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Author C. Alejandro Parraga; Robert Benavente; Maria Vanrell; Ramon Baldrich
Title Psychophysical measurements to model inter-colour regions of colour-naming space Type Journal Article
Year 2009 Publication Journal of Imaging Science and Technology Abbreviated Journal
Volume 53 Issue 3 Pages (down) 031106 (8 pages)
Keywords image processing; Analysis
Abstract JCR Impact Factor 2009: 0.391
In this paper, we present a fuzzy-set of parametric functions which segment the CIE lab space into eleven regions which correspond to the group of common universal categories present in all evolved languages as identified by anthropologists and linguists. The set of functions is intended to model a color-name assignment task by humans and differs from other models in its emphasis on the inter-color boundary regions, which were explicitly measured by means of a psychophysics experiment. In our particular implementation, the CIE lab space was segmented into eleven color categories using a Triple Sigmoid as the fuzzy sets basis, whose parameters are included in this paper. The model’s parameters were adjusted according to the psychophysical results of a yes/no discrimination paradigm where observers had to choose (English) names for isoluminant colors belonging to regions in-between neighboring categories. These colors were presented on a calibrated CRT monitor (14-bit x 3 precision). The experimental results show that inter- color boundary regions are much less defined than expected and color samples other than those near the most representatives are needed to define the position and shape of boundaries between categories. The extended set of model parameters is given as a table.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
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 CAT @ cat @ PBV2009 Serial 1157
Permanent link to this record
 

 
Author Javier Vazquez; C. Alejandro Parraga; Maria Vanrell; Ramon Baldrich
Title Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset Type Journal Article
Year 2009 Publication Journal of Imaging Science and Technology Abbreviated Journal
Volume 53 Issue 3 Pages (down) 031105–9
Keywords
Abstract The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evaluation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical illuminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate illuminants; in this case selection is based on increasing the performance on the subsequent steps of the systems. In this paper we propose a new performance measure, the perceptual angular error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experiment comparing solutions from three different color constancy algorithms. Our results show that in more than a half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral grey sphere, which better copes with the illuminant variations of the scene.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
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 CAT @ cat @ VPV2009a Serial 1171
Permanent link to this record
 

 
Author Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez
Title Automatic Ground-truthing using video registration for on-board detection algorithms Type Conference Article
Year 2009 Publication 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages (down) 4389 - 4392
Keywords
Abstract Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-4880 ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes ADAS Approved no
Call Number ADAS @ adas @ ADS2009 Serial 1201
Permanent link to this record
 

 
Author Angel Sappa; Mohammad Rouhani
Title Efficient Distance Estimation for Fitting Implicit Quadric Surfaces Type Conference Article
Year 2009 Publication 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages (down) 3521–3524
Keywords
Abstract This paper presents a novel approach for estimating the shortest Euclidean distance from a given point to the corresponding implicit quadric fitting surface. It first estimates the orthogonal orientation to the surface from the given point; then the shortest distance is directly estimated by intersecting the implicit surface with a line passing through the given point according to the estimated orthogonal orientation. The proposed orthogonal distance estimation is easily obtained without increasing computational complexity; hence it can be used in error minimization surface fitting frameworks. Comparisons of the proposed metric with previous approaches are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. Surfaces fitted by using the proposed geometric distance estimation and state of the art metrics are presented to show the viability of the proposed approach.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-4880 ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes ADAS Approved no
Call Number ADAS @ adas @ SaR2009 Serial 1232
Permanent link to this record
 

 
Author Carlo Gatta; Petia Radeva
Title Bilateral Enhancers Type Conference Article
Year 2009 Publication 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages (down) 3161-3165
Keywords
Abstract Ten years ago the concept of bilateral filtering (BF) became popular in the image processing community. The core of the idea is to blend the effect of a spatial filter, as e.g. the Gaussian filter, with the effect of a filter that acts on image values. The two filters acts on orthogonal domains of a picture: the 2D lattice of the image support and the intensity (or color) domain. The BF approach is an intuitive way to blend these two filters giving rise to algorithms that perform difficult tasks requiring a relatively simple design. In this paper we extend the concept of BF, proposing the bilateral enhancers (BE). We show how to design proper functions to obtain an edge-preserving smoothing and a selective sharpening. Moreover, we show that the proposed algorithm can perform edge-preserving smoothing and selective sharpening simultaneously in a single filtering.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-4880 ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes MILAB Approved no
Call Number BCNPCL @ bcnpcl @ GaR2009b Serial 1243
Permanent link to this record
 

