Fundamentals of digital image processing
Fundamentals of digital image processing
Recovery of Nonrigid Motion and Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram clustering for unsupervised segmentation and image retrieval
Pattern Recognition Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour Continuity in Region Based Image Segmentation
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
A Bayesian Similarity Measure for Direct Image Matching
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The discrete modal transform and its application to lossy image compression
Image Communication
IEEE Transactions on Computers
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Reconstruction of serially acquired slices using physics-based modeling
IEEE Transactions on Information Technology in Biomedicine
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive perceptual color-texture image segmentation
IEEE Transactions on Image Processing
2-D Feature-Point Selection and Tracking Using 3-D Physics-Based Deformable Surfaces
IEEE Transactions on Circuits and Systems for Video Technology
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A robust fuzzy local information C-means clustering algorithm
IEEE Transactions on Image Processing
A de-texturing and spatially constrained K-means approach for image segmentation
Pattern Recognition Letters
Color image segmentation using pixel wise support vector machine classification
Pattern Recognition
MDS-based segmentation model for the fusion of contour and texture cues in natural images
Computer Vision and Image Understanding
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This paper presents a new approach for the segmentation of color textured images, which is based on a novel energy function. The proposed energy function, which expresses the local smoothness of an image area, is derived by exploiting an intermediate step of modal analysis that is utilized in order to describe and analyze the deformations of a 3-D deformable surface model. The external forces that attract the 3-D deformable surface model combine the intensity of the image pixels with the spatial information of local image regions. The proposed image segmentation algorithm has two steps. First, a color quantization scheme, which is based on the node displacements of the deformable surface model, is utilized in order to decrease the number of colors in the image. Then, the proposed energy function is used as a criterion for a region growing algorithm. The final segmentation of the image is derived by a region merge approach. The proposed method was applied to the Berkeley segmentation database. The obtained results show good segmentation robustness, when compared to other state of the art image segmentation algorithms.