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Digital Object Identifier (DOI) : 10.14569/IJACSA.2016.070180
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 1, 2016.
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.
Kamran Kowsari and Manal H. Alassaf, “Weighted Unsupervised Learning for 3D Object Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 7(1), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070180