Weighted Unsupervised Learning for 3D Object Detection
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2016, Vol 7, Issue 1
Abstract
This paper introduces a novel weighted unsuper-vised 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.
Authors and Affiliations
Kamran Kowsari, Manal Alassaf
Keywords
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- EP ID EP133251
- DOI 10.14569/IJACSA.2016.070180
- Views 132
- Downloads 0
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