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Author Pierluigi Casale edit  openurl
  Title Approximate Ensemble Methods for Physical Activity Recognition Applications Type Book Whole
  Year 2011 Publication PhD Thesis, Universitat de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The main interest of this thesis focuses on computational methodologies able to
reduce the degree of complexity of learning algorithms and its application to physical
activity recognition.
Random Projections will be used to reduce the computational complexity in Multiple Classifier Systems. A new boosting algorithm and a new one-class classification
methodology have been developed. In both cases, random projections are used for
reducing the dimensionality of the problem and for generating diversity, exploiting in
this way the benefits that ensembles of classifiers provide in terms of performances
and stability. Moreover, the new one-class classification methodology, based on an ensemble strategy able to approximate a multidimensional convex-hull, has been proved
to over-perform state-of-the-art one-class classification methodologies.
The practical focus of the thesis is towards Physical Activity Recognition. A new
hardware platform for wearable computing application has been developed and used
for collecting data of activities of daily living allowing to study the optimal features
set able to successful classify activities.
Based on the classification methodologies developed and the study conducted on
physical activity classification, a machine learning architecture capable to provide a
continuous authentication mechanism for mobile-devices users has been worked out,
as last part of the thesis. The system, based on a personalized classifier, states on
the analysis of the characteristic gait patterns typical of each individual ensuring an
unobtrusive and continuous authentication mechanism		  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor (down) Oriol Pujol;Petia Radeva  
  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 Approved no  
  Call Number Admin @ si @ Cas2011 Serial 1837  
Permanent link to this record
 
 
Author Debora Gil; Jaume Garcia; Ruth Aris; Guillaume Houzeaux; Manuel Vazquez edit   pdf
openurl 
  Title A Riemmanian approach to cardiac fiber architecture modelling Type Conference Article
  Year 2009 Publication 1st International Conference on Mathematical & Computational Biomedical Engineering Abbreviated Journal  
  Volume Issue Pages 59-62  
  Keywords cardiac fiber architecture; diffusion tensor magnetic resonance imaging; differential (Rie- mannian) geometry.  
  Abstract There is general consensus that myocardial fiber architecture should be modelled in order to fully understand the electromechanical properties of the Left Ventricle (LV). Diffusion Tensor magnetic resonance Imaging (DTI) is the reference image modality for rapid measurement of fiber orientations by means of the tensor principal eigenvectors. In this work, we present a mathematical framework for across subject comparison of the local geometry of the LV anatomy including the fiber architecture from the statistical analysis of DTI studies. We use concepts of differential geometry for defining a parametric domain suitable for statistical analysis of a low number of samples. We use Riemannian metrics to define a consistent computation of DTI principal eigenvector modes of variation. Our framework has been applied to build an atlas of the LV fiber architecture from 7 DTI normal canine hearts.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Swansea (UK) Editor (down) Nithiarasu, R.L.R.V.L.  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CMBE  
  Notes IAM Approved no  
  Call Number IAM @ iam @ FGA2009 Serial 1520  
Permanent link to this record
 
 
Author Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis edit  isbn
openurl 
  Title Advances in Vision-Based Human Body Modeling Type Book Chapter
  Year 2004 Publication 3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body Abbreviated Journal  
  Volume Issue Pages 1-26  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (down) N. Sarris and M. Strintzis.  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 1-59140-299-9 Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ SAG2004a Serial 458  
Permanent link to this record
 
