Detection and Tracking of Point Features

TL;DR: This work defines the tracking problem in general and devise a method for local, window-based, feature trackers to track accurately in spite of background distractions, and suggests that existing model-based trackers can be improved by incorporating similar techniques at the local level.

TL;DR: A combination of two paradigms: Wireless Sensor Networks (WSN) and Computer Vision applied for Motion Analysis and Computer vision provides high-level behavioural monitoring and analysis, whereas Wireless Sensors capture detailed parameters of a moving object.

TL;DR: This research is focused on developing a model to control a system using gestures, while simultaneously implementing continuous facial recognition to avoid unauthorized access, and utilizing a background subtraction model so as to extract the foreground and reduce the misclassifications.

TL;DR: This paper introduces image based visual servoing (IBVS) into a shared autonomy grasping system to improve its performance and details the challenges faced and what it has learned from them throughout the development of the system.

TL;DR: The Stanford AI Lab cart as discussed by the authors is a card-table sized mobile robot controlled remotely through a radio link, and equipped with a TV camera and transmitter equipped with an onboard TV system.

TL;DR: The analysis of visual motion plays a central role in biological systems as discussed by the authors, and sophisticated mechanisms for extracting and utilizing motion exist even in simple animals, such as frogs and houseflies, that respond selectively to small, dark objects moving in its visual field.

TL;DR: A polyhedral approximation for the volumetric description of a moving rigid object from a real-world scene is derived, based on measurements in monocular TV-frame sequences, and is compared to related ones reported in the recent literature.