Initializing hand/finger articulation for tracking is a very challenging problem, mainly because hand articulation is complicated and it has a large number of degrees of freedom. Most existing algorithms initialize tracking manually, or use a nearest-neighbor search with restricting the number of possible hand gestures. This paper presents a new solution to this problem by increasing the dimensionality but taking advantage of the sparseness. The basic idea is to divide the set of phalange joint angles into many overlapping subsets. As each subset has a much smaller number of joint angles, it is much easier to design a smaller-scale articulation estimator. The estimation of the whole hand is done by the collaboration of a network of dependent smaller-scale estimators. This paper describes a novel way of designing the smaller-scale estimators as well as a principled way of fusing the estimates. A tracking system is also shown by using this initialization technique.

Most affinity-based grouping methods only model the inclusive relation among the data. When the data set contains a significant amount of noise data that should not be included in any clusters, these methods are likely to lead to undesired results. To address this issue, this paper presents a new approach called bipolar grouping that is targeted on extracting the groups from the data while excluding the noise. This new approach incorporates both inclusive and exclusive relations among data, and a fixed-point procedure is proposed to find the stable groups. Its effectiveness and general applicability are demonstrated in two applications, including discovering common objects in images and tracking targets in clutter.

Fusing partial estimates is a critical and common problem in many computer vision tasks such as part-based detection and tracking. It generally becomes complicated and intractable when there are a large number of multimodal partial estimates, and thus it is desirable to find an effective and scalable fusion method to integrate these partial estimates. This paper presents a novel and effective approach to fusing multimodal partial estimates in a principled way. In this new approach, fusion is related to a computational geometry problem of finding the minimum-volume orthotope, and an effective and scalable branch and bound search algorithm is designed to obtain the global optimal solution. Experiments on tracking articulated objects and occluded objects show the effectiveness of the proposed approach.