Home << 1 2 3 4 5 6 7 8 9 10 >> [11–14]
List View
 | 
Citations
 | 
Details
   web
Records
Author Josep Llados; W. Liu; Jean-Marc Ogier
Title Seventh IAPR International Workshop on Graphics Recognition GREC 2007 Type Book Whole
Year 2007 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Curitiba (Brazil)
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 (up) Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ LLO2007 Serial 835
Permanent link to this record
 
 
Author Joan Marti; Jose Miguel Benedi; Ana Maria Mendonça; Joan Serrat
Title Pattern Recognition and Image Analysis Type Book Whole
Year 2007 Publication 3rd Iberian Conference Abbreviated Journal
Volume 6669 Issue Pages 4477-4478
Keywords
Abstract
Address Girona (Spain)
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 ISBN (up) Medium
Area Expedition Conference IbPRIA
Notes ADAS Approved no
Call Number ADAS @ adas @ MBM2007 Serial 994
Permanent link to this record
 
 
Author Alfons Juan-Ciscar; Gemma Sanchez
Title PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008 Type Book Whole
Year 2008 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona (Spain)
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 (up) Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ JuS2008 Serial 1054
Permanent link to this record
 
 
Author Miquel Ferrer
Title Theory and Algorithms on the Median Graph. Application to Graph-based Classification and Clustering Type Book Whole
Year 2008 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis Ph.D. thesis
Publisher Place of Publication Editor Francesc Serratosa Casanelles;Ernest Valveny
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition 978-84-935251-7-0 Conference
Notes Approved no
Call Number Admin @ si @ Fer2008 Serial 1105
Permanent link to this record
 
 
Author Robert Benavente
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 Maria Vanrell
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes CIC Approved no
Call Number CAT @ cat @ Ben2007 Serial 1108
Permanent link to this record
 
 
Author Pau Baiget
Title Modeling Human Behavior for Image Sequence Understanding and Generation Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The comprehension of animal behavior, especially human behavior, is one of the most ancient and studied problems since the beginning of civilization. The big list of factors that interact to determine a person action require the collaboration of different disciplines, such as psichology, biology, or sociology. In the last years the analysis of human behavior has received great attention also from the computer vision community, given the latest advances in the acquisition of human motion data from image sequences.

Despite the increasing availability of that data, there still exists a gap towards obtaining a conceptual representation of the obtained observations. Human behavior analysis is based on a qualitative interpretation of the results, and therefore the assignment of concepts to quantitative data is linked to a certain ambiguity.

This Thesis tackles the problem of obtaining a proper representation of human behavior in the contexts of computer vision and animation. On the one hand, a good behavior model should permit the recognition and explanation the observed activity in image sequences. On the other hand, such a model must allow the generation of new synthetic instances, which model the behavior of virtual agents.

First, we propose methods to automatically learn the models from observations. Given a set of quantitative results output by a vision system, a normal behavior model is learnt. This results provides a tool to determine the normality or abnormality of future observations. However, machine learning methods are unable to provide a richer description of the observations. We confront this problem by means of a new method that incorporates prior knowledge about the enviornment and about the expected behaviors. This framework, formed by the reasoning engine FMTL and the modeling tool SGT allows the generation of conceptual descriptions of activity in new image sequences. Finally, we demonstrate the suitability of the proposed framework to simulate behavior of virtual agents, which are introduced into real image sequences and interact with observed real agents, thereby easing the generation of augmented reality sequences.

The set of approaches presented in this Thesis has a growing set of potential applications. The analysis and description of behavior in image sequences has its principal application in the domain of smart video--surveillance, in order to detect suspicious or dangerous behaviors. Other applications include automatic sport commentaries, elderly monitoring, road traffic analysis, and the development of semantic video search engines. Alternatively, behavioral virtual agents allow to simulate accurate real situations, such as fires or crowds. Moreover, the inclusion of virtual agents into real image sequences has been widely deployed in the games and cinema industries.
Address Bellaterra (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Xavier Roca
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ Bai2009 Serial 1210
Permanent link to this record
 
