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Robust image description with laplacian profile and radial Fourier transform

Abstract : In this thesis we explore a new image description method composed of a multi-scale vector of Laplacians of Gaussians, the Laplacian Profile, and a Radial Fourier Transform. This method captures shape information with different proportions around a point in the image. A Gaussian pyramid of scaled images is used for the extraction of the descriptor vectors. The aim of this new method is to provide image description that can be suitable for diverse applications. Adjustability as well as low computational and memory needs are as important as robustness and discrimination power. We created a method with the ability to capture the image signal efficiently with descriptor vectors of particularly small length compared to the state of the art. Experiments show that despite its small vector length, the new descriptor shows reasonable robustness and discrimination power that are competitive to the state of the art performance.We test our proposed image description method on three different visual tasks. The first task is keypoint matching for images that have undergone image transformations like rotation, scaling, blurring, JPEG compression, changes in viewpoint and changes in light. We show that against other methods from the state of the art, the proposed descriptor performs equivalently with a very small vector length. The second task is on pattern detection. We use the proposed descriptor to create two different Adaboost based detectors for people detection in images. Compared to a similar detector using Histograms of Oriented Gradients (HOG), the detectors with the proposed method show competitive performance using significantly smaller descriptor vectors. The last task is on reflection symmetry detection in real world images. We introduce a technique that exploits the proposed descriptor for detecting possible symmetry axes for the two reflecting parts of a mirror symmetric pattern. This technique introduces constraints and ideas of how to collect more efficiently the information that is important to identify reflection symmetry in images. With this task we show that the proposed descriptor can be generalized for rather complicated applications. The set of the experiments confirms the qualities of the proposed method of being easily adjustable and requires relatively low computational and storage requirements while remaining robust and discriminative.
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Evanthia Mavridou. Robust image description with laplacian profile and radial Fourier transform. Computer Vision and Pattern Recognition [cs.CV]. Université de Grenoble, 2014. English. ⟨NNT : 2014GRENM065⟩. ⟨tel-01555416⟩



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