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Author | Angel Sappa (ed) | ||||
Title | Computer Graphics and Imaging | Type | Book Whole | ||
Year | 2010 | Publication | Computer Graphics and Imaging | Abbreviated Journal | |
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Publisher | Place of Publication | Editor | Angel Sappa | ||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978–0–88986–836–6 | Medium | ||
Area | Expedition | Conference | CGIM | ||
Notes | ADAS | Approved | no | ||
Call Number | ADAS @ adas @ Sap2010 | Serial | 1468 | ||
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Author | David Augusto Rojas; Joost Van de Weijer; Theo Gevers | ||||
Title | Color Edge Saliency Boosting using Natural Image Statistics | Type | Conference Article | ||
Year | 2010 | Publication | 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science | Abbreviated Journal | |
Volume | Issue | Pages | 228–234 | ||
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Abstract | State of the art methods for image matching, content-based retrieval and recognition use local features. Most of these still exploit only the luminance information for detection. The color saliency boosting algorithm has provided an efficient method to exploit the saliency of color edges based on information theory. However, during the design of this algorithm, some issues were not addressed in depth: (1) The method has ignored the underlying distribution of derivatives in natural images. (2) The dependence of information content in color-boosted edges on its spatial derivatives has not been quantitatively established. (3) To evaluate luminance and color contributions to saliency of edges, a parameter gradually balancing both contributions is required.
We introduce a novel algorithm, based on the principles of independent component analysis, which models the first order derivatives of color natural images by a generalized Gaussian distribution. Furthermore, using this probability model we show that for images with a Laplacian distribution, which is a particular case of generalized Gaussian distribution, the magnitudes of color-boosted edges reflect their corresponding information content. In order to evaluate the impact of color edge saliency in real world applications, we introduce an extension of the Laplacian-of-Gaussian detector to color, and the performance for image matching is evaluated. Our experiments show that our approach provides more discriminative regions in comparison with the original detector. |
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Address | Joensuu, Finland | ||||
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 | 9781617388897 | Medium | ||
Area | Expedition | Conference | CGIV/MCS | ||
Notes | ISE | Approved | no | ||
Call Number | CAT @ cat @ RWG2010 | Serial | 1306 | ||
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Author | Jaime Moreno; Xavier Otazu; Maria Vanrell | ||||
Title | Local Perceptual Weighting in JPEG2000 for Color Images | Type | Conference Article | ||
Year | 2010 | Publication | 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science | Abbreviated Journal | |
Volume | Issue | Pages | 255–260 | ||
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Abstract | The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM (Chromatic Induction Wavelet Model). | ||||
Address | Joensuu, Finland | ||||
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 | 9781617388897 | Medium | ||
Area | Expedition | Conference | CGIV/MCS | ||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ MOV2010a | Serial | 1307 | ||
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Author | C. Alejandro Parraga; Ramon Baldrich; Maria Vanrell | ||||
Title | Accurate Mapping of Natural Scenes Radiance to Cone Activation Space: A New Image Dataset | Type | Conference Article | ||
Year | 2010 | Publication | 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science | Abbreviated Journal | |
Volume | Issue | Pages | 50–57 | ||
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Abstract | The characterization of trichromatic cameras is usually done in terms of a device-independent color space, such as the CIE 1931 XYZ space. This is indeed convenient since it allows the testing of results against colorimetric measures. We have characterized our camera to represent human cone activation by mapping the camera sensor's (RGB) responses to human (LMS) through a polynomial transformation, which can be “customized” according to the types of scenes we want to represent. Here we present a method to test the accuracy of the camera measures and a study on how the choice of training reflectances for the polynomial may alter the results. | ||||
Address | Joensuu, Finland | ||||
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 | 9781617388897 | Medium | ||
Area | Expedition | Conference | CGIV/MCS | ||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ PBV2010a | Serial | 1322 | ||
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Author | Javier Vazquez; G. D. Finlayson; Maria Vanrell | ||||
Title | A compact singularity function to predict WCS data and unique hues | Type | Conference Article | ||
Year | 2010 | Publication | 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science | Abbreviated Journal | |
Volume | Issue | Pages | 33–38 | ||
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Abstract | Understanding how colour is used by the human vision system is a widely studied research field. The field, though quite advanced, still faces important unanswered questions. One of them is the explanation of the unique hues and the assignment of color names. This problem addresses the fact of different perceptual status for different colors.
