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Object recognition based on convex hull alignment
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DOI：10.1016/j.patcog.2020.107199.png)
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Abstract
En 中文
A common approach to recognition of objects in cluttered scenes is to generate hypotheses about objects present in the scene by matching local descriptors of point features. These hypotheses are then evaluated by measuring how well they explain a particular part of the scene. In this paper, we investigate an alternative approach, which is based on alignment of convex hulls of segments detected in a depth image with convex hulls of target 3D object models or their parts. This alignment is performed using the Convex Template Instance descriptor. This descriptor was originally proposed for fruit recognition and classification of segmented objects. We have adapted this approach to recognize objects in complex scenes. Furthermore, we propose a novel three-level hypothesis evaluation strategy which can be used to achieve highly efficient object recognition. The proposed approach is evaluated by comparison with nine state-of-the-art approaches using three challenging benchmark datasets. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Object recognition
Shape instance detection
Depth image analysis
Convex hull
Shape alignment
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