BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition

Jacob Chan, Jimmy Addison Lee, Qian Kemao; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2068-2076

Abstract


This paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description. Our proposed concept aligns layers of object-sized patches (nets) onto highly fragmented occlusion resistant line-segment midpoints (linelets) to encode regional information into efficient binary strings. These lightweight nets encourage discriminative object description through their high-spatial resolution, enabling highly precise encoding of the object's edges and internal texture-less information. BIND achieved various invariant properties such as rotation, scale and edge-polarity through its unique binary logical-operated encoding and matching techniques, while performing remarkably well in occlusion and clutter. Apart from yielding efficient computational performance, BIND also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors such as BORDER, BOLD and LINE2D.

Related Material


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[bibtex]
@InProceedings{Chan_2017_CVPR,
author = {Chan, Jacob and Addison Lee, Jimmy and Kemao, Qian},
title = {BIND: Binary Integrated Net Descriptors for Texture-Less Object Recognition},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}