CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleImage Feature Extraction and Matching
Thesis Advisor胡占义 ; 孙凤梅
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
Keyword区域特征 点特征 特征提取和匹配 车轮轮廓 钢圈轮廓 交比 Region Feature Point Feature Feature Extraction And Matching Wheel Contour Wheel Rim Contour Cross Ratio
Abstract图像特征提取与匹配是计算机视觉中的一个关键问题,在目标检测、物体识别、三维重建、图像配准、图像理解等具体应用中发挥着重要作用。由于图像的成像条件和所记录的内容复杂多样,而且具体应用需求各有不同,对图像特征提取和图像匹配的研究一直都是视觉领域中一个极具挑战性的问题。正是这一问题的重要性和挑战性吸引着各国学者在这一领域进行研究。 本文从实际应用出发围绕图像特征提取与匹配问题作了一些研究,主要工作有: ² 本文针对鱼眼镜头下场景的三维重建系统的特殊需求,探讨了一种结合MSER区域特征、SIFT描述子和几何约束的图像边缘点匹配方法。在SIFT特征匹配过程中,由于DoG算子对图像边缘的响应比较敏感,所以将对应于边缘结构的不稳定的关键点去掉了,而在三维重建、物体识别、图像配准等应用中图像边缘处的特征点起着重要的作用。本文对这一问题作了初步的探索。具体匹配方法是首先提取图像 的MSER特征区域,并计算每个特征区域的SIFT描述子结合极线约束匹配特征区域。对匹配好的MSER特征区域对的边缘点,用极几何约束限制有可能匹配的待选边缘点,再用灰度相关确定对应的边缘点。最后在对应的特征区域内用单应矩阵(Homography)约束剔除错误的对应边缘点。从而得到对应的图像边缘点。为了验证方法的有效性,我们应用这种方法对鱼眼镜头拍摄的图像进行匹配并重建了场景的三维信息,取得了较好的重建效果。 ² 本文还研究了交通事故现场图片的特征提取问题,并提出了一种鲁棒的自动检测车轮轮廓的方法。由于提取钢圈轮廓比提取车轮橡胶轮胎的轮廓容易,而且钢圈轮廓和车轮轮廓可以近似看作同一平面上的两个同心圆,所以在本文方法中,通过检测车轮钢圈的轮廓和车轮与地面的接触点,利用射影变换保持交比不变的性质间接地计算出车轮轮廓,克服了光照、路况、车型对提取车轮轮廓的影响,大量实验证明了方法的可行性。
Other AbstractImage feature extraction and matching is a key problem in many applications of computer vision, such as target detection, object recognition, 3D reconstruction, image registration and video understanding. Due to a large family of camera lens types, various image content, and diversity of applications,image feature extraction and matching has always been a challenging problem,and a lot of researchers are actively engaged in the field. In this dissertation, the main work is focused on applications of feature extraction and matching in some real problems. The main points are as follow: ² An approach for matching edge points between two fish-eye images is proposed. In Lowe's original scheme, those points along edges are eliminated during the feature extraction process since the difference-of-Gaussian function's response is unstable to even small amounts of noise, whereas correspondence along edges play a very important role in most applications as 3D reconstruction, object detection, image registration, etc. We attempt to tackle this problem by: Firstly, MSER regions are extracted and matched using SIFT descriptor and epipolar line constraint. Within correspondent regions, intersection of epipolar line and region edges constrain the possible point correspondences, and finally, homagraphy is invoked to reject possible mismatches. Our preliminary results on fish-eye images show that the obtained edge points are viable for 3D reconstruction. ² A new automatic wheel contour detecting approach is proposed. As the contour of wheel rim is easier to detect than the contour of wheel, and the contour of wheel rim and the contour of wheel are mostly concentric circles on the same plane, we first detect the contour of wheel rim, the center of wheel rim and the contacting point between the wheel and the ground, then the contour of wheel is detected via the invariance of cross ratio under projective transformation. Our extensive experimental results with real images under various weather conditions show that the proposed approach is capable of detecting contours of wheels robustly and automatically.
Other Identifier200528014628027
Document Type学位论文
Recommended Citation
GB/T 7714
谭博怡. 图像特征提取与匹配[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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