Recently, Philipona and O'Regan have proposed a biological model that allows to extract the reflection properties of any surface independently of the lighting conditions. These invariant properties are the basis to compute a singularity index that predicts the asymmetries presented in unique hues and basic color categories psychophysical data, therefore is giving a further step in their explanation. In this paper we build on their formulation and propose a new singularity index. This new formulation equally accounts for the location of the 4 peaks of the World colour survey and has two main advantages. First, it is a simple elegant numerical measure (the Philipona measurement is a rather cumbersome formula). Second, we develop a colour-based explanation for the measure. |
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Address | Joensuu, Finland | ||||
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 | 9781617388897 | Medium | ||
Area | Expedition | Conference | CGIV/MCS | ||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ VFV2010 | Serial | 1324 | ||
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Author | David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
Title | Real-time Object Segmentation using a Bag of Features Approach | Type | Conference Article | ||
Year | 2010 | Publication | 13th International Conference of the Catalan Association for Artificial Intelligence | Abbreviated Journal | |
Volume | 220 | Issue | Pages | 321–329 | |
Keywords | Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors | ||||
Abstract | In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. | ||||
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Publisher | IOS Press Amsterdam, | Place of Publication | Editor | In R.Alquezar, A.Moreno, J.Aguilar. | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 9781607506423 | Medium | ||
Area | Expedition | Conference | CCIA | ||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ ARL2010b | Serial | 1417 | ||
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Author | N. Serrano; L. Tarazon; D. Perez; Oriol Ramos Terrades; S. Juan | ||||
Title | The GIDOC Prototype | Type | Conference Article | ||
Year | 2010 | Publication | 10th International Workshop on Pattern Recognition in Information Systems | Abbreviated Journal | |
Volume | Issue | Pages | 82-89 | ||
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Abstract | Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.
A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription. GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions. |
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Address | Funchal, Portugal | ||||
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 | 978-989-8425-14-0 | Medium | ||
Area | Expedition | Conference | PRIS | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ STP2010 | Serial | 1868 | ||
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Author | Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel | ||||
Title | Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures | Type | Book Chapter | ||
Year | 2010 | Publication | Bayesian Network | Abbreviated Journal | |
Volume | Issue | Pages | 13-37 | ||
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Publisher | Sciyo | Place of Publication | Editor | Ahmed Rebai | |
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-953-307-124-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ BDL2010 | Serial | 1461 | ||
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Author | Robert Benavente; C. Alejandro Parraga; Maria Vanrell | ||||
Title | La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas | Type | Conference Article | ||
Year | 2010 | Publication | 9th Congreso Nacional del Color | Abbreviated Journal | |
Volume | Issue | Pages | 92–95 | ||
Keywords | Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos | ||||
Abstract | En este artículo presentamos los resultados de un experimento de categorización de color en el que las muestras se presentaron sobre un fondo multicolor (Mondrian) para simular los efectos del contexto. Los resultados se comparan con los de un experimento previo que, utilizando un paradigma diferente, determinó las fronteras sin tener en cuenta el contexto. El análisis de los resultados muestra que las fronteras obtenidas con el experimento en contexto presentan menos confusión que las obtenidas en el experimento sin contexto. | ||||
Address | Alicante (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 | 978-84-9717-144-1 | Medium | ||
Area | Expedition | Conference | CNC | ||
Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ BPV2010 | Serial | 1327 | ||
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Author | Ignasi Rius | ||||
Title | Motion Priors for Efficient Bayesian Tracking in Human Sequence Evaluation | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Recovering human motion by visual analysis is a challenging computer vision research
area with a lot of potential applications. Model-based tracking approaches, and in particular particle lters, formulate the problem as a Bayesian inference task whose aim is to sequentially estimate the distribution of the parameters of a human body model over time. These approaches strongly rely on good dynamical and observation models to predict and update congurations of the human body according to measurements from the image data. However, it is very dicult to design observation models which extract useful and reliable information from image sequences robustly. This results specially challenging in monocular tracking given that only one viewpoint from the scene is available. Therefore, to overcome these limitations strong motion priors are needed to guide the exploration of the state space. The work presented in this Thesis is aimed to retrieve the 3D motion parameters of a human body model from incomplete and noisy measurements of a monocular image