INTERNATIONAL JOURNAL OF EMERGING TRENDS IN SCIENCE AND TECHNOLOGY · 2016 · 31 citations · 14 references
ComputingComputer ScienceComputer VisionDigital ImagingEdge ComputingEdge DetectionImage SegmentationObject DetectionObject RecognitionShape AnalysisDetectorsEdge FeaturesImage Processing AlgorithmsInformation GainInformation-gain Maximization
1 B-404,Neelsidhi Atlantis, PIIT, Navi Mumbai, India 2 Sector 19A,Nerul, New Panvel, Navi Mumbai, India Email: bindu_k_nair@hotmail.com, ujwal.harode@gmail.com ABSTRACT An important and very crucial component of many systems involving images is the edge detection which includes object detectors and image segmentation algorithms. Edge patches always shows shapes of its inherent structure, like T junctions or lines. Taking advantage of the shapes available in the images we get to know of an edge detector which is accurate and efficient. In this approach the structured labels is robustly mapped to a space that is discrete by which evaluation of the measure of the standard information gain is possible. The outcome is a way that shows a real time performance which is many times faster than any of the traditional and modern approaches available today, which also achieves the best results for edge detection both the Berkeley Segmentation Dataset and Benchmark (BSDS 500) and the NYU depth dataset. This approach also shows its strength as a an edge detector that can be used for any regular purpose by displaying how this method generalize across all datasets
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