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Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint
In this paper, a multi-scale framework with local region based active co...
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Use of convexity in contour detection
In this paper, we formulate a simple algorithm that detects contours aro...
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Contour Detection in Cassini ISS images based on Hierarchical Extreme Learning Machine and Dense Conditional Random Field
In Cassini ISS (Imaging Science Subsystem) images, contour detection is ...
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Image Super-Resolution by Neural Texture Transfer
Due to the significant information loss in low-resolution (LR) images, i...
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Contour and Centreline Tracking of Vessels from Angiograms using the Classical Image Processing Techniques
This article deals with the problem of vessel edge and centerline detect...
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Adversarial Colorization Of Icons Based On Structure And Color Conditions
We present a system to help designers create icons that are widely used ...
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Contour Integration using Graph-Cut and Non-Classical Receptive Field
Many edge and contour detection algorithms give a soft-value as an outpu...
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An Image Analogies Approach for Multi-Scale Contour Detection
In this paper we deal with contour detection based on the recent image analogy principle which has been successfully used for super-resolution, texture and curves synthesis and interactive editing. Hand-drawn outlines are initially as benchmarks. Given such a reference image, we present a new method based on this expertise to locate contours of a query image in the same way that it is done for the reference (i.e by analogy). Applying a image analogies for contour detection using hand drawn images as leaning images cannot gives good result for any query image. The contour detection may be improved if we increase the number of learning images such that there will be exist similarity between query image and some reference images. In addition of the hardness of contours drawing task, this will increase considerably the time computation. We investigated in this work, how can we avoid this constraint in order to guaranty that all contour pixels will be located for any query image. Fourteen derived stereo patches, derived from a mathematical study, are the knowledge used in order to locate contours at different scales independently of the light conditions. Comprehensive experiments are conducted on different data sets (BSD 500, Horses of Weizmann). The obtained results show superior performance via precision and recall vs. hand-drawn contours at multiple resolutions to the reported state of the art.
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