Abstract: A system, method and article of manufacture are provided for tracking a head portion of a person image in video images. Upon receiving video images, a first head tracking operation is executed for generating a first confidence value. Such first confidence value is representative of a confidence that a head portion of a person image in the video images is correctly located. Also executed is a second head tracking operation for generating a second confidence value representative of a confidence that the head portion of the person image in the video images is correctly located. The first confidence value and the second confidence value are then outputted. Subsequently, the depiction of the head portion of the person image in the video images is based on the first confidence value and the second confidence value.

Abstract: A system, method and article of manufacture are provided for tracking a head portion of a person image in video images. Upon receiving video images, a first head tracking operation is executed for generating a first confidence value. Such first confidence value is representative of a confidence that a head portion of a person image in the video images is correctly located. Also executed is a second head tracking operation for generating a second confidence value representative of a confidence that the head portion of the person image in the video images is correctly located. The first confidence value and the second confidence value are then outputted. Subsequently, the depiction of the head portion of the person image in the video images is based on the first confidence value and the second confidence value.

Abstract: A system, method and article of manufacture are provided for executing a setup protocol. A camera and a visual display device are coupled to a computer. Images are generated by the camera upon activation of the camera. A series of setup tests are then conducted on the images generated by the camera to determine whether the camera and surrounding environmental elements satisfy predetermined criteria of an intended computer vision application for optimal running of the intended computer vision application on the computer. The series of setup tests comprises at least one setup test selected from a library of setup tests.

Abstract: A background subtraction apparatus of the present invention includes a key point locator for locating key points on a known object type, a boundary point locator for locating boundary points of the known object that make up the edges of the known object, and an edge processor for processing the edges to provide a clean-edged extraction of the known object from a background image. Preferably, the key point locator includes an alignment detector for detecting alignment of an image of the known object type with a skeleton image. Still more preferably, the skeleton image is an exoskeleton image and the known object type is a human being.

Abstract: The invention identifies a blank segment (i.e., a segment that does not correspond to recorded visual content) in a set of visual recording data. The invention identifies a blank segment using a blank frame detector that is adapted to evaluate a frame of visual recording data to determine whether the frame of visual recording data is a blank frame, and a blank segment detector that is adapted to evaluate a characteristic of a group of frames of visual recording data to determine whether the group of frames of visual recording data is a blank segment comprising a plurality of blank frames. Identification of a blank segment can be used to, for example, decide whether to terminate digitization of a set of analog visual recording data.

Abstract: A background subtraction apparatus of the present invention includes a key point locator for locating key points on a known object type, a boundary point locator for locating boundary points of the known object that make up the edges of the known object, and an edge processor for processing the edges to provide a clean-edged extraction of the known object from a background image. Preferably, the key point locator includes an alignment detector for detecting alignment of an image of the known object type with a skeleton image. Still more preferably, the skeleton image is an exoskeleton image and the known object type is a human being.