A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
On the paper by R. M. Haralick
CVGIP: Image Understanding
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Computer and Robot Vision
Quantitative methods of evaluating image segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image-Segmentation Evaluation From the Perspective of Salient Object Extraction
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image segmentation: Rough-set theoretic approach
Pattern Recognition Letters
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Image segmentation algorithm development using ground truth image data sets
Computer Vision and Image Understanding
Dynamic Measurement of Computer Generated Image Segmentations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance evaluation of image segmentation
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neurocomputing
Neurocomputing
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Image segmentation is an important processing step in many image understanding algorithms and practical vision systems Various image segmentation algorithms have been proposed and most of them claim their superiority over others But in fact, no general acceptance has been gained of the goodness of these algorithms In this paper, we present a subjective method to assess the quality of image segmentation algorithms Our method involves the collection of a set of images belonging to different categories, optimizing the input parameters for each algorithm, conducting visual evaluation experiments and analyzing the final results We outline the framework through an evaluation of four state-of-the-art image segmentation algorithms—mean-shift segmentation, JSEG, efficient graph based segmentation and statistical region merging, and give a detailed comparison of their different aspects.