 
Author David Rotger
Title Analysis and Multi-Modal Fusion of coronary Images Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The framework of this thesis is to study in detail different techniques and tools for medical image registration in order to ease the daily life of clinical experts in cardiology. The first aim of this thesis is providing computer tools for
fusing IVUS and angiogram data is of high clinical interest to help the physicians locate in IVUS data and decide which lesion is observed, how long it is, how far from a bifurcation or another lesions stays, etc. This thesis proves and
validates that we can segment the catheter path in angiographies using geodesic snakes (based on fast marching algorithm), a three-dimensional reconstruction of the catheter inspired in stereo vision and a new technique to fuse IVUS
and angiograms that establishes exact correspondences between them. We have developed a new workstation called iFusion that has four strong advantages: registration of IVUS and angiographic images with sub-pixel precision, it works on- and off-line, it is independent on the X-ray system and there is no need of daily calibration. The second aim of the thesis is devoted to developing a computer-aided analysis of IVUS for image-guided intervention. We have designed, implemented
and validated a robust algorithm for stent extraction and reconstruction from IVUS videos. We consider a very special and recent kind of stents, bioabsorbable stents that represent a great clinical challenge due to their property to be
absorbed by time and thus avoiding the “danger” of neostenosis as one of the main problems of metallic stents. We present a new and very promising algorithm based on an optimized cascade of multiple classifiers to automatically detect individual stent struts of a very novel bioabsorbable drug eluting coronary stent. This problem represents a very challenging target given the variability in contrast, shape and grey levels of the regions to be detected, what is
denoted by the high variability between the specialists (inter-observer variability of 0.14~$\pm$0.12). The obtained results of the automatic strut detection are within the inter-observer variability.
Address Barcelona (Espanya)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Petia Radeva
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ Rot2009 Serial 1261
Permanent link to this record
 
 
Author Xavier Baro
Title Probabilistic Darwin Machines: A New Approach to Develop Evolutionary Object Detection Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Ever since computers were invented, we have wondered whether they might perform some of the human quotidian tasks. One of the most studied and still nowadays less understood problem is the capacity to learn from our experiences and how we generalize the knowledge that we acquire. One of that unaware tasks for the persons and that more interest is awakening in different scientific areas since the beginning, is the one that is known as pattern recognition. The creation of models that represent the world that surrounds us, help us for recognizing objects in our environment, to predict situations, to identify behaviors... All this information allows us to adapt ourselves and to interact with our environment. The capacity of adaptation of individuals to their environment has been related to the amount of patterns that are capable of identifying.

This thesis faces the pattern recognition problem from a Computer Vision point of view, taking one of the most paradigmatic and extended approaches to object detection as starting point. After studying this approach, two weak points are identified: The first makes reference to the description of the objects, and the second is a limitation of the learning algorithm, which hampers the utilization of best descriptors.

In order to address the learning limitations, we introduce evolutionary computation techniques to the classical object detection approach.

After testing the classical evolutionary approaches, such as genetic algorithms, we develop a new learning algorithm based on Probabilistic Darwin Machines, which better adapts to the learning problem. Once the learning limitation is avoided, we introduce a new feature set, which maintains the benefits of the classical feature set, adding the ability to describe non localities. This combination of evolutionary learning algorithm and features is tested on different public data sets, outperforming the results obtained by the classical approach.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Vitria
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes OR;HuPBA;MV Approved no
Call Number BCNPCL @ bcnpcl @ Bar2009 Serial 1262
Permanent link to this record
 
 
Author Agata Lapedriza
Title Multitask Learning Techniques for Automatic Face Classification Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Automatic face classification is currently a popular research area in Computer Vision. It involves several subproblems, such as subject recognition, gender classification or subject verification.

Current systems of automatic face classification need a large amount of training data to robustly learn a task. However, the collection of labeled data is usually a difficult issue. For this reason, the research on methods that are able to learn from a small sized training set is essential.

The dependency on the abundance of training data is not so evident in human learning processes. We are able to learn from a very small number of examples, given that we use, additionally, some prior knowledge to learn a new task. For example, we frequently find patterns and analogies from other domains to reuse them in new situations, or exploit training data from other experiences.

In computer science, Multitask Learning is a new Machine Learning approach that studies this idea of knowledge transfer among different tasks, to overcome the effects of the small sample sized problem.

