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Unsupervised vision methods based on image perceptual information

Abstract : This thesis work deals with extracting features and low-level primitives from perceptual image information to understand scenes. Motivated by the needs and problems in Unmanned Aerial Vehicles (UAVs) vision-based navigation, we propose novel methods focusing on image understanding problems. This work explores three main pieces of information in an image : intensity, color, and texture. In the first chapter of the manuscript, we work with the intensity information through image contours. We combine this information with human perception concepts, such as the Helmholtz principle and the Gestalt laws, to propose an unsupervised framework for object detection and identification. We validate this methodology in the last stage of the drone navigation, just before the landing. In the following chapters of the manuscript, we explore the color and texture information contained in the images. First, we present an analysis of color and texture as global distributions of an image. This approach leads us to study the Optimal Transport theory and its properties as a true metric for color and texture distributions comparison. We review and compare the most popular similarity measures between distributions to show the importance of a metric with the correct properties such as non-negativity and symmetry. We validate such concepts in two image retrieval systems based on the similarity of color distribution and texture energy distribution. Finally, we build an image representation that exploits the relationship between color and texture information. The image representation results from the image’s spectral decomposition, which we obtain by the convolution with a family of Gabor filters. We present in detail the improvements to the Gabor filter and the properties of the complex color spaces. We validate our methodology with a series of segmentation and boundary detection algorithms based on the computed perceptual feature space.
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Submitted on : Wednesday, June 8, 2022 - 9:50:11 AM
Last modification on : Friday, June 10, 2022 - 3:15:48 AM
Long-term archiving on: : Friday, September 9, 2022 - 6:52:06 PM


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  • HAL Id : tel-03690309, version 1


Eric Bazan. Unsupervised vision methods based on image perceptual information. Image Processing [eess.IV]. Université Paris sciences et lettres, 2021. English. ⟨NNT : 2021UPSLM063⟩. ⟨tel-03690309⟩



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