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arXiv · 1602.05920

Weighted Unsupervised Learning for 3D Object Detection

Kamran Kowsari·Manal H. Alassaf
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Abstract

This paper introduces a novel weighted unsupervised learning for object detection using an RGB-D camera. This technique is feasible for detecting the moving objects in the noisy environments that are captured by an RGB-D camera. The main contribution of this paper is a real-time algorithm for detecting each object using weighted clustering as a separate cluster. In a preprocessing step, the algorithm calculates the pose 3D position X, Y, Z and RGB color of each data point and then it calculates each data point's normal vector using the point's neighbor. After preprocessing, our algorithm calculates k-weights for each data point; each weight indicates membership. Resulting in clustered objects of the scene.

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BibTeXRIS

Kamran Kowsari, Manal H. Alassaf. 2018-06-04. Weighted Unsupervised Learning for 3D Object Detection. https://doi.org/10.14569/ijacsa.2016.070180

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