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Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
Title Efficient Exemplar Word Spotting Type Conference Article
Year 2012 Publication 23rd British Machine Vision Conference Abbreviated Journal
Volume Issue Pages (down) 67.1- 67.11
Keywords
Abstract In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
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 1-901725-46-4 Medium
Area Expedition Conference BMVC
Notes DAG Approved no
Call Number DAG @ dag @ AGF2012 Serial 1984
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Author Olivier Penacchio; Laura Dempere-Marco; Xavier Otazu
Title A Neurodynamical Model Of Brightness Induction In V1 Following Static And Dynamic Contextual Influences Type Abstract
Year 2012 Publication 8th Federation of European Neurosciences Abbreviated Journal
Volume 6 Issue Pages (down) 63-64
Keywords
Abstract Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Although striate cortex is traditionally regarded as an area mostly responsive to ensory (i.e. retinal) information,
neurophysiological evidence suggests that perceived brightness information mightbe explicitly represented in V1.
Such evidence has been observed both in anesthetised cats where neuronal response modulations have been found to follow luminance changes outside the receptive felds and in human fMRI measurements. In this work, possible neural mechanisms that ofer a plausible explanation for such phenomenon are investigated. To this end, we consider the model proposed by Z.Li (Li, Network:Comput. Neural Syst., 10 (1999)) which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual infuences, i.e. layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has reproduced other phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant efect of contextual infuences. We have extended the original model such that the input to the network is obtained from a complete multiscale and multiorientation wavelet decomposition, thereby allowing the recovery of an image refecting the perceived intensity. The proposed model successfully accounts for well known psychophysical efects for static contexts (among them: the White's and modifed White's efects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction efects) and also for brigthness induction in dynamic contexts defned by modulating the luminance of surrounding areas (e.g. the brightness of a static central area is perceived to vary in antiphase to the sinusoidal luminance changes of its surroundings). This work thus suggests that intra-cortical interactions in V1 could partially explain perceptual brightness induction efects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual processing pathway.
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 FENS
Notes CIC Approved no
Call Number Admin @ si @ PDO2012b Serial 2181
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Author Marçal Rusiñol; Josep Llados
Title The Role of the Users in Handwritten Word Spotting Applications: Query Fusion and Relevance Feedback Type Conference Article
Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages (down) 55-60
Keywords
Abstract In this paper we present the importance of including the user in the loop in a handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and a baseline word spotting approach based on a bag-of-visual-words model.
Address Bari, Italy
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 978-1-4673-2262-1 Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number Admin @ si @ RuL2012 Serial 2054
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Author Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
Title Low-dimensional and Comprehensive Color Texture Description Type Journal Article
Year 2012 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 116 Issue I Pages (down) 54-67
Keywords
Abstract Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap
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 1077-3142 ISBN Medium
Area Expedition Conference
Notes CAT;CIC Approved no
Call Number Admin @ si @ ASV2012 Serial 1827
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title Modulating Shape Features by Color Attention for Object Recognition Type Journal Article
Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 98 Issue 1 Pages (down) 49-64
Keywords
Abstract Bag-of-words based image representation is a successful approach for object recognition. Generally, the subsequent stages of the process: feature detection,feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information.
Address
Corporate Author Thesis
Publisher Springer Netherlands Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ KWV2012 Serial 1864
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Author Volkmar Frinken; Markus Baumgartner; Andreas Fischer; Horst Bunke
Title Semi-Supervised Learning for Cursive Handwriting Recognition using Keyword Spotting Type Conference Article
Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages (down) 49-54
Keywords
Abstract State-of-the-art handwriting recognition systems are learning-based systems that require large sets of training data. The creation of training data, and consequently the creation of a well-performing recognition system, requires therefore a substantial amount of human work. This can be reduced with semi-supervised learning, which uses unlabeled text lines for training as well. Current approaches estimate the correct transcription of the unlabeled data via handwriting recognition which is not only extremely demanding as far as computational costs are concerned but also requires a good model of the target language. In this paper, we propose a different approach that makes use of keyword spotting, which is significantly faster and does not need any language model. In a set of experiments we demonstrate its superiority over existing approaches.
Address Bari, Italy
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 10.1109/ICFHR.2012.268 ISBN 978-1-4673-2262-1 Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number Admin @ si @ FBF2012 Serial 2055
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Author Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo
Title Feature Selection Based on Reinforcement Learning for Object Recognition Type Conference Article
Year 2012 Publication Adaptive Learning Agents Workshop Abbreviated Journal
Volume Issue Pages (down) 33-39
Keywords
Abstract
Address Valencia
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 ALA
Notes ADAS; RV Approved no
Call Number Admin @ si @ PSL2012 Serial 2018
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Author Albert Gordo; Florent Perronnin; Ernest Valveny
Title Document classification using multiple views Type Conference Article
Year 2012 Publication 10th IAPR International Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages (down) 33-37
Keywords
Abstract The combination of multiple features or views when representing documents or other kinds of objects usually leads to improved results in classification (and retrieval) tasks. Most systems assume that those views will be available both at training and test time. However, some views may be too `expensive' to be available at test time. In this paper, we consider the use of Canonical Correlation Analysis to leverage `expensive' views that are available only at training time. Experimental results show that this information may significantly improve the results in a classification task.
Address Australia
Corporate Author Thesis
Publisher IEEE Computer Society Washington Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-7695-4661-2 Medium
Area Expedition Conference DAS
Notes DAG Approved no
Call Number Admin @ si @ GPV2012 Serial 2049
Permanent link to this record
 
