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ViRVIG
Gen3dlife Conferences Results
Andújar, Carlos; Argudo, Oscar; Besora, Isaac; Brunet, Pere; Chica, Antoni; Comino, Marc
Spanish Computer Graphics Conference (CEIG), 2018.
DOI: http://dx.doi.org/10.2312/ceig.20181162
Structure-from-motion along with multi-view stereo techniques jointly allow for the inexpensive scanning of 3D objects (e.g. buildings) using just a collection of images taken from commodity cameras. Despite major advances in these fields, a major limitation of dense reconstruction algorithms is that correct depth/normal values are not recovered on specular surfaces (e.g. windows) and parts lacking image features (e.g. flat, textureless parts of the facade). Since these reflective properties are inherent to the surface being acquired, images from different viewpoints hardly contribute to solve this problem. In this paper we present a simple method for detecting, classifying and filling non-valid data regions in depth maps produced by dense stereo algorithms. Triangles meshes reconstructed from our repaired depth maps exhibit much higher quality than those produced by state-of-the-art reconstruction algorithms like Screened Poisson-based techniques.
Orellana, Bernat; Monclús, Eva; Brunet, Pere; Navazo, Isabel; Bendezú, Álvaro; Azpiroz, Fernando
MICCAI - International Conference on Medical Image Computing and Computer Assisted Intervention, pp 638--647, 2018.
About 50% of the patients consulting a gastroenterology clinic report symptoms without detectable cause. Clinical researchers are interested in analyzing the volumetric evolution of colon segments under the effect of different diets and diseases. These studies require noninvasive abdominal MRI scans without using any contrast agent. In this work, we propose a colon segmentation framework designed to support T2-weighted abdominal MRI scans obtained from an unprepared colon. The segmentation process is based on an efficient and accurate quasiautomatic approach that drastically reduces the specialist interaction and effort with respect other state-of-the-art solutions, while decreasing the overall segmentation cost. The algorithm relies on a novel probabilistic tubularity filter, the detection of the colon medial line, probabilistic information extracted from a training set and a final unsupervised clustering. Experimental results presented show the benefits of our approach for clinical use.
3D4life PhD Thesis Results