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Author David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
Title Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes Type Conference Article
Year 2013 Publication CVPR Workshop on Ground Truth – What is a good dataset? Abbreviated Journal
Volume Issue Pages 706 - 711
Keywords Pedestrian Detection; Domain Adaptation
Abstract Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs.
Address Portland; Oregon; June 2013
Corporate Author Thesis
Publisher IEEE Place of Publication Editor (up)
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes ADAS; 600.054; 600.057; 601.217 Approved no
Call Number ADAS @ adas @ VXR2013a Serial 2219
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Author Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa
Title Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers Type Conference Article
Year 2013 Publication CVPR Workshop on Ground Truth – What is a good dataset? Abbreviated Journal
Volume Issue Pages 688 - 693
Keywords Pedestrian Detection; Domain Adaptation
Abstract Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%.
Address Portland; oregon; June 2013
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPRW
Notes ADAS; 600.054; 600.057; 601.217 Approved yes
Call Number XVR2013; ADAS @ adas @ xvr2013a Serial 2220
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Author Ivet Rafegas
Title Exploring Low-Level Vision Models. Case Study: Saliency Prediction Type Report
Year 2013 Publication CVC Technical Report Abbreviated Journal
Volume 175 Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis Master's thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ Raf2013 Serial 2409
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Author Francesco Brughi
Title Artistic Heritage Motive Retrieval: an Explorative Study Type Report
Year 2013 Publication CVC Technical Report Abbreviated Journal
Volume 176 Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis Master's thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM Approved no
Call Number Admin @ si @ Bru2013 Serial 2410
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Author Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca
Title Large scale continuous visual event recognition using max-margin Hough transformation framework Type Journal Article
Year 2013 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 117 Issue 10 Pages 1356–1368
Keywords
Abstract In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1077-3142 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ CGR2013 Serial 2413
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Author Mirko Arnold; Anarta Ghosh; Glen Doherty; Hugh Mulcahy; Stephen Patchett; Gerard Lacey
Title Towards Automatic Direct Observation of Procedure and Skill (DOPS) in Colonoscopy Type Conference Article
Year 2013 Publication Proceedings of the International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 48-53
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area 800 Expedition Conference VISIGRAPP
Notes MV Approved no
Call Number fernando @ fernando @ Serial 2427
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Author Christophe Rigaud; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier
Title Speech balloon contour classification in comics Type Conference Article
Year 2013 Publication 10th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Comic books digitization combined with subsequent comic book understanding create a variety of new applications, including mobile reading and data mining. Document understanding in this domain is challenging as comics are semi-structured documents, combining semantically important graphical and textual parts. In this work we detail a novel approach for classifying speech balloon in scanned comics book pages based on their contour time series.
Address Bethlehem; PA; USA; August 2013
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GREC
Notes DAG; 600.056 Approved no
Call Number Admin @ si @ RKB2013 Serial 2429
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Author J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin
Title Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm Type Journal Article
Year 2013 Publication Expert Systems with Applications Abbreviated Journal EXWA
Volume 40 Issue 17 Pages 6707-6712
Keywords Neural gas; Expert vision; Eye-tracking; Fixations
Abstract Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0957-4174 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ CRM2013 Serial 2438
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Author Katerine Diaz; Francesc J. Ferri; W. Diaz
Title Fast Approximated Discriminative Common Vectors using rank-one SVD updates Type Conference Article
Year 2013 Publication 20th International Conference On Neural Information Processing Abbreviated Journal
Volume 8228 Issue III Pages 368-375
Keywords
Abstract An efficient incremental approach to the discriminative common vector (DCV) method for dimensionality reduction and classification is presented. The proposal consists of a rank-one update along with an adaptive restriction on the rank of the null space which leads to an approximate but convenient solution. The algorithm can be implemented very efficiently in terms of matrix operations and space complexity, which enables its use in large-scale dynamic application domains. Deep comparative experimentation using publicly available high dimensional image datasets has been carried out in order to properly assess the proposed algorithm against several recent incremental formulations.
K. Diaz-Chito, F.J. Ferri, W. Diaz
Address Daegu; Korea; November 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-42050-4 Medium
Area Expedition Conference ICONIP
Notes ADAS Approved no
Call Number Admin @ si @ DFD2013 Serial 2439
Permanent link to this record
 
 
Author Katerine Diaz; Francesc J. Ferri
Title Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes Type Book Whole
Year 2013 Publication Extensiones del método de vectores comunes discriminantes Aplicadas a la clasificación de imágenes Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Los métodos basados en subespacios son una herramienta muy utilizada en aplicaciones de visión por computador. Aquí se presentan y validan algunos algoritmos que hemos propuesto en este campo de investigación. El primer algoritmo está relacionado con una extensión del método de vectores comunes discriminantes con kernel, que reinterpreta el espacio nulo de la matriz de dispersión intra-clase del conjunto de entrenamiento para obtener las características discriminantes. Dentro de los métodos basados en subespacios existen diferentes tipos de entrenamiento. Uno de los más populares, pero no por ello uno de los más eficientes, es el aprendizaje por lotes. En este tipo de aprendizaje, todas las muestras del conjunto de entrenamiento tienen que estar disponibles desde el inicio. De este modo, cuando nuevas muestras se ponen a disposición del algoritmo, el sistema tiene que ser reentrenado de nuevo desde cero. Una alternativa a este tipo de entrenamiento es el aprendizaje incremental. Aquí­ se proponen diferentes algoritmos incrementales del método de vectores comunes discriminantes.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-639-55339-0 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ DiF2013 Serial 2440
Permanent link to this record
 
