Showing papers by "Matthew Turk published in 2000"

System and method for visually tracking occluded objects in real time

Abstract: The present invention is embodied in a system and method for digitally tracking objects in real time. The present invention visually tracks three-dimensional (3-D) objects in dense disparity maps in real time. Tracking of the human body is achieved by digitally segmenting and modeling different body parts using statistical models defined by multiple size parameters, position and orientation. In addition, the present invention is embodied in a system and method for recognizing mutual occlusions of body parts and filling in data for the occluded parts while tracking a human body. The body parts are preferably tracked from frame to frame in image sequences as an articulated structure in which the body parts are connected at the joints instead of as individual objects moving and changing shape and orientation freely.

Gesture modeling and recognition using finite state machines

Abstract: We propose a state-based approach to gesture learning and recognition. Using spatial clustering and temporal alignment, each gesture is defined to be an ordered sequence of states in spatial-temporal space. The 2D image positions of the centers of the head and both hands of the user are used as features; these are located by a color-based tracking method. From training data of a given gesture, we first learn the spatial information and then group the data into segments that are automatically aligned temporally. The temporal information is further integrated to build a finite state machine (FSM) recognizer. Each gesture has a FSM corresponding to it. The computational efficiency of the FSM recognizers allows us to achieve real-time on-line performance. We apply this technique to build an experimental system that plays a game of "Simon Says" with the user.

Perceptual User Interfaces

Abstract: For some time, graphical user interfaces (GUIs) have been the dominant platform for human—computer interaction. The GUI-based style of interaction has made computers simpler and easier to use, especially for office productivity applications where computers are used as tools to accomplish specific tasks. However, as the way we use computers changes and computing becomes more pervasive and ubiquitous, GUIs will not easily support the range of interactions necessary to meet users’ needs. In order to accommodate a wider range of scenarios, tasks, users and preferences, we need to move toward interfaces that are natural, intuitive, adaptive and unobtrusive. The aim of a new focus in MCI, called Perceptual User Interfaces (PUIs), is to make human—computer interaction more like how people interact with each other and with the world. This chapter describes the emerging PUI field and then reports on three PUI-motivated projects: computer vision-based techniques to visually perceive relevant information about the user.

Constructing finite state machines for fast gesture recognition

Abstract: Proposes an approach to 2D gesture recognition that models each gesture as a finite state machine (FSM) in the spatial-temporal space. The model construction works in a semi-automatic way. The structure of the model is first manually decided based on the observation of the spatial topology of the data. The model is refined iteratively between two stages: data segmentation and model training. We incorporate a modified Knuth-Morris-Pratt algorithm recognition procedure to speed up recognition. The computational efficiency of the FSM recognizers allows real-time online performance to be achieved.