sequence. These measurements consist of the 2D positions of a reduced set of joints in the image plane. Towards this end, we present a novel action-specic model of human motion which is trained from several databases of real motion-captured performances of an action, and is used as a priori knowledge within a particle ltering scheme. Body postures are represented by means of a simple and compact stick gure model which uses direction cosines to represent the direction of body limbs in the 3D Cartesian space. Then, for a given action, Principal Component Analysis is applied to the training data to perform dimensionality reduction over the highly correlated input data. Before the learning stage of the action model, the input motion performances are synchronized by means of a novel dense matching algorithm based on Dynamic Programming. The algorithm synchronizes all the motion sequences of the same action class, nding an optimal solution in real-time. Then, a probabilistic action model is learnt, based on the synchronized motion examples, which captures the variability and temporal evolution of full-body motion within a specic action. In particular, for each action, the parameters learnt are: a representative manifold for the action consisting of its mean performance, the standard deviation from the mean performance, the mean observed direction vectors from each motion subsequence of a given length and the expected error at a given time instant. Subsequently, the action-specic model is used as a priori knowledge on human motion which improves the eciency and robustness of the overall particle filtering tracking framework. First, the dynamic model guides the particles according to similar situations previously learnt. Then, the state space is constrained so only feasible human postures are accepted as valid solutions at each time step. As a result, the state space is explored more eciently as the particle set covers the most probable body postures. Finally, experiments are carried out using test sequences from several motion databases. Results point out that our tracker scheme is able to estimate the rough 3D conguration of a full-body model providing only the 2D positions of a reduced set of joints. Separate tests on the sequence synchronization method and the subsequence probabilistic matching technique are also provided. |
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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 | 978-84-937261-9-5 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ Riu2010 | Serial | 1331 | ||
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Author | Jose Manuel Alvarez | ||||
Title | Combining Context and Appearance for Road Detection | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Road traffic crashes have become a major cause of death and injury throughout the world.
Hence, in order to improve road safety, the automobile manufacture is moving towards the development of vehicles with autonomous functionalities such as keeping in the right lane, safe distance keeping between vehicles or regulating the speed of the vehicle according to the traffic conditions. A key component of these systems is vision–based road detection that aims to detect the free road surface ahead the moving vehicle. Detecting the road using a monocular vision system is very challenging since the road is an outdoor scenario imaged from a mobile platform. Hence, the detection algorithm must be able to deal with continuously changing imaging conditions such as the presence ofdifferent objects (vehicles, pedestrians), different environments (urban, highways, off–road), different road types (shape, color), and different imaging conditions (varying illumination, different viewpoints and changing weather conditions). Therefore, in this thesis, we focus on vision–based road detection using a single color camera. More precisely, we first focus on analyzing and grouping pixels according to their low–level properties. In this way, two different approaches are presented to exploit color and photometric invariance. Then, we focus the research of the thesis on exploiting context information. This information provides relevant knowledge about the road not using pixel features from road regions but semantic information from the analysis of the scene. In this way, we present two different approaches to infer the geometry of the road ahead the moving vehicle. Finally, we focus on combining these context and appearance (color) approaches to improve the overall performance of road detection algorithms. The qualitative and quantitative results presented in this thesis on real–world driving sequences show that the proposed method is robust to varying imaging conditions, road types and scenarios going beyond the state–of–the–art. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Theo Gevers | |
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-937261-8-8 | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ Alv2010 | Serial | 1454 | ||
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Author | Partha Pratim Roy | ||||
Title | Multi-Oriented and Multi-Scaled Text Character Analysis and Recognition in Graphical Documents and their Applications to Document Image Retrieval | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | With the advent research of Document Image Analysis and Recognition (DIAR), an
