Abstract: The method for measuring speed is performed by dividing the time sequence image data A into long and narrow frames (step 401). Next, the framed images and the time sequence image data B produced by the line scan camera B are correlated to obtain a moving time (step 402). Next, a moving time of each framed image and a corresponding moving distance are obtained to compute a moving speed (step 403). Next, scale of a specific shape pattern and the like is corrected using the moving speed obtained to match the scales in the two image sequences A and B to obtain a similarity degree (step 404). Next, a threshold value is applied to the similarity degree to recognize the specific shape pattern, thereby recognizing that the images are produced by a moving vehicle.

Abstract: The method for measuring speed is performed by dividing the time sequence image data A into long and narrow frames (step 401). Next, the framed images and the time sequence image data B produced by the line scan camera B are correlated to obtain a moving time (step 402). Next, a moving time of each framed image and a corresponding moving distance are obtained to compute a moving speed (step 403). Next, scale of a specific shape pattern and the like is corrected using the moving speed obtained to match the scales in the two image sequences A and B to obtain a similarity degree (step 404). Next, a threshold value is applied to the similarity degree to recognize the specific shape pattern, thereby recognizing that the images are produced by a moving vehicle.

Abstract: The method for measuring speed is performed by dividing the time sequence image data A into long and narrow frames (step 401). Next, the framed images and the time sequence image data B produced by the line scan camera B are correlated to obtain a moving time (step 402). Next, a moving time of each framed image and a corresponding moving distance are obtained to compute a moving speed (step 403). Next, scale of a specific shape pattern and the like is corrected using the moving speed obtained to match the scales in the two image sequences A and B to obtain a similarity degree (step 404). Next, a threshold value is applied to the similarity degree to recognize the specific shape pattern, thereby recognizing that the images are produced by a moving vehicle.

Abstract: A method for extracting a pre-selected pattern from an image by a programmed computer is able to identify and extract the pattern even under adverse conditions such as presence of noise or shadow in the image or the target pattern is partially shielded. The steps are: storing reference patterns for referencing; storing input images; applying filtering processes to reference patterns and input images; integrating filtered results of the reference pattern and filtered results of the input image to generate integrated results; and extracting a reference pattern according to the integrated results and specifying a location of the reference pattern in the input image. An image abnormality detection can also be performed using the basic method to identify a normal scene and detect a deviation from normalcy even under adverse conditions.