This thesis explores, proposes and tests some Multitask Learning methods specially developed for face classification purposes. Moreover, it presents two more contributions dealing with the small sample sized problem, out of the Multitask Learning context. The first one is a method to extract external face features, to be used as an additional information source in automatic face classification problems. The second one is an empirical study on the most suitable face image resolution to perform automatic subject recognition.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Vitria;David Masip
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes OR;MV Approved no
Call Number BCNPCL @ bcnpcl @ Lap2009 Serial 1263
Permanent link to this record
 
 
Author Marçal Rusiñol
Title Geometric and Structural-based Symbol Spotting. Application to Focused Retrieval in Graphic Document Collections Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Usually, pattern recognition systems consist of two main parts. On the one hand, the data acquisition and, on the other hand, the classification of this data on a certain category. In order to recognize which category a certain query element belongs to, a set of pattern models must be provided beforehand. An off-line learning stage is needed to train the classifier and to offer a robust classification of the patterns. Within the pattern recognition field, we are interested in the recognition of graphics and, in particular, on the analysis of documents rich in graphical information. In this context, one of the main concerns is to see if the proposed systems remain scalable with respect to the data volume so as it can handle growing amounts of symbol models. In order to avoid to work with a database of reference symbols, symbol spotting and on-the-fly symbol recognition methods have been introduced in the past years.

Generally speaking, the symbol spotting problem can be defined as the identification of a set of regions of interest from a document image which are likely to contain an instance of a certain queriedn symbol without explicitly applying the whole pattern recognition scheme. Our application framework consists on indexing a collection of graphic-rich document images. This collection is
queried by example with a single instance of the symbol to look for and, by means of symbol spotting methods we retrieve the regions of interest where the symbol is likely to appear within the documents. This kind of applications are known as focused retrieval methods.

In order that the focused retrieval application can handle large collections of documents there is a need to provide an efficient access to the large volume of information that might be stored. We use indexing strategies in order to efficiently retrieve by similarity the locations where a certain part of the symbol appears. In that scenario, graphical patterns should be used as indices for accessing and navigating the collection of documents.
These indexing mechanism allow the user to search for similar elements using graphical information rather than textual queries.

Along this thesis we present a spotting architecture and different methods aiming to build a complete focused retrieval application dealing with a graphic-rich document collections. In addition, a protocol to evaluate the performance of symbol
spotting systems in terms of recognition abilities, location accuracy and scalability is proposed.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Josep Llados
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ Rus2009 Serial 1264
Permanent link to this record
 
 
Author Alicia Fornes
Title Writer Identification by a Combination of Graphical Features in the Framework of Old Handwritten Music Scores Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The analysis and recognition of historical document images has attracted growing interest in the last years. Mass digitization and document image understanding allows the preservation, access and indexation of this artistic, cultural and technical heritage. The analysis of handwritten documents is an outstanding subfield. The main interest is not only the transcription of the document to a standard format, but also, the identification of the author of a document from a set of writers (namely writer identification).

Writer identification in handwritten text documents is an active area of study, however, the identification of the writer of graphical documents is still a challenge. The main objective of this thesis is the identification of the writer in old music scores, as an example of graphic documents. Concerning old music scores, many historical archives contain a huge number of sheets of musical compositions without information about the composer, and the research on this field could be helpful for musicologists.

The writer identification framework proposed in this thesis combines three different writer identification approaches, which are the main scientific contributions. The first one is based on symbol recognition methods. For this purpose, two novel symbol recognition methods are proposed for coping with the typical distortions in hand-drawn symbols. The second approach preprocesses the music score for obtaining music lines, and extracts information about the slant, width of the writing, connected components, contours and fractals. Finally, the third approach extracts global information by generating texture images from the music scores and extracting textural features (such as Gabor filters and co-occurence matrices).

The high identification rates obtained in the experimental results demonstrate the suitability of the proposed ensemble architecture. To the best of our knowledge, this work is the first contribution on writer identification from images containing graphical languages.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Josep Llados;Gemma Sanchez
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes Approved no
Call Number DAG @ dag @ For2009 Serial 1265
Permanent link to this record
 
 
Author Jose Antonio Rodriguez
Title Statistical frameworks and prior information modeling in handwritten word-spotting Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Handwritten word-spotting (HWS) is the pattern analysis task that consists in finding keywords in handwritten document images. So far, HWS has been applied mostly to historical documents in order to build search engines for such image collections. This thesis addresses the problem of word-spotting for detecting important keywords in business documents. This is a first step towards the process of automatic routing of correspondence based on content.

However, the application of traditional HWS techniques fails for this type of documents. As opposed to historical documents, real business documents present a very high variability in terms of writing styles, spontaneous writing, crossed-out words, spelling mistakes, etc. The main goal of this thesis is the development of pattern recognition techniques that lead to a high-performance HWS system for this challenging type of data.