 
Author Marcelo D. Pistarelli; Angel Sappa; Ricardo Toledo
Title Multispectral Stereo Image Correspondence Type Conference Article
Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal
Volume 8048 Issue Pages 217-224
Keywords
Abstract This paper presents a novel multispectral stereo image correspondence approach. It is evaluated using a stereo rig constructed with a visible spectrum camera and a long wave infrared spectrum camera. The novelty of the proposed approach lies on the usage of Hough space as a correspondence search domain. In this way it avoids searching for correspondence in the original multispectral image domains, where information is low correlated, and a common domain is used. The proposed approach is intended to be used in outdoor urban scenarios, where images contain large amount of edges. These edges are used as distinctive characteristics for the matching in the Hough space. Experimental results are provided showing the validity of the proposed approach.
Address York; uk; August 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-40245-6 Medium
Area Expedition Conference CAIP
Notes ADAS; 600.055 Approved no
Call Number Admin @ si @ PST2013 Serial 2561
Permanent link to this record
 
 
Author Gioacchino Vino; Angel Sappa
Title Revisiting Harris Corner Detector Algorithm: a Gradual Thresholding Approach Type Conference Article
Year 2013 Publication 10th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7950 Issue Pages 354-363
Keywords
Abstract This paper presents an adaptive thresholding approach intended to increase the number of detected corners, while reducing the amount of those ones corresponding to noisy data. The proposed approach works by using the classical Harris corner detector algorithm and overcome the difficulty in finding a general threshold that work well for all the images in a given data set by proposing a novel adaptive thresholding scheme. Initially, two thresholds are used to discern between strong corners and flat regions. Then, a region based criteria is used to discriminate between weak corners and noisy points in the midway interval. Experimental results show that the proposed approach has a better capability to reject false corners and, at the same time, to detect weak ones. Comparisons with the state of the art are provided showing the validity of the proposed approach.
Address Póvoa de Varzim; Portugal; June 2013
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-39093-7 Medium
Area Expedition Conference ICIAR
Notes ADAS; 600.055 Approved no
Call Number Admin @ si @ ViS2013 Serial 2562
Permanent link to this record
 
 
Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez
Title DA-DPM Pedestrian Detection Type Conference Article
Year 2013 Publication ICCV Workshop on Reconstruction meets Recognition Abbreviated Journal
Volume Issue Pages
Keywords Domain Adaptation; Pedestrian Detection
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCVW-RR
Notes ADAS Approved no
Call Number Admin @ si @ XRV2013 Serial 2569
Permanent link to this record
 
 
Author Marc Bolaños; Maite Garolera; Petia Radeva
Title Active labeling application applied to food-related object recognition Type Conference Article
Year 2013 Publication 5th International Workshop on Multimedia for Cooking & Eating Activities Abbreviated Journal
Volume Issue Pages 45-50
Keywords
Abstract Every day, lifelogging devices, available for recording different aspects of our daily life, increase in number, quality and functions, just like the multiple applications that we give to them. Applying wearable devices to analyse the nutritional habits of people is a challenging application based on acquiring and analyzing life records in long periods of time. However, to extract the information of interest related to the eating patterns of people, we need automatic methods to process large amount of life-logging data (e.g. recognition of food-related objects). Creating a rich set of manually labeled samples to train the algorithms is slow, tedious and subjective. To address this problem, we propose a novel method in the framework of Active Labeling for construct- ing a training set of thousands of images. Inspired by the hierarchical sampling method for active learning [6], we propose an Active forest that organizes hierarchically the data for easy and fast labeling. Moreover, introducing a classifier into the hierarchical structures, as well as transforming the feature space for better data clustering, additionally im- prove the algorithm. Our method is successfully tested to label 89.700 food-related objects and achieves significant reduction in expert time labelling.

Active labeling application applied to food-related object recognition ResearchGate. Available from: http://www.researchgate.net/publication/262252017Activelabelingapplicationappliedtofood-relatedobjectrecognition [accessed Jul 14, 2015].
Address Barcelona; October 2013
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ACM-CEA
Notes MILAB Approved no
Call Number Admin @ si @ BGR2013b Serial 2637
Permanent link to this record
 
 
Author Mohammad Rouhani; Angel Sappa
Title The Richer Representation the Better Registration Type Journal Article
Year 2013 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 22 Issue 12 Pages 5036-5049
Keywords
Abstract In this paper, the registration problem is formulated as a point to model distance minimization. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, this formulation avoids the correspondence search that is time-consuming. In the first stage, the target set is described through an implicit function by employing a linear least squares fitting. This function can be either an implicit polynomial or an implicit B-spline from a coarse to fine representation. In the second stage, we show how the obtained implicit representation is used as an interface to convert point-to-point registration into point-to-implicit problem. Furthermore, we show that this registration distance is smooth and can be minimized through the Levengberg-Marquardt algorithm. All the formulations presented for both stages are compact and easy to implement. In addition, we show that our registration method can be handled using any implicit representation though some are coarse and others provide finer representations; hence, a tradeoff between speed and accuracy can be set by employing the right implicit function. Experimental results and comparisons in 2D and 3D show the robustness and the speed of convergence of the proposed approach.
Address
Corporate Author Thesis
Publisher Place of Publication Editor (up)
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1057-7149 ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ RoS2013 Serial 2665
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