Binocular vision is an important branch of computer vision that processes images captured from two different cameras with different perspectives to obtain depth information of a scene. However, the stereo matching algorithm is complex and traditional platforms often struggle to meet real-time and accuracy requirements simultaneously. In this article, we implement a binocular vision system based on the idea of software-hardware co-design on the Zynq platform. According to the characteristics of the Zynq platform, the system's software and hardware functions are divided. The processing system (PS) is mainly responsible for flow control and binocular correction, while the programmable logic (PL) is responsible for parallel acceleration of the stereo matching algorithm. In the image acquisition part, we configure the format and output image timing using a designed camera acquisition module. In the stereo matching part, we design a semi-global stereo matching algorithm using Verilog language. Finally, we build a binocular vision system on the Zynq platform for testing. The experimental results show that the frame rate can reach 30fps when the input image resolution is 640×480, which satisfies the requirements of real-time and accuracy of the output disparity map.
With the rapid development of computers, there are more and more network security problems. While the network brings convenience to people's life, it also brings many security risks. Many users have suffered losses due to vulnerabilities in the network. In order to maintain the security of cyberspace, it is urgent to build a network security ecosystem. Web fingerprint identification technology is a network security technology, which is widely used in vulnerability detection. However, traditional Web fingerprint identification technology. This paper will optimize the matching between Web fingerprint identification and fingerprint database according to the improved PSO particle swarm optimization algorithm to improve efficiency and accuracy.
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