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Appl. Sci. 2018, 8(6), 902; https://doi.org/10.3390/app8060902

Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images

1
College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
2
Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110004, China
*
Authors to whom correspondence should be addressed.
Received: 14 April 2018 / Revised: 28 May 2018 / Accepted: 29 May 2018 / Published: 31 May 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

Superpixel segmentation is a widely used preprocessing method in computer vision, but its performance is unsatisfactory for color images in cluttered indoor environments. In this work, a superpixel method named weighted coplanar feature clustering (WCFC) is proposed, which produces full coverage of superpixels in RGB-depth (RGBD) images of indoor scenes. Basically, a linear iterative clustering is adopted based on a cluster criterion that measures the color similarity, space proximity and geometric resemblance between pixels. However, to avoid the adverse impact of RGBD image flaws and to make full use of the depth information, WCFC first preprocesses the raw depth maps with an inpainting algorithm called a Cross-Bilateral Filter. Second, a coplanar feature is extracted from the refined RGBD image to represent the geometric similarities between pixels. Third, combined with the colors and positions of the pixels, the coplanar feature constructs the feature vector of the clustering method; thus, the distance measure, as the cluster criterion, is computed by normalizing the feature vectors. Finally, in order to extract the features of the RGBD image dynamically, a content-adaptive weight is introduced as a coefficient of the coplanar feature, which strikes a balance between the coplanar feature and other features. Experiments performed on the New York University (NYU) Depth V2 dataset demonstrate that WCFC outperforms the available state-of-the-art methods in terms of accuracy of superpixel segmentation, while maintaining a high speed. View Full-Text
Keywords: depth maps; RGB-depth; segmentation; simple linear iterative clustering superpixels depth maps; RGB-depth; segmentation; simple linear iterative clustering superpixels
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Fang, Z.; Yu, X.; Wu, C.; Chen, D.; Jia, T. Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images. Appl. Sci. 2018, 8, 902.

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