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MB-LBP特征在视觉目标检测和分类中的应用
Alternative TitleObject Detection and Classification with Multi-block LBP Feature Representation
张伦
Subtype工学硕士
Thesis Advisor李子青
2008-06-04
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
KeywordMb-lbp特征 人脸检测 视频流中物体分类 Adaboost学习算法 Mb-lbp Feature Face Detection Object Classification In Video Adaboost Algorithm
Abstract计算机视觉的一个核心任务就是识别图像中所包含的一系列有实际意义的物体(如人、飞机等),它是对该幅图像做出进一步解释和理解的基本前提。如何提取有效的特征来对物体进行表征和描述是视觉目标识别非常关键的一个问题。本论文主要研究在静态图像目标(特别是人脸)检测和视频中的运动目标分类问题里,提取兼具很强辨别能力和运算简单特点的视觉目标特征及其对应的目标分类和检测算法。论文的主要工作如下: 1. 基于 AdaBoost的目标检测框架大都基于简单的Haar特征。但是,原始的Haar特征由于描述能力较弱导致无论模型训练还是应用测试两个阶段均具有很高的运算复杂度。同时,在模型训练的后期,基于Haar特征的弱分类器分类能力太弱以致于不能提高整个检测器的性能。本文应用MB-LBP特征代替Haar特征训练人脸/非人脸分类器。MB-LBP特征(Multi-block Local Binary Pattern)是对原始LBP特征的一种扩展。改进了原始LBP特征只能描述小范围的图像信息以及易受噪声影响的缺点。在使用AdaBoost进行特征选择和分类器构建时,针对MB-LBP特征值非度量的情况,本文设计了多叉树型的弱分类器来解决这一问题。 2. 本文提出了利用MB-LBP特征和ECOC规则设计的多类别目标分类算法,并把它应用到视频中的运动目标分类问题里。本文应用基于MB-LBP、ECOC的分类器对视频中的前景图像进行分类识别,该方法可以把运动目标分类成行人、自行车、轿车、面包车等六个类别。同以前大多数方法相比,本文介绍的方法可以在各种具有不同摄像机视角和背景的视频场景中使用同一个分类器对运动目标进行分类。同时,本文的方法可以识别小轿车、卡车、骑自行车的人这样的更细致的类别。 3. 在实际应用中只能从有限数目的视频场景中采集前景图像数据,提取MB-LBP特征训练分类器。在一个新的视频场景中,由于受摄像机视角、光照变化的影响,该场景中的前景目标可能同训练集里的数据存在不小的外观上的差异。这导致了所训练的基于MB-LBP特征的分类器在该场景中的分类性能有限。本文使用该分类器以及利用基于物体形状、运动信息等特征实现在各种摄像机视角下的鲁棒的运动目标分类。
Other AbstractA central theme in computer vision is to describe an image in terms of the meaningful objects that comprise it (such as persons, airplanes, etc). It is the basic for computers to automatically achieve the visual perception of the real world. Extracting suitable features for image data representation is one key step of object recognition. This thesis mainly studies object detection (especially face) in static image and object classification in video, especially studies extract vision features of objects with good ability of discriminative and low compute complexity. The contributions of this work are as follows: (1) In the domain of face detection, Haar-features, which are widely used, seems too simple, this results in high computer complexity both in training procedure and detection procedure. In this thesis, we use MB-LBP feature to replace Haar-feature. In our experiments, MB-LBP features show more distinctive performance. MB-LBP is extended from the original LBP feature. Compared with the original LBP, MB-LBP can capture image structures with different scales and aspect ratios. Aiming at dealing with the non-metric feature value of MB-LBP features, multi-branch regression tree is developed to construct the weak classifiers when applying AdaBoost algorithm. (2) This thesis describes an appearance-based method based on MB-LBP features to classify objects in video. This method achieved real-time and robust objects classification performance in diverse camera viewing angles. Besides classifying objects to human or vehicles, we also studies to classify the objects into car, van, truck, person, bike and group of people. The ECOC-based method is introduced to solve this multi-class classification problem. (3) This thesis studies use some shape-based and motion-based features to improve the moving object classification performance of MB-LBP feature based classifier.
shelfnumXWLW1270
Other Identifier200528014628045
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/7466
Collection毕业生_硕士学位论文
Recommended Citation
GB/T 7714
张伦. MB-LBP特征在视觉目标检测和分类中的应用[D]. 中国科学院自动化研究所. 中国科学院研究生院,2008.
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