1. Confidence-based dynamic optimization model for biomedical image mosaicking
- Author
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Pascal Bourdon, Romuald Perrot, David Helbert, Université de Poitiers - Faculté de Sciences fondamentales et appliquées, Université de Poitiers, Synthèse et analyse d'images (XLIM-ASALI), XLIM (XLIM), and Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS)-Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS)
- Subjects
Image mosaicking ,Computer science ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image registration ,Image processing ,Iterative reconstruction ,01 natural sciences ,010309 optics ,Optics ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,0103 physical sciences ,Image Processing, Computer-Assisted ,Computer vision ,Global optimization ,Image resolution ,business.industry ,Template matching ,Atomic and Molecular Physics, and Optics ,Molecular Imaging ,Electronic, Optical and Magnetic Materials ,biomedical image processing ,image registration ,Feature (computer vision) ,Pairwise comparison ,Computer Vision and Pattern Recognition ,Artificial intelligence ,biomedical imaging ,business - Abstract
International audience; Biomedical image mosaicking is a trending topic. It consists in computing a single large image from multiple observations, and becomes a challenging task when said observations barely overlap or are subject to illumination changes, poor resolution, blur and either highly textured or predominantly homogeneous content. Because such challenges are common in biomedical images, classical keypoint/feature-based methods perform poorly. In this paper, we propose a new framework based on pairwise template matching coupled with a constrained, confidence-driven global optimization strategy to solve the issue of microscopic biomedical image mo-saicking. First we provide experimental results that show significant improvement on a subjective level. Then we describe a new validation strategy for objective assessment, which shows improvement as well. Address
- Published
- 2019
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