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Research on Technique of Image Mosaic of Multiple Model in Large View Scene

Author: FengGuoZuo
Tutor: LiuYu
School: National University of Defense Science and Technology
Course: Information and Communication Engineering
Keywords: Image Stitching Large Field Projection Model Multi-model integration Point of the same name
CLC: TP391.41
Type: Master's thesis
Year: 2007
Downloads: 168
Quote: 1
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Abstract


This paper studies the large field of view based on the integration of multi- model image stitching technology . The large field of image projection mosaic must first choose a reasonable model . The commonly used model is a polynomial model and projection model . Polynomial model can be linear fit solution calculation is relatively simple , but it requires a lot of uniform distribution of points of the same name, in order to obtain the desired results . The biggest advantages of the projection model is in line with the camera imaging mechanism , and requires only four precisely the same name point on will be able to solve the model coefficients . Therefore, the selection of the projection model , deduced integration method for solving multi- projection model . Since the projection nonlinear model is computationally more difficult , this paper focuses on the problem . Projection model is only four points of the same name , but its high accuracy requirements . In this paper, the better performance of SIFT algorithm comprehensive analysis to determine compliance with solving the the projection model coefficients need key algorithm parameters , such as scale space miningæž“the frequency , etc. . The large field of multiple image projection mosaic commonly used local alignment splicing technology , its fatal flaw is the large accumulation of error . To solve this problem , this paper proposes a large field of multi-model projection model based integration stitching method . The main idea of this method is that multiple images each projector model coefficients Unified Solution minimum goal of solving the overall calibration error , using the camera outside the parameter trend model coefficients between iterative correction coefficient solution . The experiments show that the method of stitching the accumulation of error is small , effective male .

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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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