Абстрактный

Pattern Recognition Using Combination of Shifted Filter Response

Mr. Dipak L. Patil, Dr. Prakash J. Kulkarni

Pattern recognition is important for many computer vision applications such as handwritten character recognition, traffic sign detection, and also it is widely used in the medical field such as detection of retinal vascular bifurcations. In this paper, we are going to propose a new approach for pattern recognition called as a Combination of Shifted Filter Response. It is configured for detecting same and similar pattern. It is configured with the help of prototype pattern provided by the user. It uses a bank of Gabor filters for the edge detection purpose. The output of the Gabor filter is then blurred and shifted with the help of some parameters. All blurred and shifted Gabor responses are then combined with the help of weighted geometric mean. The weighted geometric mean is acts like an AND gate i.e. this will produce output when all the sub parts of a pattern of interest (provided by user) are present. The final result will be the COSFIRE output. It will detect the traffic signs present in the complex scenes. For this, public dataset of traffic sign is used which consist of 48 images. It will detect the traffic sign present in the image. This will helpful to assist the driver during driving or useful for automated vehicle (without driver).