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Author |
Carles Sanchez; Debora Gil; R. Tazi; Jorge Bernal; Y. Ruiz; L. Planas; F. Javier Sanchez; Antoni Rosell |
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Title |
Quasi-real time digital assessment of Central Airway Obstruction |
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Conference Article |
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2015 |
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3rd European congress for bronchology and interventional pulmonology ECBIP2015 |
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Barcelona; Spain; April 2015 |
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ECBIP |
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IAM; MV; 600.075 |
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SGT2015 |
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2612 |
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Author |
Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
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Title |
Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy |
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2015 |
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6th International Conference on Information Processing in Computer-Assisted Interventions IPCAI2015 |
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10 |
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6 |
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935-945 |
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PURPOSE:
Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service.
METHODS:
Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion.
RESULTS:
Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room.
CONCLUSIONS:
Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done. |
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Barcelona; Spain; June 2015 |
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IPCAI |
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IAM; MV; 600.075 |
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Admin @ si @ SBS2015b |
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2613 |
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Author |
Sergio Vera; Miguel Angel Gonzalez Ballester; Debora Gil |
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Title |
A Novel Cochlear Reference Frame Based On The Laplace Equation |
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2015 |
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29th international Congress and Exhibition on Computer Assisted Radiology and Surgery |
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10 |
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1 |
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1-312 |
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Barcelona; Spain; June 2015 |
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CARS |
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IAM; 600.075 |
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Admin @ si @ VGG2015 |
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2615 |
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Hanne Kause; Patricia Marquez; Andrea Fuster; Aura Hernandez-Sabate; Luc Florack; Debora Gil; Hans van Assen |
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Title |
Quality Assessment of Optical Flow in Tagging MRI |
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2015 |
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5th Dutch Bio-Medical Engineering Conference BME2015 |
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The Netherlands; January 2015 |
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BME |
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IAM; ADAS; 600.076; 600.075 |
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Admin @ si @ KMF2015 |
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2616 |
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Olivier Lefebvre; Pau Riba; Charles Fournier; Alicia Fornes; Josep Llados; Rejean Plamondon; Jules Gagnon-Marchand |
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Title |
Monitoring neuromotricity on-line: a cloud computing approach |
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Conference Article |
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2015 |
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17th Conference of the International Graphonomics Society IGS2015 |
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The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain. |
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Pointe-à-Pitre; Guadeloupe; June 2015 |
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IGS |
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DAG; 600.077 |
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Admin @ si @ LRF2015 |
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2617 |
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Author |
Youssef El Rhabi; Simon Loic; Brun Luc |
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Title |
Estimation de la pose d’une caméra à partir d’un flux vidéo en s’approchant du temps réel |
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Conference Article |
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2015 |
Publication |
15ème édition d'ORASIS, journées francophones des jeunes chercheurs en vision par ordinateur ORASIS2015 |
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Augmented Reality; SFM; SLAM; real time pose computation; 2D/3D registration |
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Finding a way to estimate quickly and robustly the pose of an image is essential in augmented reality. Here we will discuss the approach we chose in order to get closer to real time by using SIFT points [4]. We propose a method based on filtering both SIFT points and images on which to focus on. Hence we will focus on relevant data. |
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Amiens; France; June 2015 |
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ORASIS |
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DAG; 600.077 |
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Admin @ si @ RLL2015 |
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2626 |
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Author |
Bogdan Raducanu; Alireza Bosaghzadeh; Fadi Dornaika |
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Title |
Multi-observation Face Recognition in Videos based on Label Propagation |
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Conference Article |
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2015 |
Publication |
6th Workshop on Analysis and Modeling of Faces and Gestures AMFG2015 |
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10-17 |
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In order to deal with the huge amount of content generated by social media, especially for indexing and retrieval purposes, the focus shifted from single object recognition to multi-observation object recognition. Of particular interest is the problem of face recognition (used as primary cue for persons’ identity assessment), since it is highly required by popular social media search engines like Facebook and Youtube. Recently, several approaches for graph-based label propagation were proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot cope properly with the rapid and frequent changes in data appearance, a phenomenon intrinsically related with video sequences. In this paper, we
propose a novel approach for efficient and adaptive graph construction, based on a two-phase scheme: (i) the first phase is used to adaptively find the neighbors of a sample and also to find the adequate weights for the minimization function of the second phase; (ii) in the second phase, the
selected neighbors along with their corresponding weights are used to locally and collaboratively estimate the sparse affinity matrix weights. Experimental results performed on Honda Video Database (HVDB) and a subset of video
sequences extracted from the popular TV-series ’Friends’ show a distinct advantage of the proposed method over the existing standard graph construction methods. |
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Boston; USA; June 2015 |
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CVPRW |
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LAMP; 600.068; 600.072; |
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Admin @ si @ RBD2015 |
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2627 |
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Author |
Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca |
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A coarse-to-fine approach for fast deformable object detection |
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Journal Article |
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2015 |
Publication |
Pattern Recognition |
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PR |
