A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Digital image processing
Three-dimensional image segmentation using a split, merge and group approach
Pattern Recognition Letters
Image thresholding: some new techniques
Signal Processing
Objective and quantitative segmentation evaluation and comparison
Signal Processing
Influence of segmentation over feature measurement
Pattern Recognition Letters
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quantitative evaluation of color image segmentation results
Pattern Recognition Letters
Edge detector evaluation using empirical ROC curves
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Comparison of edge detector performance through use in an object recognition task
Computer Vision and Image Understanding - Special issue on empirical evaluation of computer vision algorithms
Adaptive Image Segmentation by Combining Photometric Invariant Region and Edge Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Photometric Invariant Region Detection in Multispectral Images
International Journal of Computer Vision
Modelling of single mode distributions of colour data using directional statistics
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Quantitative methods of evaluating image segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Characterization of empirical discrepancy evaluation measures
Pattern Recognition Letters
Tuning range image segmentation by genetic algorithm
EURASIP Journal on Applied Signal Processing
Classifying color edges in video into shadow-geometry, highlight, or material transitions
IEEE Transactions on Multimedia
Robust segmentation and tracking of colored objects in video
IEEE Transactions on Circuits and Systems for Video Technology
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The development of common and reasonable criteria for evaluating and comparing the performance of segmentation algorithms has always been a concern for researchers in the area. As it is discussed in the paper, some of the measures proposed are not adequate for general images (i.e. images of any sort of scene, without any assumption about the features of the scene objects or the illumination distribution) because they assume a certain distribution of pixel gray-level or colour values for the interior of the regions. This paper reviews performance measures not performing such an assumption and proposes a set of new performance measures in the same line, called the percentage of correctly grouped pixels (CG), the percentage of over-segmentation (OS) and the percentage of under-segmentation (US). Apart from accounting for misclassified pixels, the proposed set of new measures are intended to compute the level of fragmentation of reference regions into output regions and vice versa. A comparison involving similar measures is provided at the end of the paper.