From announce at list.ipol.im Tue Mar 21 17:59:36 2023 From: announce at list.ipol.im (announcements about the IPOL journal) Date: Tue, 21 Mar 2023 17:59:36 +0100 Subject: [IPOL announce] new article: Robust Homography Estimation from Local Affine Maps Message-ID: A new article is available in IPOL: https://www.ipol.im/pub/art/2023/356/ Mariano Rodr?guez, Gabriele Facciolo, and Jean-Michel Morel, Robust Homography Estimation from Local Affine Maps, Image Processing On Line, 13 (2023), pp. 65?89. https://doi.org/10.5201/ipol.2023.356 Abstract The corresponding point coordinates determined by classic image matching approaches define local zero-order approximations of the global mapping between two images. But the patches around keypoints typically contain more information, which may be exploited to obtain a first-order approximation of the mapping, incorporating local affine maps between corresponding keypoints. Several methods have been proposed in the literature to compute this first-order approximation. In this paper we present several modifications of the RANSAC (RANdom SAmple Consensus) algorithm, that uses affine approximations and a-contrario procedures to improve the homography estimation between a pair of images. The a-contrario methodology provides a definition of the soundness of an estimation and allows for adaptive thresholds for inlier/outlier discrimination. These approaches outperform the state-of-the-art for different choices of image descriptors and image datasets, and permit to increase the probability of success in identifying image pairs in challenging matching databases. From announce at list.ipol.im Tue Mar 21 19:16:38 2023 From: announce at list.ipol.im (announcements about the IPOL journal) Date: Tue, 21 Mar 2023 19:16:38 +0100 Subject: [IPOL announce] new article: Electron Paramagnetic Resonance Image Reconstruction with Total Variation Regularization Message-ID: A new article is available in IPOL: https://www.ipol.im/pub/art/2023/414/ R?my Abergel, Mehdi Bouss?a, Sylvain Durand, and Yves-Michel Frapart, Electron Paramagnetic Resonance Image Reconstruction with Total Variation Regularization, Image Processing On Line, 13 (2023), pp. 90?139. https://doi.org/10.5201/ipol.2023.414 Abstract This work focuses on the reconstruction of two and three dimensional images of the concentration of paramagnetic species from electron paramagnetic resonance (EPR) measurements. A direct operator, modeling how the measurements are related to the paramagnetic sample to be imaged, is derived in the continuous framework taking into account the physical phenomena at work during the acquisition process. Then, this direct operator is discretized to closely take into account the discrete nature of the measurements and provide an explicit link between them and the discrete image to be reconstructed. A variational inverse problem with total variation regularization is formulated and an efficient resolvant scheme is implemented. The setting of the reconstruction parameters is thoroughly studied and facilitated thanks to the introduction of appropriate normalization factors. Moreover, an a contrario algorithm is proposed to derive the optimal resolution at which the data should be acquired. Finally, an in-depth experimental study over real EPR datasets is done to illustrate the potential and limitations of the presented image reconstruction model.