Image segmentation from consensus information
Computer Vision and Image Understanding
Normalized Cuts and Image Segmentation
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
On the coverings by tolerance classes
Information Sciences—Informatics and Computer Science: An International Journal
Thresholding technique with adaptive window selection for uneven lighting image
Pattern Recognition Letters
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
A novel approach for edge detection based on the theory of universal gravity
Pattern Recognition
Artificial Intelligence with Uncertainty
Artificial Intelligence with Uncertainty
Fast edge integration based active contours for color images
Computers and Electrical Engineering
Gray level difference-based transition region extraction and thresholding
Computers and Electrical Engineering
Vlfeat: an open and portable library of computer vision algorithms
Proceedings of the international conference on Multimedia
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Some innovative methods for image segmentation inspired by physical world are presented in recent years. Aiming to find homogeneous regions and latent semantic information, the paper presents a novel image segmentation method based on image data field. Image data field, developed by simulating the short-range nuclear forces field theory in the physical world, can effectively represent the spatial interactions of neighborhood pixels. Then, the homogeneous regions are characterized by maximum tolerance classes, which induced by homogeneous attraction relation comparing the contributions of potential values in image data field. More specifically, the proposed method mainly focuses on the images with uneven lighting conditions. Compared with the existing relative methods on a variety of images, the experimental results suggest that the presented method is efficient and effective.