Abstract: A method for the detection of motion in video data is disclosed. In an embodiment, a method uses a background technique for the motion detection. In another embodiment, a Gaussian Mixture Model is applied to a neighborhood or block of pixels within an image frame, thereby identifying background and foreground in the image based on said blocks.

Abstract: A system and method predicts pitting corrosion growth using imaging technology. In an embodiment, a first module preprocesses images to locate seed points, and a second module models a corrosion life cycle of the seed points. In another embodiment, the seed points are identified with defined confidence levels, and the rate of change of features in corrosion images is analyzed to identify dominant life-cycle phases of corrosion and defining corrosion impact factor for corrosion quantification.

Abstract: An image processing system and method uses color information and texture information to compare a first image and a second image. The color information and texture information are derived independently, and then combined to obtain robust comparison and image retrieval results. For the color features of the images, color component differences are calculated and compared. For the texture features of the images, a multi-dimensional K-means clustering of a gradient vectors are calculated and compared.

Abstract: A system and a method to detect a label are described herein.

Abstract: A method to classify objects labels objects as human, vehicle, multiple human, or other based on output from a motion detection algorithm. Features that are extracted from the blob, such as size, shape, and area, form a basis of the classification. The extracted features are subjected to various mathematical analyses that distinguish the classes that are available for labeling an object.

Abstract: A system detects motion in video data. In an embodiment, a difference frame is created by comparing the pixels from a first frame and a second frame. The difference frame is divided up into blocks of pixels, and the system calculates standard deviations on a block basis. A threshold value is calculated based on the standard deviation, and the presence or absence of motion is determined based on that threshold value.

Abstract: An example method of recognizing characters in video data includes (i) Obtaining a binary image from a scene in video data; (ii) segmenting characters in the binary image (e.g., by using region labeling); and (iii) using a character recognition model to recognize the segmented characters. The method may be incorporated into an existing video system or newly developed video systems to perform character recognition tasks on a variety of different objects. In some embodiments, the character recognition module uses a learning-based neural network to recognize characters. In other embodiments, the character recognition module uses a non-learning-based progressive shape analysis process for character recognition.

Abstract: In some embodiments, the present invention is related to a method that includes determining difference frames between successive frames in video data and determining an energy value of each difference frame. The method further includes determining a cumulative energy mean for each difference frame and a predetermined number of previous difference frames and updating the energy value of each frame by removing the cumulative energy mean from the energy value of each difference frame. In addition, the method further includes identifying a temporal change in the energy value of each difference frame to extract key frames from video data. Identifying a temporal change in the energy value of each difference frame to extract key frames from the video data may include identifying difference frames that have an energy value greater than zero after the cumulative energy mean has been removed from the energy value of each difference frame.

Abstract: A moving object is detected in a video data stream by extracting color information to estimate regions of motion in two or more sequential video frames, extracting edge information to estimate object shape of the moving object in two or more sequential video frames; and combining the color information and edge information to estimate motion of the object.

Abstract: A system and a method to extract dominant color information using fusion of color space data may include Red, Green, Blue (RGB) color space data and may include (H), Saturation (S), and Intensity Value (V) color space data. Red, Green and Blue color space histograms may be computed and maximum count values of each color space may be obtained, which may help finding ‘shine’ information in the object. Normalized HSV color space may be fused with Normalized RGB color space for dominant color determination. The dominant color decision conditions involved in the fusion process may change based on a ‘shine’ flag.

Abstract: A rate of change of visual content (compared to an adjacent video frame) of a video frame is determined, and the video frame is selected as a key video frame if the rate exceeds a threshold value. In an embodiment, to compute the rate, the motion energy vector magnitude (square of displacement magnitude) of each moved pixel of the current frame is determined, and an average displacement magnitude is determined. The average displacement magnitude may also be used to determine the rate.