Description: 该gui函数基本上包括图像处理里面的最基本处理，相当于一个小型photoshop。比如读取文件，几何变换中的垂直镜像，平移，旋转，缩放；正交变换的DFT，FFT，DCT，DST，DHT，DWashT；灰度处理中的反色，直方图均衡，全局线性变换，分段线性变换，指数非线性变换，对数非线性变换；图像增强里面的加噪声，平滑，锐化，伪彩色增强；图像分割里面的灰度阈值法，Robert，Laplace，sobel，prewitt，canny算子边缘检测法；图像恢复里面的直接逆滤波，维纳滤波；图像编码里面的霍夫曼编码，行程编码-Basically, the gui functions including the most basic image processing inside the handle, which is equivalent to a small photoshop. For example, read the document, the geometric transformation of the vertical mirroring, shifting, rotating, zooming orthogonal transformation of the DFT, FFT, DCT, DST, DHT, DWashT anti-gray color processing, histogram equalization, global linear transformation , piecewise linear transformation, index of non-linear transform of the number of non-linear transformation image enhancement which increases the noise, smoothing, sharpening, pseudo-color enhancement the gray-scale image segmentation inside the threshold, Robert, Laplace, sobel, prewitt, canny edge detection operator method image restoration inside direct inverse filtering, Wiener filtering image coding inside the Huffman code, and so on Run-Length Coding Platform: |
Size: 398336 |
Author:guhuxiang |
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Description: 摘要
车辆牌照识别(License Plate Recognition System, LPR)作为目标自动识别的一
种重要形式，可用于电子收费、出入控制、车流监控等众多场合，从而提高交通
管理自动化的程度，它的相关技术的研究正逐渐受到人们的重视。
本文主要介绍基于Run Length原理和Tamura纹理的车牌定位系统，该系统是基
于视频流进行开发的，主要包括车辆运动区域检测、车牌图像处理、车牌定位。
其中车辆运动区域检测利用多帧求平均的背景估计方法实现；车牌图像处理包括
灰度变换、中值滤波、边缘检测和二值化；车牌定位包括车牌水平定位、车牌垂
直定位、颜色判断和细定位。
该定位系统是在C++Builder环境下实现的，针对不同大小车牌、不同光照条件
的1400张图片进行测试，其定位率可以达到92 以上；同时也将该算法封装成DLL
库，应用在雷达测试仪中，对各种光照条件下的视频流进行测试。测试结果证明
本文算法简单、快速、准确率高，满足车牌识别系统实时性、通用性、和准确性
的要求，具有一定的应用价值。
关键词：车牌定位Run Length Tamura纹理-Abstract
As an important application form of target automatic recognition, vehicle license
plate recognition( LPR) technology is used in some fields, such as electron charging,
pass controlling and automobile stream supervising etc.
The paper introduces license plate location system based on the principle of Run
Length and Tamura texture feature. The system is developed based on video streams,
mainly includes vehicle motion region detection, license plate image processing, vehicle
license plate location. The detection of vehicle motion region uses the background
estimation methods based on the average of multi-frame license plate image processing
includes gray-scale transformation, median filtering, edge detection and binarization
the license plate location includes license plate horizontal location, license plate vertical
position, color judgments and accurate location.
The system is achieved in the C++ Builder environment, and it uses 1400 images
that are of different Platform: |
Size: 2509824 |
Author:王述 |
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Search - vertical run length