Title :
A Novel Local Surface Description for Automatic 3D Object Recognition in Low Resolution Cluttered Scenes
Author :
Shah, S.A.A. ; Bennamoun, Mohammed ; Boussaid, Farid ; El-Sallam, A.A.
Author_Institution :
Sch. of Comput. Sci. & Software Eng., Univ. of Western Australia, Crawley, WA, Australia
Abstract :
Local surface description is a critical stage for feature matching and recognition tasks. This paper presents a rotation invariant local surface descriptor, called 3D-Div. The proposed descriptor is based on the concept of 3D vector field´s divergence, extensively used in electromagnetic theory. To generate a 3D-Div descriptor of a 3D surface, a local surface patch is parameterized around a randomly selected 3D point at a fixed scale. A unique Local Reference Frame (LRF) is then constructed at that 3D point using all the neighboring points forming the patch. A normalized 3D vector field is then computed at each point in the patch and referenced with LRF vectors. The 3D-Div descriptor is finally generated as the divergence of the reoriented 3D vector field. We tested our proposed descriptor on the challenging low resolution Washington RGB-D (Kinect) object dataset, for the task of automatic 3D object recognition. Reported experimental results show that 3D-Div based recognition achieves 93% accuracy as compared to 85% for existing state-of-the-art depth kernel descriptors [2].
Keywords :
feature extraction; image matching; image resolution; image sensors; object recognition; 3D object recognition; 3D vector field; LRF; Washington RGB-D Kinect object dataset; automatic 3D object recognition; electromagnetic theory; feature matching; feature recognition; local reference frame; low resolution cluttered scenes; novel local surface description; Accuracy; Feature extraction; Object detection; Object recognition; Three-dimensional displays; Vectors; Video sequences;
Conference_Titel :
Computer Vision Workshops (ICCVW), 2013 IEEE International Conference on
Conference_Location :
Sydney, NSW
DOI :
10.1109/ICCVW.2013.88