Improving 3D object detection and classification based on kinect sensor and hough transform

Hough Transform has been successfully applied to a variety of image processing problems in recent years. This papers presents a novel approach for detecting and classifying 3D objects by using the generalized Hough method and the Kinect^TM sensor. Our algorithm considers feature points and color spectra as two interleaved processes to cooperatively recognize objects in a 2.5D fashion. With this strategy, the algorithm automates the image pre-processing operations regardless of scenes (i.e., particle cleaning, hole filling, particle eroding, and object dilating) and reduces the processing load over the sensor´s point cloud for 3D object classification. Extensive experiments applied - but not limited - to recognition between different and similar objects, occlusion, and perspective change analyzing fitness and processing time show that the 2.5D approach makes feasible 3D object recognition for applications with video information.