图像分割中阴影去除算法的研究

国内图书分类号：TN911.73

国际图书分类号：621.3

工学硕士学位论文

图像分割中阴影去除算法的研究

硕士研究生：王宏

导师：关宇东 副教授

申请学位级别：工学硕士

学科、专业：信息与通信工程

所在单位：电子与信息技术研究院

答辩日期：2008年7月

授予学位单位：哈尔滨工业大学

Classified Index: TN911.73

U.D.C.: 621.3

Dissertation for the Master Degree in Engineering

RESEARCH ON REMOV AL ALGORITHM OF SHADOWS IN IMAGE

SEGMENTATION

Candidate：Wang Hong

Supervisor：Associate Prof. Guan Yudong Academic Degree Applied for：Master of Engineering

Specialty：Information and Communication

Engineering

Affiliation：School of Electronics and

Information Technology

Date of Defence：July, 2008

Degree-Conferring-Institution：Harbin Institute of Technology

哈尔滨工业大学工学硕士学位论文

摘要

随着计算机技术的飞速发展数字图像处理技术也得到了快速发展，人们越来越追求更高的图像效果。然而在成像过程中受到许多不可避免的因素影响，图像出现了降质现象。图像阴影就是图像一个典型的降质现象，它的存在直接影响了图像匹配的精度、模式识别的准确度以及目标提取的自动化程度，使智能视频监控系统中后续的目标跟踪、识别等工作的出错率大大增加。因此，阴影消除对于提升智能视频监控系统的工作性能很有必要。

本文针对大多数分割算法输出目标携带阴影的问题，分析了阴影形成的原理和光学属性，构造阴影形成的光照模型。从阴影相关理论出发利用数字图像处理的相关理论和信息论的有关知识，设计了三种有效的去除阴影的算法。三种算法各有其特点，各有其适合处理的图像，结合起来实现自适应去除阴影的算法。

论文在绪论中阐述了课题的背景、意义、来源以及国内外现状，明确了研究的主要目的。第一章中研究了阴影的相关理论，其是阴影去除的重要依据，根据其做了如下工作。

首先根据图像处理的直方图原理和聚类技术，提出了基于直方图和聚类技术去除阴影的算法，此方法实现了多个目标的阴影去除。利用直方图的相关知识能够确定目标的个数，目标的大概宽度、边界，阴影的方向，阴影的大致区域；结合灰度图像的聚类分析得到精确的阴影区域，实现了单个或者多个目标阴影的去除。

其次利用阴影颜色和纹理不变的光学属性，建立了基于色度畸变和局部交叉熵去除阴影的算法。在RGB彩色空间运用颜色矢量计算阴影与对应背景颜色变化的角度，即色度畸变角，通过阈值确定阴影区域。利用信息论中的交叉熵进一步区分目标与阴影。色度畸变和交叉熵的结合，能够有效地去除分割目标的阴影。

再次根据阴影区域灰度连续、平坦的特点，设计了基于多梯度分析和线扫描去除阴影的算法。梯度体现了灰度的变化，灰度变化越剧烈梯度越大，线扫描可以检测出一个像素宽度的线。梯度可以检测出灰度连续的阴影区域，线扫面可以去除连续区域面积小的非阴影区域，两者结合进而实现了去除阴影的目的。

哈尔滨工业大学工学硕士学位论文

最后根据三种算法的原理和应用范围，设计了判定程序实现了图像自适应的选择去影算法，减少人为判断，提高了算法的鲁棒性。引入了一种量化评估方法，对各算法进行客观评定。

关键词聚类技术；梯度分析；色度畸变；交叉熵；自适应算法。

哈尔滨工业大学工学硕士学位论文

Abstract

With the rapid development of computer, the technology of digital image processing is developing fast. And people have a growing pursuit of higher image processing effect. However, the effect of digital images is degraded more or less by many inescapability factors during the imaging process. Image shadows is typical phenomenon of the drop of image quality. It will directly affect the precision of image matching, the accuracy of pattern recognition and the automation of objects extraction, which will cause more errors in tracking and recognition of object at post-treatment of intelligent video monitoring. Therefore, it is very important to wipe off shadows to improve work quality of intelligent video monitoring.

Against the problem that many methods of image segmentation often bring shadow, this thesis analyzed the principle and optical properties of shadow whose illumination model is also made. Based on the theories of shadow, three effectively algorithms to remove shadow were designed by theories of digital image processing and information. Because the algorithms have different features and one algorithm is adapted to a kind of image, automation algorithm of removement shadows was designed by the algorithms.

This thesis explains background, significance, source and present situations of shadow removing in domestic and foreign to clear the purpose of this research. And Chapter One studied the theory of shadow which is an important basis for the removal of the shadow, according to it, do the jobs as follows.

Firstly, based on histogram and clustering techniques, a shadow eliminating method which depended on those techniques was presented. It can remove multi-object shadows. The quantity, probably width, borderline of objects, direction and approximately area of shadows could be confirmed by theory of histogram. And this method combined with clustering techniques of gray image, this method could obtain exact area of shadows in order to eliminate shadow of one-object or multi-objects.

Secondly, by the use of the optical characteristic of shadow (such as color,