Paper Info
Title
RISAS: A novel rotation, illumination, scale invariant appearance and shape feature.
Abstract
This paper presents a novel appearance and shape feature, RISAS, which is robust to viewpoint, illumination, scale and rotation variations. RISAS consists of a keypoint detector and a feature descriptor both of which utilise texture and geometric information present in the appearance and shape channels. A novel response function based on the surface normals is used in combination with the Harris corner detector for selecting keypoints in the scene. A strategy that uses the depth information for scale estimation and background elimination is proposed to select the neighbourhood around the keypoints in order to build precise invariant descriptors. Proposed descriptor relies on the ordering of both grayscale intensity and shape information in the neighbourhood. Comprehensive experiments which confirm the effectiveness of the proposed RGB-D feature when compared with CSHOT [1] and LOIND[2] are presented. Furthermore, we highlight the utility of incorporating texture and shape information in the design of both the detector and the descriptor by demonstrating the enhanced performance of CSHOT and LOIND when combined with RISAS detector.
Year
DOI
Venue
2017
10.1109/ICRA.2017.7989461
ICRA
Field
DocType
Volume
Computer vision,Scale invariance,Corner detection,Pattern recognition,Communication channel,Scale estimation,RGB color model,Invariant (mathematics),Artificial intelligence,Detector,Grayscale,Mathematics
Conference
2017
Issue
Citations 
PageRank 
1
2
0.38
References 
Authors
10
5
Name
Order
Citations
PageRank
Kanzhi Wu1312.69
Xiaoyang Li25310.12
Ravindra Ranasinghe3358.97
Gamini Dissanayake42226256.36
Yong Liu521345.82