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A Fast Approach to Texture-Less Object Detection Based on Orientation Compressing Map and Discriminative Regional Weight

Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
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Algorithms 2018, 11(12), 201; https://doi.org/10.3390/a11120201
Received: 24 October 2018 / Revised: 4 December 2018 / Accepted: 6 December 2018 / Published: 12 December 2018
This paper presents a fast algorithm for texture-less object recognition, which is designed to be robust to cluttered backgrounds and small transformations. At its core, the proposed method demonstrates a two-stage template-based procedure using an orientation compressing map and discriminative regional weight (OCM-DRW) to effectively detect texture-less objects. In the first stage, the proposed method quantizes and compresses all the orientations in a neighborhood to obtain the orientation compressing map which then is used to generate a set of possible object locations. To recognize the object in these possible object locations, the second stage computes the similarity of each possible object location with the learned template by using discriminative regional weight, which can effectively distinguish different categories of objects with similar parts. Experiments on publiclyavailable, texture-less object datasets indicate that apart from yielding efficient computational performance, the proposed method also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors in the presence of high-clutter, occlusion and scale-rotation changes. It improves the accuracy and speed by 8% and 370% respectively, relative to the previous best result on D-Textureless dataset. View Full-Text
Keywords: texture-less object; object detection; template matching; possible object locations; orientation compressed map; real-time detection texture-less object; object detection; template matching; possible object locations; orientation compressed map; real-time detection
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Yu, H.; Qin, H.; Peng, M. A Fast Approach to Texture-Less Object Detection Based on Orientation Compressing Map and Discriminative Regional Weight. Algorithms 2018, 11, 201.

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