Weighted Unsupervised Learning for 3D Object Detection

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.

@article{Kowsari2016,
  title     = {Weighted Unsupervised Learning for 3D Object Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Kamran Kowsari and Manal H. Alassaf},
  doi       = {10.14569/IJACSA.2016.070180},
  url       = {https://doi.org/10.14569/IJACSA.2016.070180}
}