 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Three-Dimensional Design of Error Correcting Output Codes Type Conference Article
Year 2012 Publication 8th International Conference on Machine Learning and Data Mining Abbreviated Journal
Volume Issue Pages (down) 29-
Keywords
Abstract
Address Berlin, Germany
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 MLDM
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2012a Serial 2041
Permanent link to this record
 
 
Author Alicia Fornes; Josep Llados; Gemma Sanchez; Horst Bunke
Title Writer Identification in Old Handwritten Music Scores Type Book Chapter
Year 2012 Publication Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology Abbreviated Journal
Volume Issue Pages (down) 27-63
Keywords
Abstract The aim of writer identification is determining the writer of a piece of handwriting from a set of writers. In this paper we present a system for writer identification in old handwritten music scores. Even though an important amount of compositions contains handwritten text in the music scores, the aim of our work is to use only music notation to determine the author. The steps of the system proposed are the following. First of all, the music sheet is preprocessed and normalized for obtaining a single binarized music line, without the staff lines. Afterwards, 100 features are extracted for every music line, which are subsequently used in a k-NN classifier that compares every feature vector with prototypes stored in a database. By applying feature selection and extraction methods on the original feature set, the performance is increased. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving a recognition rate of about 95%.
Address
Corporate Author Thesis
Publisher IGI-Global Place of Publication Editor Copnstantin Papaodysseus
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 Admin @ si @ FLS2012 Serial 1828
Permanent link to this record
 
 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Efficient pairwise classification using Local Cross Off strategy Type Conference Article
Year 2012 Publication 25th Canadian Conference on Artificial Intelligence Abbreviated Journal
Volume 7310 Issue Pages (down) 25-36
Keywords
Abstract The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
Address Toronto, Ontario
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-30352-4 Medium
Area Expedition Conference AI
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2012c Serial 2044
Permanent link to this record
 
 
Author Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez
Title Moving object detection from mobile platforms using stereo data registration Type Book Chapter
Year 2012 Publication Computational Intelligence paradigms in advanced pattern classification Abbreviated Journal
Volume 386 Issue Pages (down) 25-37
Keywords pedestrian detection
Abstract This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor Marek R. Ogiela; Lakhmi C. Jain
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1860-949X ISBN 978-3-642-24048-5 Medium
Area Expedition Conference
Notes ADAS Approved no
Call Number Admin @ si @ SGD2012 Serial 2061
Permanent link to this record
 
 
Author Xavier Perez Sala; Laura Igual; Sergio Escalera; Cecilio Angulo
Title Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models Type Book Chapter
Year 2012 Publication Vision Robotics: Technologies for Machine Learning and Vision Applications Abbreviated Journal
Volume Issue 2 Pages (down) 23-42
Keywords
Abstract Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis.
Address
Corporate Author Thesis
Publisher IGI-Global 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 MILAB;HuPBA Approved no
Call Number Admin @ si @ PIE2012 Serial 2064
Permanent link to this record
 
 
Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez
Title Video Co-segmentation Type Conference Article
Year 2012 Publication 11th Asian Conference on Computer Vision Abbreviated Journal
Volume 7725 Issue Pages (down) 13-24
Keywords
Abstract Segmentation of a single image is in general a highly underconstrained problem. A frequent approach to solve it is to somehow provide prior knowledge or constraints on how the objects of interest look like (in terms of their shape, size, color, location or structure). Image co-segmentation trades the need for such knowledge for something much easier to obtain, namely, additional images showing the object from other viewpoints. Now the segmentation problem is posed as one of differentiating the similar object regions in all the images from the more varying background. In this paper, for the first time, we extend this approach to video segmentation: given two or more video sequences showing the same object (or objects belonging to the same class) moving in a similar manner, we aim to outline its region in all the frames. In addition, the method works in an unsupervised manner, by learning to segment at testing time. We compare favorably with two state-of-the-art methods on video segmentation and report results on benchmark videos.
Address Daejeon, Korea
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-37443-2 Medium
Area Expedition Conference ACCV
Notes ADAS Approved no
Call Number Admin @ si @ RSL2012d Serial 2153
Permanent link to this record
 
 
Author Cesar Isaza; Joaquin Salas; Bogdan Raducanu
Title Synthetic ground truth dataset to detect shadow cast by static objects in outdoor Type Conference Article
Year 2012 Publication 1st International Workshop on Visual Interfaces for Ground Truth Collection in Computer Vision Applications Abbreviated Journal
Volume Issue Pages (down) art. 11
Keywords
Abstract In this paper, we propose a precise synthetic ground truth dataset to study the problem of detection of the shadows cast by static objects in outdoor environments during extended periods of time (days). For our dataset, we have created a virtual scenario using a rendering software. To increase the realism of the simulated environment, we have defined the scenario in a precise geographical location. In our dataset the sun is by far the main illumination source. The sun position during the simulation time takes into consideration factors related to the geographical location, such as the latitude, longitude, elevation above sea level, and precise image capturing day and time. In our simulation the camera remains fixed. The dataset consists of seven days of simulation, from 10:00am to 5:00pm. Images are captured every 10 seconds. The shadows' ground truth is automatically computed by the rendering software.
Address Capri, Italy
Corporate Author Thesis
Publisher ACM Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4503-1405-3 Medium
Area Expedition Conference VIGTA
Notes OR;MV Approved no
Call Number Admin @ si @ ISR2012a Serial 2037
Permanent link to this record