  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.
