Abstract: A method of and an apparatus for automatically estimating the tilting angle in tilted images enables level correction of the images. A preferred orientation of objects in an image and the deviation of the current orientation from the preferred orientation is determined by tilt image analysis without object recognition. Tilt image analysis includes several steps such as gradient feature computation, line segment tracking, line segment estimation and orientation deviation estimation. Once the tilt angle is determined, the image can be corrected so that an object or scene is not tilted or is tilted by only the appropriate amount.

Abstract: A gesture recognition system using a skin-color based method combined with motion information to achieve real-time segmentation. A Kalman filter is used to track the centroid of the hand. The palm center, palm bottom, as well as the largest distance from the palm center to the contour from extracted hand mask are computed. The computed distance to a threshold is then compared to decide if the current posture is “open” or “closed.” In a preferred embodiment, the transition between the “open” and “closed” posture to decide if the current gesture is in “select” or “grab” state.

Abstract: A gesture recognition system and method that inputs videos of a moving hand and outputs the recognized gesture states for the input sequence. In each image, the hand area is segmented from the background and used to estimate parameters of all five fingers. The system further classifies the hand image as one of the postures in the pre-defined database and applies a geometric classification algorithm to recognize the gesture. The system combines a skin color model with motion information to achieve real-time hand segmentation performance, and considers each dynamic gesture as a multi-dimensional volume and uses a geometric algorithm to classify each volume.

Abstract: A method for detecting presence of a hand gesture in video frames includes receiving video frames having an original resolution, downscaling the received video frames into video frames having a lower resolution, and detecting a motion corresponding to the predefined hand gesture in the downscaled video frames based on temporal motion information in the downscaled video frames. The method also includes detecting a hand shape corresponding to the predefined hand gesture in a candidate search window within one of the downscaled video frames using a binary classifier. The candidate search window corresponds to a motion region containing the detected motion. The method further includes determining whether the received video frames contain the predefined hand gesture based on the hand shape detection.

Abstract: A method for detecting presence of a hand gesture in video frames includes receiving video frames having an original resolution, downscaling the received video frames into video frames having a lower resolution, and detecting a motion corresponding to the predefined hand gesture in the downscaled video frames based on temporal motion information in the downscaled video frames. The method also includes detecting a hand shape corresponding to the predefined hand gesture in a candidate search window within one of the downscaled video frames using a binary classifier. The candidate search window corresponds to a motion region containing the detected motion. The method further includes determining whether the received video frames contain the predefined hand gesture based on the hand shape detection.

Abstract: A computer-readable medium contains software that, when read by a computer, causes the computer to perform a method for wide-area site-based surveillance.

Abstract: A computer-readable medium contains software that, when read by a computer, causes the computer to perform a method for wide-area site-based surveillance.