In this paper we introduce a versatile and robust method for analyzing the feature space associated with a given surface. The method is based on the mean-shift operator which was shown to be successful in image and video processing. Its strength stems from the fact that it works in a single space of joint geometry and attributes called the feature-space. The feature-space attributes can be scal...

Mean shift is a nonparametric clustering technique that does not require the number of clusters in input and can find clusters of arbitrary shapes. While appealing, the performance of the mean shift algorithm is sensitive to the selection of the bandwidth, and can fail to capture the correct clustering structure when multiple modes exist in one cluster. DBSCAN is an efficient density based clus...

We present an improved framework for real-time segmentation and tracking by fusing depth and RGB color data. We are able to solve common problems seen in tracking and segmentation of RGB images, such as occlusions, fast motion, and objects of similar color. Our proposed real-time mean shift based algorithm outperforms the current state of the art and is significantly better in difficult scenarios.

Statistical analysis extracts characteristic features of an object class from raw training images

The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although it is important to adapt the mean-shift kernel to handle changes in illumination for robot vision at outdoor site, there is presently no clean mechanism for doing this. This paper presents a novel approach for color tracking that is robust to illumination changes for robot vision. We use two inte...

Article history: Received 17 April 2008 Available online 10 October 2008 Communicated by M.A. Girolami

An object tracking algorithm that uses a novel simple symmetric similarity function between spatially-smoothed kernel-density estimates of the model and target distributions is proposed and tested. The similarity measure is based on the expectation of the density estimates over the model or target images. The density is estimated using radial-basis kernel functions which measure the affinity be...

An object tracking algorithm using a novel simple symmetric similarity function between spatially-smoothed kernel-density estimates of the model and target distributions is proposed and tested. The similarity measure is based on the expectation of the density estimates over the model or target images. The density is estimated using radial-basis kernel functions that measure the affinity between...

Much of computer vision and image analysis involves the extraction of “meaningful” information from images using concepts akin to regression and model fitting. Applications include: robot vision, automated surveillance (civil and military) and inspection, biomedical image analysis, video coding, human-machine interface, visualization, historical film restoration etc. However, problems in comput...