Dedicated hardware for parallel extraction of local binary pattern feature vectors

In this paper we propose a dedicated hardware for extraction of local binary pattern (LBP) feature vectors. The LBP method transforms local features of image data into binary micro-patterns that represent local and global features of the image. The LBP method can be used in applications such as texture classification, moving object detection and face detection and recognition. The hardware proposed in this paper has a massively parallel architecture in order to speed up the LBP feature extraction in real-time applications. The image data is pre-processed using an analog comparison method. Therefore, simulations are performed to find out how mismatch affects the performance.