New shape descriptor in the context of edge continuity. Issue 2 (31st May 2019)

• Record Type: Journal Article
• Authors: Susan, Seba; Agrawal, Prachi; Mittal, Minni; Bansal, Srishti
• Is Part Of: CAAI transactions on intelligence technology. Volume 4:Issue 2(2019)
• Page Start: 101
• Page End: 109
• Publication Date: 2019-05-31
• Subjects: image representation -- feature extraction -- object recognition -- edge detection -- image colour analysis -- learning (artificial intelligence); edge continuity features -- object contour -- intra‐class contour variations -- object recognition pipeline -- colour edge information -- identifying categorising objects -- noisy edge pixels -- adjacent edge pixels -- adjacent pixel; B6135 Optical, image and video signal processing -- B6135E Image recognition -- C1140Z Other topics in statistics -- C5260B Computer vision and image processing techniques -- C6170K Knowledge engineering techniques; Artificial intelligence -- Periodicals; Computer science -- Periodicals; Artificial intelligence; Computer science; Electronic journals; Periodicals; 006.305
• DOI: 10.1049/trit.2019.0002
• Languages: English
• ISSNs: 2468-6557

Abstract

Abstract : 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.