Object tracking

TL;DR: The validation showed that a designated target can be reliably tracked within several pixels or a few centimeters of accuracy over a 10-m traverse using a rover step size of 10% of the target distance in any direction.

TL;DR: A structured synopsis of the problems in image motion computation and analysis, and of the methods proposed, exposing the underlying models and supporting assumptions are offered.

TL;DR: This is one of the firstpublications to focus on wireless ad hoc and sensor networks with a concentration on algorithms and protocols providing you with a comprehensive resource to learn about the continuous advances in wireless and mobile communications.

TL;DR: A novel solution to this dilemma by considering the context of the tracking scene by integrating into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining.

TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.

TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.

TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.