Authors
Ryuugo Mochizuki*, Kazuo Ishii
Center for Socio-Robotic Synthesis, Kyushu Institute of Technology, 2-4, Hibikino Wakamatsuku, Kitakyushu 808-0196, Japan
*Corresponding author. Email: [email protected]
Corresponding Author
Ryuugo Mochizuki
Received 26 November 2019, Accepted 19 February 2020, Available Online 18 May 2020.
DOI
https://doi.org/10.2991/jrnal.k.200512.004How to use a DOI?
Keywords
Saliency map; binary robust invariant scalable keypoint; keypoint stability
Abstract
The saliency map is proposed by Itti et al., to represent the conspicuity or saliency in the visual field and to guide the selection of attended locations based on the spatial distribution of saliency, which works as the trigger of bottom-up attention. If a certain location in the visual field is sufficiently different from its surrounding, we naturally pay attention to the characteristic of visual scene. In the research of computer vision, image feature extraction methods such as Scale-Invariant Feature Transform (SIFT), Speed-Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoint (BRISK) etc., have been proposed to extract keypoints robust to size change or rotation of target objects. These feature extraction methods are inevitable techniques for image mosaicking and Visual SLAM (Simultaneous Localization and Mapping), on the other hand, have big influence to photographing condition change of luminance, defocusing and so on. However, the relation between human attention model, Saliency map, and feature extraction methods in computer vision is not well discussed. In this paper, we propose a new saliency map and discuss the stability of keypoints extraction and their locations using BRISK by comparing other saliency maps.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).