important line of research is explored on indexing and retrieval of graphics rich documents. It aims at finding relevant documents relying on segmentation and recognition of text and graphics components underlying in non-standard layout where commercial OCRs can not be applied due to complexity. This thesis is focused towards text information extraction approaches in graphical documents and retrieval of such documents using text information. Automatic text recognition in graphical documents (map, engineering drawing, etc.) involves many challenges because text characters are usually printed in multioriented and multi-scale way along with different graphical objects. Text characters are used to annotate the graphical curve lines and hence, many times they follow curvi-linear paths too. For OCR of such documents, individual text lines and their corresponding words/characters need to be extracted. For recognition of multi-font, multi-scale and multi-oriented characters, we have proposed a feature descriptor for character shape using angular information from contour pixels to take care of the invariance nature. To improve the efficiency of OCR, an approach towards the segmentation of multi-oriented touching strings into individual characters is also discussed. Convex hull based background information is used to segment a touching string into possible primitive segments and later these primitive segments are merged to get optimum segmentation using dynamic programming. To overcome the touching/overlapping problem of text with graphical lines, a character spotting approach using SIFT and skeleton information is included. Afterwards, we propose a novel method to extract individual curvi-linear text lines using the foreground and background information of the characters of the text and a water reservoir concept is used to utilize the background information. We have also formulated the methodologies for graphical document retrieval applications using query words and seals. The retrieval approaches are performed using recognition results of individual components in the document. Given a query text, the system extracts positional knowledge from the query word and uses the same to generate hypothetical locations in the document. Indexing of documents is also performed based on automatic detection of seals from documents containing cluttered background. A seal is characterized by scale and rotation invariant spatial feature descriptors computed from labelled text characters and a concept based on the Generalized Hough Transform is used to locate the seal in documents. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Josep Llados;Umapada Pal | |
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-937261-7-1 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ Roy2010 | Serial | 1455 | ||
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Author | Joan Mas | ||||
Title | A Syntactic Pattern Recognition Approach based on a Distribution Tolerant Adjacency Grammar and a Spatial Indexed Parser. Application to Sketched Document Recognition | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Sketch recognition is a discipline which has gained an increasing interest in the last
20 years. This is due to the appearance of new devices such as PDA, Tablet PC’s or digital pen & paper protocols. From the wide range of sketched documents we focus on those that represent structured documents such as: architectural floor-plans, engineering drawing, UML diagrams, etc. To recognize and understand these kinds of documents, first we have to recognize the different compounding symbols and then we have to identify the relations between these elements. From the way that a sketch is captured, there are two categories: on-line and off-line. On-line input modes refer to draw directly on a PDA or a Tablet PC’s while off-line input modes refer to scan a previously drawn sketch. This thesis is an overlapping of three different areas on Computer Science: Pattern Recognition, Document Analysis and Human-Computer Interaction. The aim of this thesis is to interpret sketched documents independently on whether they are captured on-line or off-line. For this reason, the proposed approach should contain the following features. First, as we are working with sketches the elements present in our input contain distortions. Second, as we would work in on-line or off-line input modes, the order in the input of the primitives is indifferent. Finally, the proposed method should be applied in real scenarios, its response time must be slow. To interpret a sketched document we propose a syntactic approach. A syntactic approach is composed of two correlated components: a grammar and a parser. The grammar allows describing the different elements on the document as well as their relations. The parser, given a document checks whether it belongs to the language generated by the grammar or not. Thus, the grammar should be able to cope with the distortions appearing on the instances of the elements. Moreover, it would be necessary to define a symbol independently of the order of their primitives. Concerning to the parser when analyzing 2D sentences, it does not assume an order in the primitives. Then, at each new primitive in the input, the parser searches among the previous analyzed symbols candidates to produce a valid reduction. Taking into account these features, we have proposed a grammar based on Adjacency Grammars. This kind of grammars defines their productions as a multiset of symbols rather than a list. This allows describing a symbol without an order in their components. To cope with distortion we have proposed a distortion model. This distortion model is an attributed estimated over the constraints of the grammar and passed through the productions. This measure gives an idea on how far is the symbol from its ideal model. In addition to the distortion on the constraints other distortions appear when working with sketches. These distortions are: overtracing, overlapping, gaps or spurious strokes. Some grammatical productions have been defined to cope with these errors. Concerning the recognition, we have proposed an incremental parser with an indexation mechanism. Incremental parsers analyze the input symbol by symbol given a response to the user when a primitive is analyzed. This makes incremental parser suitable to work in on-line as well as off-line input modes. The parser has been adapted with an indexation mechanism based on a spatial division. This