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48 |
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5 |
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1844-1853 |
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We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part
placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy. |
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ISE; 600.078; 602.005; 605.001; 302.012 |
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Admin @ si @ PVG2015 |
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2628 |
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Author |
M. Cruz; Cristhian A. Aguilera-Carrasco; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa |
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Cross-spectral image registration and fusion: an evaluation study |
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2015 |
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2nd International Conference on Machine Vision and Machine Learning |
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multispectral imaging; image registration; data fusion; infrared and visible spectra |
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This paper presents a preliminary study on the registration and fusion of cross-spectral imaging. The objective is to evaluate the validity of widely used computer vision approaches when they are applied at different
spectral bands. In particular, we are interested in merging images from the infrared (both long wave infrared: LWIR and near infrared: NIR) and visible spectrum (VS). Experimental results with different data sets are presented. |
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Barcelona; July 2015 |
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MVML |
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ADAS; 600.076 |
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Admin @ si @ CAV2015 |
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2629 |
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Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo |
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LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations |
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2015 |
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22th IEEE International Conference on Image Processing |
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178 - 181 |
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Quebec; Canada; September 2015 |
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ICIP |
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ADAS; 600.076 |
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Admin @ si @ AST2015 |
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2630 |
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Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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Brightness and colour induction through contextual influences in V1 |
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2015 |
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Scottish Vision Group 2015 SGV2015 |
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12 |
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9 |
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1208-2012 |
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Carnoustie; Scotland; March 2015 |
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NEUROBIT; |
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Admin @ si @ OPC2015a |
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2632 |
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Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris |
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Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code |
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2015 |
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European Conference on Visual Perception ECVP2015 |
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Liverpool; uk; August 2015 |
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NEUROBIT; |
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Admin @ si @ POW2015 |
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2633 |
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Xavier Otazu; Olivier Penacchio; Xim Cerda-Company |
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An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort |
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2015 |
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Barcelona Computational, Cognitive and Systems Neuroscience |
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Barcelona; June 2015 |
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BARCCSYN |
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NEUROBIT; |
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Admin @ si @ OPC2015b |
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2634 |
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Michal Drozdzal; Santiago Segui; Petia Radeva; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria |
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Motility bar: a new tool for motility analysis of endoluminal videos |
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2015 |
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Computers in Biology and Medicine |
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CBM |
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65 |
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320-330 |
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Small intestine; Motility; WCE; Computer vision; Image classification |
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Wireless Capsule Endoscopy (WCE) provides a new perspective of the small intestine, since it enables, for the first time, visualization of the entire organ. However, the long visual video analysis time, due to the large number of data in a single WCE study, was an important factor impeding the widespread use of the capsule as a tool for intestinal abnormalities detection. Therefore, the introduction of WCE triggered a new field for the application of computational methods, and in particular, of computer vision. In this paper, we follow the computational approach and come up with a new perspective on the small intestine motility problem. Our approach consists of three steps: first, we review a tool for the visualization of the motility information contained in WCE video; second, we propose algorithms for the characterization of two motility building-blocks: contraction detector and lumen size estimation; finally, we introduce an approach to detect segments of stable motility behavior. Our claims are supported by an evaluation performed with 10 WCE videos, suggesting that our methods ably capture the intestinal motility information. |
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MILAB;MV |
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Admin @ si @ DSR2015 |
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2635 |
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Author |
Santiago Segui; Oriol Pujol; Jordi Vitria |
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Title |
Learning to count with deep object features |
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2015 |
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Deep Vision: Deep Learning in Computer Vision, CVPR 2015 Workshop |
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90-96 |
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Abstract |
Learning to count is a learning strategy that has been recently proposed in the literature for dealing with problems where estimating the number of object instances in a scene is the final objective. In this framework, the task of learning to detect and localize individual object instances is seen as a harder task that can be evaded by casting the problem as that of computing a regression value from hand-crafted image features. In this paper we explore the features that are learned when training a counting convolutional neural
network in order to understand their underlying representation.
To this end we define a counting problem for MNIST data and show that the internal representation of the network is able to classify digits in spite of the fact that no direct supervision was provided for them during training.
We also present preliminary results about a deep network that is able to count the number of pedestrians in a scene. |
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Boston; USA; June 2015 |
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CVPRW |
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Notes |
MILAB; HuPBA; OR;MV |
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no |
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Call Number |
Admin @ si @ SPV2015 |
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2636 |
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