基于Simulink与Stateflow的红外图像边缘检测系统设计

基于模型设计方法实现基于细胞神经网络的红外图像边缘检测系统。将simulink、stateflow设计方法与CNN的设计理论相结合，建立CNN IP核模块。接着采用串行结构搭建图像输入、输出模块，达到可视化实时仿真及代码自动生成。仿真结果表明：基于模型的设计方法取得了较好的效果。在Xilinx公司Virtex-6系列的FPGA平台上，综合后占用极少资源的情况下达到了142.693MHz最高频率和7.927Mpixels/sec的处理速度。

Infrared image edge detection system design based on Simulink and Stateflow

Cellular neural network (CNN) has achieved good results in the infrared image edge detection, but its application prospects are restricted by CNN hardware system. Considering the cycle is long and high-risk of CNN hardware development, the infrared image edge detection system based on cellular neural networks is achieved by model-based design method. Combining simulink, stateflow with theory of CNN, we should establish the IP core of CNN. Then the input and output module is designed by the serial structure, the system is achieved the real-time simulation. The verilog-code of the system is automatically generated. The experimental results demonstrated: the model-based design method obtain good results. Implemented target a Virtex-6 FPGA from Xilinx, system reach the 142.693MHz of highest frequency and 7.927Mpixels/sec of processing speed.