 
Author Ignasi Rius; Jordi Gonzalez; Javier Varona; Xavier Roca
Title Action-specific motion prior for efficient bayesian 3D human body tracking Type Journal Article
Year 2009 Publication Pattern Recognition Abbreviated Journal PR
Volume 42 Issue 11 Pages (down) 2907–2921
Keywords
Abstract In this paper, we aim to reconstruct the 3D motion parameters of a human body
model from the known 2D positions of a reduced set of joints in the image plane.
Towards this end, an action-specific motion model is trained from a database of real
motion-captured performances. The learnt motion model is used within a particle
filtering framework as a priori knowledge on human motion. First, our dynamic
model guides the particles according to similar situations previously learnt. Then, the solution space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, we are able to track the 3D configuration of the full human body from several cycles of walking motion sequences using only the 2D positions of a very reduced set of joints from lateral or frontal viewpoints.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-3203 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number ISE @ ise @ RGV2009 Serial 1159
Permanent link to this record
 

 
Author Daniel Ponsa; Antonio Lopez
Title Variance reduction techniques in particle-based visual contour Tracking Type Journal Article
Year 2009 Publication Pattern Recognition Abbreviated Journal PR
Volume 42 Issue 11 Pages (down) 2372–2391
Keywords Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling
Abstract This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number ADAS @ adas @ PoL2009a Serial 1168
Permanent link to this record
 

 
Author Jose Antonio Rodriguez; Florent Perronnin
Title Handwritten word-spotting using hidden Markov models and universal vocabularies Type Journal Article
Year 2009 Publication Pattern Recognition Abbreviated Journal PR
Volume 42 Issue 9 Pages (down) 2103-2116
Keywords Word-spotting; Hidden Markov model; Score normalization; Universal vocabulary; Handwriting recognition
Abstract Handwritten word-spotting is traditionally viewed as an image matching task between one or multiple query word-images and a set of candidate word-images in a database. This is a typical instance of the query-by-example paradigm. In this article, we introduce a statistical framework for the word-spotting problem which employs hidden Markov models (HMMs) to model keywords and a Gaussian mixture model (GMM) for score normalization. We explore the use of two types of HMMs for the word modeling part: continuous HMMs (C-HMMs) and semi-continuous HMMs (SC-HMMs), i.e. HMMs with a shared set of Gaussians. We show on a challenging multi-writer corpus that the proposed statistical framework is always superior to a traditional matching system which uses dynamic time warping (DTW) for word-image distance computation. A very important finding is that the SC-HMM is superior when labeled training data is scarce—as low as one sample per keyword—thanks to the prior information which can be incorporated in the shared set of Gaussians.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0031-3203 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ RoP2009 Serial 1053
Permanent link to this record
 

 
Author Miquel Ferrer; Ernest Valveny; F. Serratosa
Title Median Graphs: A Genetic Approach based on New Theoretical Properties Type Journal Article
Year 2009 Publication Pattern Recognition Abbreviated Journal PR
Volume 42 Issue 9 Pages (down) 2003–2012
Keywords Median graph; Genetic search; Maximum common subgraph; Graph matching; Structural pattern recognition
Abstract Given a set of graphs, the median graph has been theoretically presented as a useful concept to infer a representative of the set. However, the computation of the median graph is a highly complex task and its practical application has been very limited up to now. In this work we present two major contributions. On one side, and from a theoretical point of view, we show new theoretical properties of the median graph. On the other side, using these new properties, we present a new approximate algorithm based on the genetic search, that improves the computation of the median graph. Finally, we perform a set of experiments on real data, where none of the existing algorithms for the median graph computation could be applied up to now due to their computational complexity. With these results, we show how the concept of the median graph can be used in real applications and leaves the box of the only-theoretical concepts, demonstrating, from a practical point of view, that can be a useful tool to represent a set of graphs.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ FVS2009b Serial 1167
Permanent link to this record
 

