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摘要边缘检测是图像分析及处理领域中的一个很有价值的探索方面，是进行图像提取及模糊识别的一个必要的手段，它是检测图像特性发生了变化的位置，而在成像过程中所出现的畸变、噪声、重合等因素往往会让边缘检测具有一定的难度，因此对边缘检测的探索有很好的理论与实际的意义。而FPGA因其很强的灵活性，优越的运算处理能力，很适合用于图像边缘检测。
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本文首先介绍了边缘检测的基本原理、常见的几种边缘检测算子，然后采用Matlab和modelsim对算法进行了联合仿真，验证了所设计算法的正确性，接着采用Verilog HDL编写了Sobel算法程序，完成了基于FPGA的Sobel边缘检测模块的设计，实现了对图像的实时边缘检测，取得了良好的效果。实验证明，利用FPGA在并行操作和流水线结构等方面的独特的优势，大幅提高了边缘检测的速度。32738
关键词 边缘检测 FPGA Sobel Matlab 图像处理
毕业设计说明书(毕业论文)外文摘要
Title Image Edge Detection Based on FPGA
Abstract
Edge detection is one of the most valuable exploration in image analysis and processing, and it is a necessary means for extracting the images and the fuzzy recognition , it is the position changes occurred in the detection of image features, in the imaging process of distortion, noise, coincidence factors tend to make edge detection has a certain degree of difficulty, therefore the exploration of edge detection has great theoretical and practical significance. Because of its strong flexibility, FPGA is suitable for image edge detection.
In this paper, we first introduce the basic principle of edge detection, several common edge detection operators, and then we use the algorithm of CO simulation with MATLAB and Modelsim, verifying the algorithm is correct and then Verilog HDL Sobel algorithm program is compiled, completed based on FPGA Sobel edge detection module design, to achieve the real-time edge detection of image, and achieved good results. Experiments show that using FPGA in parallel and pipelining structure and other aspects of the unique advantages, greatly improves the speed of the edge detection.
Keywords edge detection FPGA Sobel Matlab image processing
1 引言 1
1.1 课题的研究背景 1
1.2 国内外发展现状 1
1.3 论文主要工作 2
2 边缘检测算法原理 4
2.1 图像边缘检测基础 4
2.2 常用的图像边缘检测法 5
2.2.1 基于一阶微分的边缘检测法 5
2.2.2 基于二阶微分的边缘检测法 9
2.2.3 Sobel算子Matlab仿真 9
2.3 本章小结 10
3 FPGA系统硬件结构及开发流程 11
3.1 FPGA硬件结构 11
3.2 Cyclone IV芯片 12
3.3 WAT-250D2摄像头 14
3.4 VGA接口简介 15
3.5 FPGA开发流程 15
3.6 本章小结 17
4 基于FPGA边缘检测实现 18
4.1 系统设计框图 18
4.2 边缘检测模块 18
4.2.1 边缘检测阵列实现 19
4.2.2 卷积运算模块 21
4.2.3 边缘的输出 24
4.3 结果与验证 24
4.3.1 Matlab和Modelsim联合仿真 24
4.3.2 硬件实现 25