A novel 3D vorticity based approach for automatic registration of low resolution range images. Issue 9 (September 2015)

• Record Type: Journal Article
• Authors: Shah, Syed Afaq Ali; Bennamoun, Mohammed; Boussaid, Farid
• Is Part Of: Pattern recognition. Volume 48:Issue 9(2015:Sep.)
• Page Start: 2859
• Page End: 2871
• Publication Date: 2015-09
• Subjects: Automatic registration -- Vector field -- Vorticity -- Range images; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2015.03.014
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: This paper tackles the problem of feature matching and range image registration. Our approach is based on a novel set of discriminating three-dimensional (3D) local features, named 3D-Vor ( Vor ticity). In contrast to conventional local feature representation techniques, which use the vector field (i.e. surface normals) to just construct their local reference frames, the proposed feature representation exploits the vorticity of the vector field computed at each point of the local surface to capture the distinctive characteristics at each point of the underlying 3D surface. The 3D-Vor descriptors of two range images are then matched using a fully automatic feature matching algorithm which identifies correspondences between the two range images. Correspondences are verified in a local validation step of the proposed algorithm and used for the pairwise registration of the range images. Quantitative results on low resolution Kinect 3D data (Washington RGB-D dataset) show that our proposed automatic registration algorithm is accurate and computationally efficient. The performance evaluation of the proposed descriptor was also carried out on the challenging low resolution Washington RGB-D (Kinect) object dataset, for the tasks of automatic range image registration. Reported experimental results show that the proposed local surface descriptor is robust to resolution, noise and more accurate than state-of-the-art techniques. It achieves 90% registration accuracy compared to 50%, 69.2% and 52% for spin image, 3D SURF and SISI/LD-SIFT descriptors, respectively. Abstract : Highlights: A novel local surface descriptor (3D-Vor) is proposed for surface representation. The proposed 3D-Vor exploits the vector field׳s vorticity. A novel pairwise registration algorithm is also proposed. 3D-Vor is tested on low resolution dataset for range image registration. 3D-Vor based registration achieves 90% accuracy on low resolution data and outperforms state-of-the-art techniques.