 
Author Sergio Escalera; Alicia Fornes; Oriol Pujol; Alberto Escudero; Petia Radeva
Title Circular Blurred Shape Model for Symbol Spotting in Documents Type Conference Article
Year 2009 Publication 16th IEEE International Conference on Image Processing Abbreviated Journal
Volume Issue Pages (down) 1985-1988
Keywords
Abstract Symbol spotting problem requires feature extraction strategies able to generalize from training samples and to localize the target object while discarding most part of the image. In the case of document analysis, symbol spotting techniques have to deal with a high variability of symbols' appearance. In this paper, we propose the Circular Blurred Shape Model descriptor. Feature extraction is performed capturing the spatial arrangement of significant object characteristics in a correlogram structure. Shape information from objects is shared among correlogram regions, being tolerant to the irregular deformations. Descriptors are learnt using a cascade of classifiers and Abadoost as the base classifier. Finally, symbol spotting is performed by means of a windowing strategy using the learnt cascade over plan and old musical score documents. Spotting and multi-class categorization results show better performance comparing with the state-of-the-art descriptors.
Address Cairo, Egypt
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4244-5653-6 Medium
Area Expedition Conference ICIP
Notes MILAB;HuPBA;DAG Approved no
Call Number BCNPCL @ bcnpcl @ EFP2009b Serial 1184
Permanent link to this record
 

 
Author Bogdan Raducanu; Jordi Vitria; D. Gatica-Perez
Title You are Fired! Nonverbal Role Analysis in Competitive Meetings Type Conference Article
Year 2009 Publication IEEE International Conference on Audio, Speech and Signal Processing Abbreviated Journal
Volume Issue Pages (down) 1949–1952
Keywords
Abstract This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words.
Address Taipei, Taiwan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1520-6149 ISBN 978-1-4244-2353-8 Medium
Area Expedition Conference ICASSP
Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ RVG2009 Serial 1154
Permanent link to this record
 

 
Author Aura Hernandez-Sabate; Debora Gil;Eduard Fernandez-Nofrerias;Petia Radeva; Enric Marti
Title Approaching Artery Rigid Dynamics in IVUS Type Journal Article
Year 2009 Publication IEEE Transactions on Medical Imaging Abbreviated Journal TMI
Volume 28 Issue 11 Pages (down) 1670-1680
Keywords Fourier analysis; intravascular ultrasound (IVUS) dynamics; longitudinal motion; quality measures; tissue deformation.
Abstract Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel-catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical cases.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0278-0062 ISBN Medium
Area Expedition Conference
Notes IAM; MILAB Approved no
Call Number IAM @ iam @ HGF2009 Serial 1545
Permanent link to this record
 

 
Author Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone
Title Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework Type Journal Article
Year 2009 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 31 Issue 9 Pages (down) 1630–1644
Keywords
Abstract The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0162-8828 ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ RVT2009 Serial 1220
Permanent link to this record
 

 
Author Xavier Baro; Sergio Escalera; Petia Radeva; Jordi Vitria
Title Visual Content Layer for Scalable Recognition in Urban Image Databases, Internet Multimedia Search and Mining Type Conference Article
Year 2009 Publication 10th IEEE International Conference on Multimedia and Expo Abbreviated Journal
Volume Issue Pages (down) 1616–1619
Keywords
Abstract Rich online map interaction represents a useful tool to get multimedia information related to physical places. With this type of systems, users can automatically compute the optimal route for a trip or to look for entertainment places or hotels near their actual position. Standard maps are defined as a fusion of layers, where each one contains specific data such height, streets, or a particular business location. In this paper we propose the construction of a visual content layer which describes the visual appearance of geographic locations in a city. We captured, by means of a Mobile Mapping system, a huge set of georeferenced images (> 500K) which cover the whole city of Barcelona. For each image, hundreds of region descriptions are computed off-line and described as a hash code. This allows an efficient and scalable way of accessing maps by visual content.
Address New York (USA)
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4244-4291-1 Medium
Area Expedition Conference ICME
Notes OR;MILAB;HuPBA;MV Approved no
Call Number BCNPCL @ bcnpcl @ BER2009 Serial 1189
Permanent link to this record
 

 
Author Joost Van de Weijer; Cordelia Schmid; Jakob Verbeek; Diane Larlus
Title Learning Color Names for Real-World Applications Type Journal Article
Year 2009 Publication IEEE Transaction in Image Processing Abbreviated Journal TIP
Volume 18 Issue 7 Pages (down) 1512–1524
Keywords
Abstract Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labelled color chips. These color chips are labelled with color names within a well-defined experimental setup by human test subjects. However naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labelling real-world images with color names we use Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labelled color chips for both image retrieval and image annotation.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
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 Approved no
Call Number CAT @ cat @ WSV2009 Serial 1195
Permanent link to this record