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
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
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
A Segmentation Algorithm Based on an Iterative Computation of the Mean Shift Filtering
Journal of Intelligent and Robotic Systems
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Image segmentation plays an important role in many systems of computer vision. The good performance of recognition algorithms depend on the quality of segmented image. According to the opinion of many authors the segmentation concludes when it satisfies the observer's objectives, the more effective methods being the iterative. However, a problem of these algorithms is the stopping criterion. In this work the entropy is used as stopping criterion in the segmentation process by using recursively the mean shift filtering. In such sense a new algorithm is introduced. The good performance of this algorithm is illustrated with extensive experimental results. The obtained results demonstrated that this algorithm is a straightforward extension of the filtering process. In this paper a comparison was carried out between the obtained results with our algorithm and with the EDISON System [16].