Rotary model-based fisheye image quasi dense corresponding point matching diffusion method

A technology of corresponding point matching and fisheye image, which is applied in the field of computer vision, can solve the problems of unstable affine model and unreliable diffusion results, and achieve the effect of stable and reliable diffusion process, flexible use and simple calculation

Inactive Publication Date: 2011-04-27
SHANXI UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the final diffusion result is unreliable due to the instability of the affine mo

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  • Rotary model-based fisheye image quasi dense corresponding point matching diffusion method
  • Rotary model-based fisheye image quasi dense corresponding point matching diffusion method
  • Rotary model-based fisheye image quasi dense corresponding point matching diffusion method

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Embodiment Construction

[0028] The present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the described embodiments are only intended to facilitate the understanding of the present invention, rather than limiting it in any way.

[0029] The present invention assumes that the camera first takes two fisheye images from different positions, and then realizes the matching diffusion of quasi-dense corresponding points through the following steps. The whole process can be found in figure 1 .

[0030] 1. Extract and match feature points of two images

[0031] In this step, many classic methods in the literature can be used to automatically realize feature extraction and matching, such as feature extraction methods based on affine invariants [4] Mikolajczyk K, Tuytelaars T, Schmid C, Zisserman A, Matas J, Schaffalitzky F , Kadir T and Van Gool L.A comparison of affine region detectors. International Journal of Computer Vision, 2005, 65(1-2)...

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Abstract

The invention discloses a rotary model-based quasi dense corresponding point matching diffusion method. The method mainly comprises the following steps of: for a pair of fisheye images shot in the same scene from different positions, extracting and matching characteristic points in the images, and then accurately positioning the characteristic points, wherein the characteristic points are used as initial seed points; and performing quasi dense corresponding point diffusion from the optimal seed points to the neighborhood, wherein the diffused corresponding points are used as new seed points for subsequent continuous diffusion. In the method, a rotary conversion model is adopted for parallax constraints of the corresponding points, and compared with the conventional affine transformation model, the model is simple in calculation and has only one free parameter, so the whole diffusion process is stable and reliable and can meet most application requirements. In addition, the method is a non-constraint diffusion method, does not need to demarcate the motion parameters of a camera in advance, and has high flexibility. Experimental results verify the feasibility of the method, and the method has strong practicability.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a corresponding point matching method between two or more images. Background technique [0002] Correspondence matching is a fundamental problem in computer vision and related fields. Most of the previous related studies focused on ordinary perspective images. However, due to the large field of view of fisheye images, they have important application value in reality, so it is of great significance to study the corresponding point matching problem of fisheye images. [0003] Quasi-dense matching is a kind of corresponding point matching method between sparse matching and dense matching. Reliability, the corresponding point matching of this kind of method is only performed in the area with rich texture, and the corresponding point matching is not performed on the homogeneous area. The basic idea of ​​this kind of method can be summarized as: firstly detect and match the...

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Application Information

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IPC IPC(8): G06T7/00G06T7/33
Inventor 李晓明李婧田亚平
Owner SHANXI UNIV
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