Publication Type : Conference Paper
Publisher : Springer
Source : High Performance Computing and Networking
Url : https://link.springer.com/chapter/10.1007/978-981-16-9885-9_20
Campus : Coimbatore
School : School of Computing
Year : 2022
Abstract : Detecting the changes within a single image is significant for applications like planogram compliance where objects of same type are arranged in a sequence. This article discusses on detection of such objects which differ in color, texture and shape from other objects in the given image. In this proposed algorithm, the given image is converted to HSV color space and it is partitioned into regions; Fast Discrete Curvelet Transform (FDCT) coefficients are extracted. In order to identify changed regions in the given image, salient points are detected using the Features from Accelerated Segment Test (FAST) corner detection algorithm. It has been observed that the value channel content of the HSV image is much significant in extracting useful features for change detection. Experimental results show an efficacy of 87% in detecting changes in a single row of a shelf image having same items arranged in a linear fashion.
Cite this Research Publication : Bagyammal, T., Parameswaran Latha, and Vaiapury Karthikeyan. "Intra change detection in shelf images using fast discrete curvelet transform and features from accelerated segment test." In High Performance Computing and Networking: Select Proceedings of CHSN 2021, pp. 235-245. Singapore: Springer Singapore, 2022.