Open AccessNew shape descriptor in the context of edge continuity
Author(s) -
Susan Seba,
Agrawal Prachi,
Mittal Minni,
Bansal Srishti
Publication year - 2019
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2019.0002
Subject(s) - artificial intelligence , pixel , computer vision , enhanced data rates for gsm evolution , object (grammar) , clutter , context (archaeology) , edge detection , pattern recognition (psychology) , cognitive neuroscience of visual object recognition , computer science , feature (linguistics) , pipeline (software) , mathematics , image (mathematics) , image processing , geography , archaeology , telecommunications , radar , linguistics , philosophy , programming language
The object contour is a significant cue for identifying and categorising objects. The current work is motivated by indicative researches that attribute object contours to edge information. The spatial continuity exhibited by the edge pixels belonging to the object contour make these different from the noisy edge pixels belonging to the background clutter. In this study, the authors seek to quantify the object contour from a relative count of the adjacent edge pixels that are oriented in the four possible directions, and measure using exponential functions the continuity of each edge over the next adjacent pixel in that direction. The resulting computationally simple, low‐dimensional feature set, called as ‘edge continuity features’, can successfully distinguish between object contours and at the same time discriminate intra‐class contour variations, as proved by the high accuracies of object recognition achieved on a challenging subset of the Caltech‐256 dataset. Grey‐to‐RGB template matching with City‐block distance is implemented that makes the object recognition pipeline independent of the actual colour of the object, but at the same time incorporates colour edge information for discrimination. Comparison with the state‐of‐the‐art validates the efficiency of the proposed approach.