Texture Segmentation Using Fractal Dimension
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Strategies for image segmentation combining region and boundary information
Pattern Recognition Letters
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
How Close Are We to Understanding V1?
Neural Computation
KSS: Using Region and Edge Maps to Detect Image Boundaries
Computing in Science and Engineering
A Strategy for Boundary Detection Combining Region and Edge Information
SIBGRAPI '11 Proceedings of the 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images
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This paper proposes a new unsupervised and fully automatic method to detect the boundaries in color natural images, inspired in the human visual model proposed by Grossberg. One of the hypotheses of Grossberg, the FACADE, admits complementary specialized streams at the bifurcation of the parvocellular pathway in the visual cortex: one of the branches performs edge processing and the other performs surface processing. In a similar way, this proposal has two parallel processes that are integrated at the end. The edge processing is implemented through a classical edge-detection method, whereas the surface processing is performed through a region growing method. The proposed integration scheme eliminates false contours resulted from the region growing guided by the result of edge detection, and eliminates the noise resulted from the edge detection as well, now guided by the result of the region growing, thus taking advantage of their complementary natures. Experiments on a large set of color images show that the results of the proposed system are closer to the human perception than the those correspondent to the individual methods (each branch), in quantitative and qualitative terms.