Context dependent edge detection and evaluation
Pattern Recognition
Validation of the interleaved pyramid for the segmentation of 3D vector images
VIP '94 The international conference on volume image processing on Volume image processing
A new dichotomization technique to multilevel thresholding devoted to inspection applications
Pattern Recognition Letters
Empirical Evaluation Techniques in Computer Vision
Empirical Evaluation Techniques in Computer Vision
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
Stand-alone objective segmentation quality evaluation
EURASIP Journal on Applied Signal Processing - Image analysis for multimedia interactive services - part I
The Performance Evaluation of Thresholding Algorithms for Optical character Recognition
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
A Supervised Approach to the Evaluation of Image Segmentation Methods
CAIP '95 Proceedings of the 6th International Conference on Computer Analysis of Images and Patterns
Evaluation of global image thresholding for change detection
Pattern Recognition Letters
A Method for Objective Edge Detection Evaluation and Detector Parameter Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Evaluation of Image Segmentation Application to Multi-spectral Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Non-supervised image segmentation based on multiobjective optimization
Pattern Recognition Letters
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
WaterBalloons: A hybrid watershed Balloon Snake segmentation
Image and Vision Computing
An object-based comparative methodology for motion detection based on the F-Measure
Computer Vision and Image Understanding
A spatially distributed model for foreground segmentation
Image and Vision Computing
Expert Systems with Applications: An International Journal
Linguistic description about circular structures of the Mars' surface
Applied Soft Computing
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Image segmentation is a significant low-level method of the image processing area. As the matter of the fact that there is no selected certainty in interpreting the computer vision problems, there are many likely solutions. Some morphological methods used in image segmentation cause over-segmentation problems. Region merging, the usage of markers and the usage of multi-scale are the solutions for the over-segmentation problems found in the literature. However, these approaches give rise to under-segmentation problem. Simulated annealing (SA) is an optimization technique for soft computing. In our study, the problem of image segmentation is treated as a p-median (i.e., combinatorial optimization) problem. Therefore, the SA is used to solve p-median problem as a probabilistic metaheuristic. In the optimization method that is introduced in this paper, optimal threshold has been obtained for bi-level segmentation of grayscale images using our entropy-based simulated annealing (ESA) method. In addition, this threshold is used in determining optimal contour for edge-based image segmentation of grayscale images. Compared to the available methods (i.e., Otsu, only-entropy and Snake method) in the literature, our ESA method is more feasible in terms of performance measurements, threshold values and coverage area ratio of the region of interest (ROI).