Abstract: A device or software may detect a number of times an item is accessed. A more direct path to the item may be automatically generated. The more direct path may be based at least in part on how often the item is accessed.

Abstract: According to an embodiment, an apparatus and method are disclosed for dynamic gesture recognition from stereo sequences. In an embodiment, a stereo sequence of images of a subject is obtained and a depth disparity map is generated from the stereo sequence. The system is initiated automatically based upon a statistical model of the upper body of the subject. The upper body of the subject is modeled as three planes, representing the torso and arms of the subject, and three Gaussian components, representing the head and hands of the subject. The system tracks the upper body of the subject using the statistical upper body model and extracts three-dimensional features of the gestures performed. The system recognizes the gestures using recognition units, which, under a particular embodiment, utilizes hidden Markov models for the three-dimensional gestures.

Abstract: According to some embodiments, a set of images may be determined, including at least one training image and at least one image to be provided to a viewer. A reduced training image may be created based on the training image, and at least one enlarging parameter may be calculated based on a portion of the training image and a corresponding portion of the reduced training image. The enlarging parameter may then be used to facilitate an enlargement of a reduced image to be provided to the viewer.

Abstract: A device or software may detect a number of times an item is accessed. A more direct path to the item may be automatically generated. The more direct path may be based at least in part on how often the item is accessed.

Abstract: According to an embodiment, an apparatus and method are disclosed for dynamic gesture recognition from stereo sequences. In an embodiment, a stereo sequence of images of a subject is obtained and a depth disparity map is generated from the stereo sequence. The system is initiated automatically based upon a statistical model of the upper body of the subject. The upper body of the subject is modeled as three planes, representing the torso and arms of the subject, and three Gaussian components, representing the head and hands of the subject. The system tracks the upper body of the subject using the statistical upper body model and extracts three-dimensional features of the gestures performed. The system recognizes the gestures using recognition units, which, under a particular embodiment, utilizes hidden Markov models for the three-dimensional gestures.