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Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
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Journal Article |
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Year |
2006 |
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Pattern Recognition Letters |
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PRL |
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Volume |
27 |
Issue |
8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Abstract |
Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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no |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Author |
Svebor Karaman; Giuseppe Lisanti; Andrew Bagdanov; Alberto del Bimbo |
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Title |
Leveraging local neighborhood topology for large scale person re-identification |
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Journal Article |
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Year |
2014 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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47 |
Issue |
12 |
Pages |
3767–3778 |
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Re-identification; Conditional random field; Semi-supervised; ETHZ; CAVIAR; 3DPeS; CMV100 |
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In this paper we describe a semi-supervised approach to person re-identification that combines discriminative models of person identity with a Conditional Random Field (CRF) to exploit the local manifold approximation induced by the nearest neighbor graph in feature space. The linear discriminative models learned on few gallery images provides coarse separation of probe images into identities, while a graph topology defined by distances between all person images in feature space leverages local support for label propagation in the CRF. We evaluate our approach using multiple scenarios on several publicly available datasets, where the number of identities varies from 28 to 191 and the number of images ranges between 1003 and 36 171. We demonstrate that the discriminative model and the CRF are complementary and that the combination of both leads to significant improvement over state-of-the-art approaches. We further demonstrate how the performance of our approach improves with increasing test data and also with increasing amounts of additional unlabeled data. |
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LAMP; 601.240; 600.079 |
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Admin @ si @ KLB2014a |
Serial |
2522 |
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Author |
V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich |
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Title |
Adaptive Correlation Filters for Pattern Recognition |
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Journal |
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2006 |
Publication |
Pattern Recognition and Image Analysis |
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16 |
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3 |
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425-431 |
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Pattern recognition, Correlation filters, A adaptive filters |
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Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. |
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ISE |
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ISE @ ise @ KMA2006a |
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673 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
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Title |
Automatic Verification of Properly Signed Multi-page Document Images |
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Conference Article |
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2015 |
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Proceedings of the Eleventh International Symposium on Visual Computing |
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9475 |
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327-336 |
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Document Image; Manual Inspection; Signature Verification; Rejection Criterion; Document Flow |
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In this paper we present an industrial application for the automatic screening of incoming multi-page documents in a banking workflow aimed at determining whether these documents are properly signed or not. The proposed method is divided in three main steps. First individual pages are classified in order to identify the pages that should contain a signature. In a second step, we segment within those key pages the location where the signatures should appear. The last step checks whether the signatures are present or not. Our method is tested in a real large-scale environment and we report the results when checking two different types of real multi-page contracts, having in total more than 14,500 pages. |
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Las Vegas, Nevada, USA; December 2015 |
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LNCS |
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9475 |
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ISVC |
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DAG; 600.077 |
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Admin @ si @ |
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3189 |
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Author |
Rain Eric Haamer; Eka Rusadze; Iiris Lusi; Tauseef Ahmed; Sergio Escalera; Gholamreza Anbarjafari |
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Title |
Review on Emotion Recognition Databases |
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Book Chapter |
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2018 |
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Human-Robot Interaction: Theory and Application |
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emotion; computer vision; databases |
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Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format. |
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978-1-78923-316-2 |
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HUPBA; 602.133 |
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Admin @ si @ HRL2018 |
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3212 |
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Author |
Cristhian Aguilera; M.Ramos; Angel Sappa |
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Title |
Simulated Annealing: A Novel Application of Image Processing in the Wood Area |
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Book Chapter |
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2012 |
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Simulated Annealing – Advances, Applications and Hybridizations |
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91-104 |
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Marcos de Sales Guerra Tsuzuki |
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978-953-51-0710-1 |
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ADAS |
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no |
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Admin @ si @ ARS2012 |
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2156 |
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Author |
Arturo Fuentes; F. Javier Sanchez; Thomas Voncina; Jorge Bernal |
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Title |
LAMV: Learning to Predict Where Spectators Look in Live Music Performances |
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Conference Article |
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2021 |
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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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5 |
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500-507 |
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The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends. |
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Virtual; February 2021 |
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VISIGRAPP |
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MV; ISE; 600.119; |
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Admin @ si @ FSV2021 |
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3570 |
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Author |
Eduardo Aguilar; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva |
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Title |
Uncertainty Modeling and Deep Learning Applied to Food Image Analysis |
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Conference Article |
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2020 |
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13th International Joint Conference on Biomedical Engineering Systems and Technologies |
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Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition has received a little effort in the Computer Vision community. In this project, we review the field of food image analysis and focus on how to combine with two challenging research lines: deep learning and uncertainty modeling. After discussing our methodology to advance in this direction, we comment potential research, social and economic impact of the research on food image analysis. |
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Villetta; Malta; February 2020 |
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BIODEVICES |
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MILAB |
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Admin @ si @ ANK2020 |
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3526 |
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Author |
Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca |
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Title |
Transfer Learning from Synthetic Data in the Camera Pose Estimation Problem |
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Conference Article |
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2020 |
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15th International Conference on Computer Vision Theory and Applications |
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This paper presents a novel Siamese network architecture, as a variant of Resnet-50, to estimate the relative camera pose on multi-view environments. In order to improve the performance of the proposed model a transfer learning strategy, based on synthetic images obtained from a virtual-world, is considered. The transfer learning consists of first training the network using pairs of images from the virtual-world scenario
considering different conditions (i.e., weather, illumination, objects, buildings, etc.); then, the learned weight
of the network are transferred to the real case, where images from real-world scenarios are considered. Experimental results and comparisons with the state of the art show both, improvements on the relative pose estimation accuracy using the proposed model, as well as further improvements when the transfer learning strategy (synthetic-world data transfer learning real-world data) is considered to tackle the limitation on the
training due to the reduced number of pairs of real-images on most of the public data sets. |
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Valletta; Malta; February 2020 |
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MSIAU; 600.130; 601.349; 600.122 |
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Admin @ si @ CSV2020 |
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3433 |
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Author |
Mohamed Ilyes Lakhal; Hakan Cevikalp; Sergio Escalera |
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CRN: End-to-end Convolutional Recurrent Network Structure Applied to Vehicle Classification |
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Conference Article |
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2018 |
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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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5 |
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137-144 |
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Vehicle Classification; Deep Learning; End-to-end Learning |
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Vehicle type classification is considered to be a central part of Intelligent Traffic Systems. In the recent years, deep learning methods have emerged in as being the state-of-the-art in many computer vision tasks. In this paper, we present a novel yet simple deep learning framework for the vehicle type classification problem. We propose an end-to-end trainable system, that combines convolution neural network for feature extraction and recurrent neural network as a classifier. The recurrent network structure is used to handle various types of feature inputs, and at the same time allows to produce a single or a set of class predictions. In order to assess the effectiveness of our solution, we have conducted a set of experiments in two public datasets, obtaining state of the art results. In addition, we also report results on the newly released MIO-TCD dataset. |
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Funchal; Madeira; Portugal; January 2018 |
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HUPBA |
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Admin @ si @ LCE2018a |
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3094 |
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