 
Author Sergio Vera; Debora Gil; Agnes Borras; Marius George Linguraru; Miguel Angel Gonzalez Ballester edit   pdf
url  doi
openurl 
  Title Geometric Steerable Medial Maps Type Journal Article
  Year 2013 Publication Machine Vision and Applications Abbreviated Journal MVA  
  Volume 24 Issue 6 Pages 1255-1266  
  Keywords Medial Representations ,Medial Manifolds Comparation , Surface , Reconstruction  
  Abstract In order to provide more intuitive and easily interpretable representations of complex shapes/organs, medial manifolds should reach a compromise between simplicity in geometry and capability for restoring the anatomy/shape of the organ/volume. Existing morphological methods show excellent results when applied to 2D objects, but their quality drops across dimensions.
This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoids degenerated medial axis segments. Second, we introduce a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to syn- thetic shapes of known medial geometry. We also show its higher performance for medical imaging applications in terms of simplicity of medial structures and capability for reconstructing the anatomical volume.		  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor (down) Mubarak Shah  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes IAM; 605.203; 600.060; 600.044 Approved no  
  Call Number IAM @ iam @ VGB2013 Serial 2192  
Permanent link to this record
 
 
Author Ariel Amato edit  openurl
  Title Environment-Independent Moving Cast Shadow Suppression in Video Surveillance Type Book Whole
  Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This thesis is devoted to moving shadows detection and suppression. Shadows could be defined as the parts of the scene that are not directly illuminated by a light source due to obstructing object or objects. Often, moving shadows in images sequences are undesirable since they could cause degradation of the expected results during processing of images for object detection, segmentation, scene surveillance or similar purposes. In this thesis first moving shadow detection methods are exhaustively overviewed. Beside the mentioned methods from literature and to compensate their limitations a new moving shadow detection method is proposed. It requires no prior knowledge about the scene, nor is it restricted to assumptions about specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene the values of the background image are divided by values of the current frame in the RGB color space. In the thesis how this luminance ratio can be used to identify segments with low gradient constancy is shown, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of the proposed method compared with the most sophisticated state-of-the-art shadow detection algorithms. These results show that the proposed approach is robust and accurate over a broad range of shadow types and challenging video conditions.  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor (down) Mikhail Mozerov;Jordi Gonzalez  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ Ama2012 Serial 2201  
Permanent link to this record
 
 
Author Joost Van de Weijer; Shida Beigpour edit   pdf
url  isbn
openurl 
  Title The Dichromatic Reflection Model: Future Research Directions and Applications Type Conference Article
  Year 2011 Publication International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal  
  Volume Issue Pages  
  Keywords dblp  
  Abstract The dichromatic reflection model (DRM) predicts that color distributions form a parallelogram in color space, whose shape is defined by the body reflectance and the illuminant color. In this paper we resume the assumptions which led to the DRM and shortly recall two of its main applications domains: color image segmentation and photometric invariant feature computation. After having introduced the model we discuss several limitations of the theory, especially those which are raised once working on real-world uncalibrated images. In addition, we summerize recent extensions of the model which allow to handle more complicated light interactions. Finally, we suggest some future research directions which would further extend its applicability.  
  Address Algarve, Portugal  
  Corporate Author Thesis  
  Publisher SciTePress Place of Publication Editor (down) Mestetskiy, Leonid and Braz, José  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-989-8425-47-8 Medium  
  Area Expedition Conference VISIGRAPP  
  Notes CIC Approved no  
  Call Number Admin @ si @ WeB2011 Serial 1778  
Permanent link to this record
 
 
Author Susana Alvarez edit  openurl
  Title Revisión de la teoría de los Textons Enfoque computacional en color Type Book Whole
  Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract El color y la textura son dos estímulos visuales importantes para la interpretación de las imágenes. La definición de descriptores computacionales que combinan estas dos características es aún un problema abierto. La dificultad se deriva esencialmente de la propia naturaleza de ambas, mientras que la textura es una propiedad de una región, el color es una propiedad de un punto.

Hasta ahora se han utilizado tres los tipos de aproximaciones para la combinación, (a) se describe la textura directamente en cada uno de los canales color, (b) se describen textura y color por separado y se combinan al final, y (c) la combinación se realiza con técnicas de aprendizaje automático. Considerando que este problema se resuelve en el sistema visual humano en niveles muy tempranos, en esta tesis se propone estudiar el problema a partir de la implementación directa de una teoría perceptual, la teoría de los textons, y explorar así su extensión a color.

