Implementation of BRISK and BRISK-I Algorithm for Feature Detection of Object

Abhrajeet Basak, Soham Basu, Aishi Kundu, Aditya Banerjee

Abstract: Nowadays, RGB-D cameras offer both color and profundity pictures of the encompassing environment, making them an alluring alternative for automated and vision applications. This work presents the BRISK_I calculation, which productively combines Highlights from Quickened Section Test (Quick) and Double Vigorous Invariant Adaptable Key points (BRISK) strategies. Within the BRISK_I calculation, the key points are identified by the Quick calculation and the area of the key point is refined within the scale and the space. The scale calculate of the key point is specifically computed with the profundity data of the picture. Within the exploration, we have made a nitty gritty comparative examination of the two calculations BRISK and BRISK_I from the perspectives of scaling, turn, point of view and obscure. The BRISK_I calculation combines profundity data and has great calculation execution.

Keywords: RGB-D, BRISK, BRISK_I, algorithm, investigate, intensity centroid, keypoint, descriptor, coordinating score

How to Cite?: Abhrajeet Basak, Soham Basu, Aishi Kundu, Aditya Banerjee, "Implementation of BRISK and BRISK-I Algorithm for Feature Detection of Object", Volume 12 Issue 8, August 2023, International Journal of Science and Research (IJSR), Pages: 1710-1714, https://www.ijsr.net/getabstract.php?paperid=MR23731224725, DOI: https://dx.doi.org/10.21275/MR23731224725