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Francisco Blanco; Felipe Lumbreras; Joan Serrat; Roswitha Siener; Silvia Serranti; Giuseppe Bonifazi; Montserrat Lopez Mesas; Manuel Valiente |
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Title |
Taking advantage of Hyperspectral Imaging classification of urinary stones against conventional IR Spectroscopy |
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Journal Article |
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2014 |
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Journal of Biomedical Optics |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
JBiO |
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19 |
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12 |
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126004-1 - 126004-9 |
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The analysis of urinary stones is mandatory for the best management of the disease after the stone passage in order to prevent further stone episodes. Thus the use of an appropriate methodology for an individualized stone analysis becomes a key factor for giving the patient the most suitable treatment. A recently developed hyperspectral imaging methodology, based on pixel-to-pixel analysis of near-infrared spectral images, is compared to the reference technique in stone analysis, infrared (IR) spectroscopy. The developed classification model yields >90% correct classification rate when compared to IR and is able to precisely locate stone components within the structure of the stone with a 15 µm resolution. Due to the little sample pretreatment, low analysis time, good performance of the model, and the automation of the measurements, they become analyst independent; this methodology can be considered to become a routine analysis for clinical laboratories. |
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ADAS; 600.076 |
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Admin @ si @ BLS2014 |
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2563 |
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David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville |
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Title |
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images |
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Journal Article |
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2017 |
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Journal of Healthcare Engineering |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
JHCE |
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2040-2295 |
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Colonoscopy images; Deep Learning; Semantic Segmentation |
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Colorectal cancer (CRC) is the third cause of cancer death world-wide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss- rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aim- ing to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image segmentation, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. The proposed dataset consists of 4 relevant classes to inspect the endolumninal scene, tar- geting different clinical needs. Together with the dataset and taking advantage of advances in semantic segmentation literature, we provide new baselines by training standard fully convolutional networks (FCN). We perform a compar- ative study to show that FCN significantly outperform, without any further post-processing, prior results in endoluminal scene segmentation, especially with respect to polyp segmentation and localization. |
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ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 |
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VBS2017b |
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2940 |
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Arnau Ramisa; Alex Goldhoorn; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras |
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Title |
Combining Invariant Features and the ALV Homing Method for Autonomous Robot Navigation Based on Panoramas |
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Journal Article |
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2011 |
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Journal of Intelligent and Robotic Systems |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
JIRC |
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64 |
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3-4 |
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625-649 |
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Biologically inspired homing methods, such as the Average Landmark Vector, are an interesting solution for local navigation due to its simplicity. However, usually they require a modification of the environment by placing artificial landmarks in order to work reliably. In this paper we combine the Average Landmark Vector with invariant feature points automatically detected in panoramic images to overcome this limitation. The proposed approach has been evaluated first in simulation and, as promising results are found, also in two data sets of panoramas from real world environments. |
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Springer Netherlands |
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0921-0296 |
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RV;ADAS |
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Admin @ si @ RGA2011 |
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1728 |
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Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras |
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Title |
Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot |
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Journal Article |
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2012 |
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Journal of Intelligent and Robotic Systems |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
JIRC |
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68 |
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2 |
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185-208 |
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This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings. |
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Springer Netherlands |
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0921-0296 |
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ADAS |
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Admin @ si @ RAV2012 |
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2150 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
An iterative multiresolution scheme for SFM with missing data |
Type |
Journal Article |
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Year |
2009 |
Publication |
Journal of Mathematical Imaging and Vision |
Abbreviated Journal ![sorted by Abbreviated Journal field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
JMIV |
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34 |
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3 |
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240–258 |
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Several techniques have been proposed for tackling the Structure from Motion problem through factorization in the case of missing data. However, when the percentage of unknown data is high, most of them may not perform as well as expected. Focussing on this problem, an iterative multiresolution scheme, which aims at recovering missing entries in the originally given input matrix, is proposed. Information recovered following a coarse-to-fine strategy is used for filling in the missing entries. The objective is to recover, as much as possible, missing data in the given matrix.
Thus, when a factorization technique is applied to the partially or totally filled in matrix, instead of to the originally given input one, better results will be obtained. An evaluation study about the robustness to missing and noisy data is reported.
Experimental results obtained with synthetic and real video sequences are presented to show the viability of the proposed approach. |
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ADAS @ adas @ JSL2009a |
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1163 |
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