Puesto que la teoría de los textons se basa en la descripción de la textura a partir de las densidades de los atributos locales, esto se adapta perfectamente al marco de trabajo de los descriptores holísticos (bag-of-words). Se han estudiado diversos descriptores basados en diferentes espacios de textons, y diferentes representaciones de las imágenes. Asimismo se ha estudiado la viabilidad de estos descriptores en una representación conceptual de nivel intermedio.

Los descriptores propuestos han demostrado ser muy eficientes en aplicaciones de recuperación y clasificación de imágenes, presentando ventajas en la generación de vocabularios. Los vocabularios se obtienen cuantificando directamente espacios de baja dimensión y la perceptualidad de estos espacios permite asociar semántica de bajo nivel a las palabras visuales. El estudio de los resultados permite concluir que si bien la aproximación holística es muy eficiente, la introducción de co-ocurrencia espacial de las propiedades de forma y color de los blobs de la imagen es un elemento clave para su combinación, hecho que no contradice las evidencias en percepción		  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor (down) Maria Vanrell;Xavier Otazu  
  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 Alv2012b Serial 2216  
Permanent link to this record
 
 
Author Hassan Ahmed Sial edit  isbn
openurl 
  Title Estimating Light Effects from a Single Image: Deep Architectures and Ground-Truth Generation Type Book Whole
  Year 2021 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract In this thesis, we explore how to estimate the effects of the light interacting with the scene objects from a single image. To achieve this goal, we focus on recovering intrinsic components like reflectance, shading, or light properties such as color and position using deep architectures. The success of these approaches relies on training on large and diversified image datasets. Therefore, we present several contributions on this such as: (a) a data-augmentation technique; (b) a ground-truth for an existing multi-illuminant dataset; (c) a family of synthetic datasets, SID for Surreal Intrinsic Datasets, with diversified backgrounds and coherent light conditions; and (d) a practical pipeline to create hybrid ground-truths to overcome the complexity of acquiring realistic light conditions in a massive way. In parallel with the creation of datasets, we trained different flexible encoder-decoder deep architectures incorporating physical constraints from the image formation models.

In the last part of the thesis, we apply all the previous experience to two different problems. Firstly, we create a large hybrid Doc3DShade dataset with real shading and synthetic reflectance under complex illumination conditions, that is used to train a two-stage architecture that improves the character recognition task in complex lighting conditions of unwrapped documents. Secondly, we tackle the problem of single image scene relighting by extending both, the SID dataset to present stronger shading and shadows effects, and the deep architectures to use intrinsic components to estimate new relit images.		  
  Address September 2021  
  Corporate Author Thesis Ph.D. thesis  
  Publisher IMPRIMA Place of Publication Editor (down) Maria Vanrell;Ramon Baldrich  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-122714-8-5 Medium  
  Area Expedition Conference  
  Notes CIC; Approved no  
  Call Number Admin @ si @ Sia2021 Serial 3607  
Permanent link to this record
 
