Granular computing, rough entropy and object extraction

The problem of image object extraction in the framework of rough sets and granular computing is addressed. A measure called "rough entropy of image" is defined based on the concept of image granules. Its maximization results in minimization of roughness in both object and background regions; thereby determining the threshold of partitioning. Methods of selecting the appropriate granule size and efficient computation of rough entropy are described

Granulation, rough entropy and spatiotemporal moving object detection

A new spatio-temporal segmentation approach for moving object(s) detection and tracking from a video sequence is described. Spatial segmentation is carried out using rough entropy maximization, where we use the quad-tree decomposition, resulting in unequal image granulation which is closer to natural granulation. A three point estimation based on Beta Distribution is formulated for background estimation during temporal segmentation. Reconstruction and tracking of the object in the target frame is performed after combining the two segmentation outputs using its color and shift information. The algorithm is more robust to noise and gradual illumination change, because their presence is less likely to affect both its spatial and temporal segments inside the search window. The proposed methods for spatial and temporal segmentation are seen to be superior to several related methods. The accuracy of reconstruction has been significantly high. © 2012 Elsevier B.V. All rights reserved

Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation

Effectiveness of various fuzzy thresholding techniques (based on entropy of fuzzy sets, fuzzy geometrical properties, and fuzzy correlation) is demonstrated on remotely sensed (IRS and SPOT) images. A new quantitative index for image segmentation using the concept of homogeneity within regions is defined. Results are compared with those of probabilistic thresholding, and fuzzy c-means and hard c-means clustering algorithms, both in terms of index value (quantitatively) and structural details (qualitatively). Fuzzy set theoretic algorithms are seen to be superior to their respective non-fuzzy counterparts. Among all the techniques, fuzzy correlation, followed by fuzzy entropy, performed better for extracting the structures. Fuzzy geometry based thresholding algorithms produced a single stable threshold for a wide range of membership variation

Granulation, rough entropy and spatiotemporal moving object detection

A new spatio-temporal segmentation approach for moving object(s) detection and tracking from a video sequence is described. Spatial segmentation is carried out using rough entropy maximization, where we use the quad-tree decomposition, resulting in unequal image granulation which is closer to natural granulation. A three point estimation based on Beta Distribution is formulated for background estimation during temporal segmentation. Reconstruction and tracking of the object in the target frame is performed after combining the two segmentation outputs using its color and shift information. The algorithm is more robust to noise and gradual illumination change, because their presence is less likely to affect both its spatial and temporal segments inside the search window. The proposed methods for spatial and temporal segmentation are seen to be superior to several related methods. The accuracy of reconstruction has been significantly high