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Fish-Eye Image Feature Extraction and Matching Based on Affine Invariant Regions

Author: ZhaoXiaoNing
Tutor: LiXiaoMing
School: Shanxi University
Course: Pattern Recognition and Intelligent Systems
Keywords: Fisheye image Feature extraction Feature matching Omnidirectional vision Affine invariant
CLC: TP391.41
Type: Master's thesis
Year: 2009
Downloads: 92
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Abstract


Fish - eye camera in robot navigation , scene monitoring with a wide range of applications , virtual reality and 3D reconstruction . However, due to the characteristics of the automatic extraction and matching problem has never been satisfactorily resolved , these applications are still stuck in manual or human-computer interaction stage . Image feature extraction and matching is a fundamental problem in computer vision , is a very difficult problem . Compared with the traditional perspective image , due to the the fisheye image has a large non-linear distortion , makes feature extraction and matching more complex , the study fisheye image feature extraction and matching problem has important theoretical significance and practical value . This paper focuses on the characteristics of the fish-eye image extraction and matching , the completed work are: 1, the classical affine invariant feature detection and matching algorithms are reviewed , and the performance of these methods were analyzed and to compare. 2, fisheye image , given an affine invariant stability in the region contour point extraction and matching method . First extracts MSER (Maximal Stable Extremal Region) affine invariant regional characteristics and initial matching descriptor SIFT ( ScaleInvariant , Feature transform ) ; Then , the region contour smoothing , and then extract the region contour curvature extreme points as stable feature points ; matching of these feature points based on geometric consistency constraints and gray similarity measure . 3, fish - eye image , an affine invariant region of the internal feature point detection and matching method . First calculate the the Hessian area between the affine transformation model, and then , in the application of this model on the basis of this prediction and similarity measure of regional internal matching feature points . This article within the region feature points using a Harris corner extraction algorithm, the matching method based on the cross-validation method . Experimental results show that the extraction and matching method compared with the traditional affine invariant characteristics of the region , the method given in this paper can get more feature points , and stable performance , high positioning accuracy . Matching strategy , this paper also has a high reliability of these methods for nonlinear distortion of a fish -eye image , is a better solution .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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