 
Author Javier Vazquez edit  openurl
  Title Colour Constancy in Natural Through Colour Naming and Sensor Sharpening Type Book Whole
  Year 2011 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Colour is derived from three physical properties: incident light, object reflectance and sensor sensitivities. Incident light varies under natural conditions; hence, recovering scene illuminant is an important issue in computational colour. One way to deal with this problem under calibrated conditions is by following three steps, 1) building a narrow-band sensor basis to accomplish the diagonal model, 2) building a feasible set of illuminants, and 3) defining criteria to select the best illuminant. In this work we focus on colour constancy for natural images by introducing perceptual criteria in the first and third stages.
To deal with the illuminant selection step, we hypothesise that basic colour categories can be used as anchor categories to recover the best illuminant. These colour names are related to the way that the human visual system has evolved to encode relevant natural colour statistics. Therefore the recovered image provides the best representation of the scene labelled with the basic colour terms. We demonstrate with several experiments how this selection criterion achieves current state-of-art results in computational colour constancy. In addition to this result, we psychophysically prove that usual angular error used in colour constancy does not correlate with human preferences, and we propose a new perceptual colour constancy evaluation.
The implementation of this selection criterion strongly relies on the use of a diagonal
model for illuminant change. Consequently, the second contribution focuses on building an appropriate narrow-band sensor basis to represent natural images. We propose to use the spectral sharpening technique to compute a unique narrow-band basis optimised to represent a large set of natural reflectances under natural illuminants and given in the basis of human cones. The proposed sensors allow predicting unique hues and the World colour Survey data independently of the illuminant by using a compact singularity function. Additionally, we studied different families of sharp sensors to minimise different perceptual measures. This study brought us to extend the spherical sampling procedure from 3D to 6D.
Several research lines still remain open. One natural extension would be to measure the
effects of using the computed sharp sensors on the category hypothesis, while another might be to insert spatial contextual information to improve category hypothesis. Finally, much work still needs to be done to explore how individual sensors can be adjusted to the colours in a scene.		  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor (down) Maria Vanrell;Graham D. Finlayson  
  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 @ Vaz2011a Serial 1785  
Permanent link to this record
 
 
Author Jordi Roca edit  openurl
  Title Constancy and inconstancy in categorical colour perception Type Book Whole
  Year 2012 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract To recognise objects is perhaps the most important task an autonomous system, either biological or artificial needs to perform. In the context of human vision, this is partly achieved by recognizing the colour of surfaces despite changes in the wavelength distribution of the illumination, a property called colour constancy. Correct surface colour recognition may be adequately accomplished by colour category matching without the need to match colours precisely, therefore categorical colour constancy is likely to play an important role for object identification to be successful. The main aim of this work is to study the relationship between colour constancy and categorical colour perception. Previous studies of colour constancy have shown the influence of factors such the spatio-chromatic properties of the background, individual observer's performance, semantics, etc. However there is very little systematic study of these influences. To this end, we developed a new approach to colour constancy which includes both individual observers' categorical perception, the categorical structure of the background, and their interrelations resulting in a more comprehensive characterization of the phenomenon. In our study, we first developed a new method to analyse the categorical structure of 3D colour space, which allowed us to characterize individual categorical colour perception as well as quantify inter-individual variations in terms of shape and centroid location of 3D categorical regions. Second, we developed a new colour constancy paradigm, termed chromatic setting, which allows measuring the precise location of nine categorically-relevant points in colour space under immersive illumination. Additionally, we derived from these measurements a new colour constancy index which takes into account the magnitude and orientation of the chromatic shift, memory effects and the interrelations among colours and a model of colour naming tuned to each observer/adaptation state. Our results lead to the following conclusions: (1) There exists large inter-individual variations in the categorical structure of colour space, and thus colour naming ability varies significantly but this is not well predicted by low-level chromatic discrimination ability; (2) Analysis of the average colour naming space suggested the need for an additional three basic colour terms (turquoise, lilac and lime) for optimal colour communication; (3) Chromatic setting improved the precision of more complex linear colour constancy models and suggested that mechanisms other than cone gain might be best suited to explain colour constancy; (4) The categorical structure of colour space is broadly stable under illuminant changes for categorically balanced backgrounds; (5) Categorical inconstancy exists for categorically unbalanced backgrounds thus indicating that categorical information perceived in the initial stages of adaptation may constrain further categorical perception.  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Place of Publication Editor (down) Maria Vanrell;C. Alejandro Parraga  
  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 @ Roc2012 Serial 2893  
Permanent link to this record
 
 
Author Robert Benavente edit  openurl
  Title A Parametric Model for Computational Colour Naming Type Book Whole
  Year 2007 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords PhD Thesis  
  Abstract  
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor (down) Maria Vanrell  
  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 @ Ben2007 Serial 1108  
Permanent link to this record
 
 
Author Ivet Rafegas edit  isbn
openurl 
  Title Color in Visual Recognition: from flat to deep representations and some biological parallelisms Type Book Whole
  Year 2017 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.

Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations.

Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias.		  
  Address November 2017  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor (down) Maria Vanrell  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-945373-7-0 Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ Raf2017 Serial 3100  
Permanent link to this record
 
 
Author Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez edit   pdf
doi  isbn
openurl 
  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 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 (down) 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 Cristhian Aguilera; M.Ramos; Angel Sappa edit   pdf
doi  isbn
openurl 
  Title Simulated Annealing: A Novel Application of Image Processing in the Wood Area Type Book Chapter
  Year 2012 Publication Simulated Annealing – Advances, Applications and Hybridizations Abbreviated Journal  
  Volume Issue Pages 91-104  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor (down) Marcos de Sales Guerra Tsuzuki  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-953-51-0710-1 Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ ARS2012 Serial 2156  
Permanent link to this record
 
 
Author David Aldavert edit  isbn
openurl 
  Title Efficient and Scalable Handwritten Word Spotting on Historical Documents using Bag of Visual Words Type Book Whole
  Year 2021 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Word spotting can be defined as the pattern recognition tasked aimed at locating and retrieving a specific keyword within a document image collection without explicitly transcribing the whole corpus. Its use is particularly interesting when applied in scenarios where Optical Character Recognition performs poorly or can not be used at all. This thesis focuses on such a scenario, word spotting on historical handwritten documents that have been written by a single author or by multiple authors with a similar calligraphy.
This problem requires a visual signature that is robust to image artifacts, flexible to accommodate script variations and efficient to retrieve information in a rapid manner. For this, we have developed a set of word spotting methods that on their foundation use the well known Bag-of-Visual-Words (BoVW) representation. This representation has gained popularity among the document image analysis community to characterize handwritten words
in an unsupervised manner. However, most approaches on this field rely on a basic BoVW configuration and disregard complex encoding and spatial representations. We determine which BoVW configurations provide the best performance boost to a spotting system.
Then, we extend the segmentation-based word spotting, where word candidates are given a priori, to segmentation-free spotting. The proposed approach seeds the document images with overlapping word location candidates and characterizes them with a BoVW signature. Retrieval is achieved comparing the query and candidate signatures and returning the locations that provide a higher consensus. This is a simple but powerful approach that requires a more compact signature than in a segmentation-based scenario. We first
project the BoVW signature into a reduced semantic topics space and then compress it further using Product Quantizers. The resulting signature only requires a few dozen bytes, allowing us to index thousands of pages on a common desktop computer. The final system still yields a performance comparable to the state-of-the-art despite all the information loss during the compression phases.
Afterwards, we also study how to combine different modalities of information in order to create a query-by-X spotting system where, words are indexed using an information modality and queries are retrieved using another. We consider three different information modalities: visual, textual and audio. Our proposal is to create a latent feature space where features which are semantically related are projected onto the same topics. Creating thus a new feature space where information from different modalities can be compared. Later, we consider the codebook generation and descriptor encoding problem. The codebooks used to encode the BoVW signatures are usually created using an unsupervised clustering algorithm and, they require to test multiple parameters to determine which configuration is best for a certain document collection. We propose a semantic clustering algorithm which allows to estimate the best parameter from data. Since gather annotated data is costly, we use synthetically generated word images. The resulting codebook is database agnostic, i. e. a codebook that yields a good performance on document collections that use the same script. We also propose the use of an additional codebook to approximate descriptors and reduce the descriptor encoding
complexity to sub-linear.
Finally, we focus on the problem of signatures dimensionality. We propose a new symbol probability signature where each bin represents the probability that a certain symbol is present a certain location of the word image. This signature is extremely compact and combined with compression techniques can represent word images with just a few bytes per signature.		  
  Address April 2021  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor (down) Marçal Rusiñol;Josep Llados  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-84-122714-5-4 Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ Ald2021 Serial 3601  
Permanent link to this record
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