Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
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
Color Segmentation Applied to Study of the Angiogenesis. Part I
Journal of Intelligent and Robotic Systems
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Nonparametric robust methods for computer vision
Nonparametric robust methods for computer vision
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
An image segmentation algorithm using iteratively the mean shift
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Color image segmentation for Bladder Cancer Diagnosis
Mathematical and Computer Modelling: An International Journal
A recursive thresholding technique for image segmentation
IEEE Transactions on Image Processing
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Image segmentation is accepted to be one of the most important problems in image analysis. The good performance of any recognition system strongly depends on the results provided by the segmentation module. According to many researchers, segmentation finishes when the goal of observer is satisfied. Experience has shown that the most effective methods continue to be the iterative algorithms. However, a problem with these algorithms is the stopping criterion. In this work, we present a strategy for image segmentation through a new algorithm based on recursively applying the mean shift filtering, where entropy is used as a stopping criterion. The main feature of the proposed algorithm is to carry out segmentation in an only step. In other words, with the new algorithm is not necessary to carry out additionally the segmentation step, where in many occasions (mainly in complex applications), it can be computationally expensive. The effectiveness of the proposed algorithm is shown through several experimental results. The obtained results proved that the proposed segmentation algorithm is a straightforward extension of the filtering process. In this paper a comparison between our algorithm and so called EDISON System was carried out.