygons to enhance the segmentation fit. The deformation forces for each polygon are generated based upon the average color of the region and the color of the pixels surrounding it. The algorithm is completely autonomous and does not require any user interference, training or pre-knowledge about the image contents. The experimental results demonstrate the capability of the algorithm to segment color images from arbitrary sources within reasonable time. Furthermore, the compact mathematical representation of the resulting boundaries could be of value for further image analysis.