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Estimation of motion from observed objects in image sequences

Abstract : This thesis describes approaches estimating motion from image sequences with data assimilation methods. A particular attention is given to include representations of the displayed objects in the estimation process. Variational and sequential implementations are discussed in the document.The variational methods rely on an evolution equation, a background equation and an observation equation, which characterize the studied system and the observations. The motion estimation is obtained as the minimum of a cost function. In a first approach, the structures are modeled by their boundaries. The image model describes both the evolution of the gray level function and the displacement of the structures. The resulting motion field should allow the position of the structures in the model to match their observed position. The use of structures betters the result. A second approach, less expensive regarding the computational costs, is designed, where the structures are modeled by the values of the background error covariance matrix.The sequential approach, described in the thesis, relies on the creation of an ensemble of state vectors and on the use of localization methods. In order to model the structures, a new localization criteria based on the gray level values is defined. However, the localization method, if directly applied on the background error covariance matrix, renders the approach inoperable on large images. Therefore, another localization method is designed, which consists to decompose the image domain into independent subdomains before the estimation. Here, the structures representation intervenes while decomposing the global domain.
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Submitted on : Monday, June 13, 2016 - 1:49:12 PM
Last modification on : Wednesday, June 8, 2022 - 12:50:03 PM


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  • HAL Id : tel-01241514, version 2


Yann Lepoittevin. Estimation of motion from observed objects in image sequences. Biological Physics []. Université Pierre et Marie Curie - Paris VI, 2015. English. ⟨NNT : 2015PA066567⟩. ⟨tel-01241514v2⟩



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