Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear operator for oriented texture
IEEE Transactions on Image Processing
EdgeFlow: a technique for boundary detection and image segmentation
IEEE Transactions on Image Processing
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
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Boundary detection in natural images is a fundamental problem in many computer vision tasks. In this paper, we argue that early stages in primary visual cortex provide ample information to address the boundary detection problem. In other words, global visual primitives such as object and region boundaries can be extracted using local features captured by the receptive fields. The anatomy of visual cortex and psychological evidences are studied to identify some of the important underlying computational principles for the boundary detection task. A scheme for boundary detection based on these principles is developed and presented. Results of testing the scheme on a benchmark set of natural images, with associated human marked boundaries, show the performance to be quantitatively competitive with existing computer vision approaches.