We develop a statistical framework in which word models are expressed in terms of hidden Markov models and the a priori information is encoded in a universal vocabulary of Gaussian codewords. This systems leads to a very robust performance in word-spotting task. We also find that by constraining the word models to the universal vocabulary, the a priori information of the problem of interest can be exploited for developing new contributions. These include a novel writer adaptation method, a system for searching handwritten words by generating typed text images, and a novel model-based similarity between feature vector sequences.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Gemma Sanchez;Josep Llados;Florent Perronnin
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ Rod2009 Serial 1266
Permanent link to this record
 
 
Author Agnes Borras
Title Contributions to the Content-Based Image Retrieval Using Pictorial Queries Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The broad access to digital cameras, personal computers and Internet, has lead to the generation of large volumes of data in digital form. If we want an effective usage of this huge amount of data, we need automatic tools to allow the retrieval of relevant information. Image data is a particular type of information that requires specific techniques of description and indexing. The computer vision field that studies these kind of techniques is called Content-Based Image Retrieval (CBIR). Instead of using text-based descriptions, a system of CBIR deals on properties that are inherent in the images themselves. Hence, the feature-based description provides a universal via of image expression in contrast with the more than 6000 languages spoken in the world.
Nowadays, the CBIR is a dynamic focus of research that has derived in important applications for many professional groups. The potential fields of application can be such diverse as: the medical domain, the crime prevention, the protection of the intel- lectual property, the journalism, the graphic design, the web search, the preservation of cultural heritage, etc.
The definition on the role of the user is a key point in the development of a CBIR application. The user is in charge to formulate the queries from which the images are retrieved. We have centered our attention on the image retrieval techniques that use queries based on pictorial information. We have identified a taxonomy composed by four main query paradigms: query-by-selection, query-by-iconic-composition, query- by-sketch and query-by-paint. Each one of these paradigms allows a different degree of user expressivity. From a simple image selection, to a complete painting of the query, the user takes control of the input in the CBIR system.
Along the chapters of this thesis we have analyzed the influence that each query paradigm imposes in the internal operations of a CBIR system. Moreover, we have proposed a set of contributions that we have exemplified in the context of a final application.
Address Barcelona (Spain)
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Bellaterra Editor Josep Llados
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes DAG; Approved no
Call Number DAG @ dag @ Bor2009; IAM @ iam @ Bor2009 Serial 1269
Permanent link to this record
 
 
Author Jaume Garcia
Title Statistical Models of the Architecture and Function of the Left Ventricle Type Book Whole
Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Cardiovascular Diseases, specially those affecting the Left Ventricle (LV), are the leading cause of death in developed countries with approximately a 30% of all global deaths. In order to address this public health concern, physicians focus on diagnosis and therapy planning. On one hand, early and accurate detection of Regional Wall Motion Abnormalities (RWMA) significantly contributes to a quick diagnosis and prevents the patient to reach more severe stages. On the other hand, a thouroughly knowledge of the normal gross anatomy of the LV, as well as, the distribution of its muscular fibers is crucial for designing specific interventions and therapies (such as pacemaker implanction). Statistical models obtained from the analysis of different imaging modalities allow the computation of the normal ranges of variation within a given population. Normality models are a valuable tool for the definition of objective criterions quantifying the degree of (anomalous) deviation of the LV function and anatomy for a given subject. The creation of statistical models involve addressing three main issues: extraction of data from images, definition of a common domain for comparison of data across patients and designing appropriate statistical analysis schemes. In this PhD thesis we present generic image processing tools for the creation of statistical models of the LV anatomy and function. On one hand, we use differential geometry concepts to define a computational framework (the Normalized Parametric Domain, NPD) suitable for the comparison and fusion of several clinical scores obtained over the LV. On the other hand, we present a variational approach (the Harmonic Phase Flow, HPF) for the estimation of myocardial motion that provides dense and continuous vector fields without overestimating motion at injured areas. These tools are used for the creation of statistical models. Regarding anatomy, we obtain an atlas jointly modelling, both, LV gross anatomy and fiber architecture. Regarding function, we compute normality patterns of scores characterizing the (global and local) LV function and explore, for the first time, the configuration of local scores better suited for RWMA detection.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Debora Gil
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes IAM Approved no
Call Number IAM @ iam @ Gar2009a Serial 1499
Permanent link to this record
 
 
Author Javier Vazquez
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 Maria Vanrell;Graham D. Finlayson
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN (up) Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ Vaz2011a Serial 1785
Permanent link to this record