GPU-Accelerated Otsu Algorithm Of Image Threshold Segmenting

In digital image processing applications, it is often necessary to extract and recognize the image target, such as face recognition, character recognition, fingerprint recognition, license plate recognition and content-based image retrieval, image segmentation is the crucial step in image recognition pre-processing stag. Image Threshold segmentation is the most common image segmentation approach. The traditional image threshold segmentation algorithm achieves image segmentation through adaptive selection of the optimal threshold by using gray-scale image features. Among them, the largest between-class variance (Otsu) has nothing to do with the object and background pixel distribution model, has better overall segmentation result, is widely used in astronomy, military, road transport, medical imaging, forensic and other fields.However, in several applications which have large volumes of image processing data and require real-time image processing operations such as the airport security checks, license plate recognition, military reconnaissance, medical diagnostics, computing speed of Otsu image segmentation algorithm has not yet reached real-time requirements. To address this issue, several algorithms have been proposed, such as using fuzzy sets and histogram similarity to shorten time of finding the Otsu threshold, using election algorithm to optimize global threshold of Otsu search image method. Although these improved Otsu image threshold segmentation algorithms achieved a certain acceleration effect, the acceleration effect is limited because of the serial method in CPU.In order to solve the problems mentioned above, this paper presents a GPU-based Otsu image threshold segmentation acceleration algorithm by studying the Otsu threshold image segmentation algorithm and the parallel framework for GPU programming,. The algorithm transforms serial processing method of the Otsu threshold image segmentation algorithm in the CPU into parallel processing texture rendering process in the GPU through using the GPU parallel frame structure and vector parallel computing capabilities to overcome that the Otsu threshold image segmentation algorithm is serially achieve very slowly in the CPU, that the amount of data operations is very large and seeking the compromised second-best threshold algorithm. This algorithm not only eliminates the latency of data transmission of the Otsu threshold image segmentation algorithm in the CPU and the GPU, but also reduces rendering processing steps in GPU compared with the Otsu threshold segmentation algorithm steps in the CPU, so that the Otsu image threshold segmentation algorithm is achieved in the GPU parallel and faster, and retaining the optimal threshold of Otsu algorithm. The innovation of this paper is transforming the serial algorithm in the CPU into parallel algorithm in the GPU, considering the Otsu image threshold segmentation algorithm can be parallelized to meet the space conditions, taking advantage of four channels in GPU texture RBGA to storage the parallel data respectively. And the one side of the rendering operation to complete all the data operations saves a lot of time.The experimental results show that: in general PC, the threshold segmentation of a image of 1600×1200 pixels can meet the real-time requirements of the airport security checks, license plate recognition, medical diagnostics and other areas. At the same time, Otsu image threshold segmentation algorithm in GPU has provided a viable approach for other image segmentation algorithms in GPU.