indexation mechanism allows setting the primitives in the space and reducing the search to a neighbourhood. A third contribution is a grammatical inference algorithm. This method given a set of symbols captures the production describing it. In the field of formal languages, different approaches has been proposed but in the graphical domain not so much work is done in this field. The proposed method is able to capture the production from a set of symbol although they are drawn in different order. A matching step based on the Haussdorff distance and the Hungarian method has been proposed to match the primitives of the different symbols. In addition the proposed approach is able to capture the variability in the parameters of the constraints. From the experimental results, we may conclude that we have proposed a robust approach to describe and recognize sketches. Moreover, the addition of new symbols to the alphabet is not restricted to an expert. Finally, the proposed approach has been used in two real scenarios obtaining a good performance. |
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Gemma Sanchez;Josep Llados | |
Language | Summary Language | Original Title | |||
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Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-937261-4-0 | Medium | ||
Area | Expedition | Conference | |||
Notes | DAG | Approved | no | ||
Call Number | DAG @ dag @ Mas2010 | Serial | 1334 | ||
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Author | Ivan Huerta | ||||
Title | Foreground Object Segmentation and Shadow Detection for Video Sequences in Uncontrolled Environments | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | This Thesis is mainly divided in two parts. The first one presents a study of motion
segmentation problems. Based on this study, a novel algorithm for mobile-object segmentation from a static background scene is also presented. This approach is demonstrated robust and accurate under most of the common problems in motion segmentation. The second one tackles the problem of shadows in depth. Firstly, a bottom-up approach based on a chromatic shadow detector is presented to deal with umbra shadows. Secondly, a top-down approach based on a tracking system has been developed in order to enhance the chromatic shadow detection. In our first contribution, a case analysis of motion segmentation problems is presented by taking into account the problems associated with different cues, namely colour, edge and intensity. Our second contribution is a hybrid architecture which handles the main problems observed in such a case analysis, by fusing (i) the knowledge from these three cues and (ii) a temporal difference algorithm. On the one hand, we enhance the colour and edge models to solve both global/local illumination changes (shadows and highlights) and camouflage in intensity. In addition, local information is exploited to cope with a very challenging problem such as the camouflage in chroma. On the other hand, the intensity cue is also applied when colour and edge cues are not available, such as when beyond the dynamic range. Additionally, temporal difference is included to segment motion when these three cues are not available, such as that background not visible during the training period. Lastly, the approach is enhanced for allowing ghost detection. As a result, our approach obtains very accurate and robust motion segmentation in both indoor and outdoor scenarios, as quantitatively and qualitatively demonstrated in the experimental results, by comparing our approach with most best-known state-of-the-art approaches. Motion Segmentation has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. Firstly, a bottom-up approach for detection and removal of chromatic moving shadows in surveillance scenarios is proposed. Secondly, a top-down approach based on kalman filters to detect and track shadows has been developed in order to enhance the chromatic shadow detection. In the Bottom-up part, the shadow detection approach applies a novel technique based on gradient and colour models for separating chromatic moving shadows from moving objects. Well-known colour and gradient models are extended and improved into an invariant colour cone model and an invariant gradient model, respectively, to perform automatic segmentation while detecting potential shadows. Hereafter, the regions corresponding to potential shadows are grouped by considering ”a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between local gradient structures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. In the top-down process, after detection of objects and shadows both are tracked using Kalman filters, in order to enhance the chromatic shadow detection, when it fails to detect a shadow. Firstly, this implies a data association between the blobs (foreground and shadow) and Kalman filters. Secondly, an event analysis of the different data association cases is performed, and occlusion handling is managed by a Probabilistic Appearance Model (PAM). Based on this association, temporal consistency is looked for the association between foregrounds and shadows and their respective Kalman Filters. From this association several cases are studied, as a result lost chromatic shadows are correctly detected. Finally, the tracking results are used as feedback to improve the shadow and object detection. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions. |
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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 | 978-84-937261-3-3 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | ISE @ ise @ Hue2010 | Serial | 1332 | ||
Permanent link to this record | |||||
Author | Carles Fernandez | ||||
Title | Understanding Image Sequences: the Role of Ontologies in Cognitive Vision | Type | Book Whole | ||
Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | The increasing ubiquitousness of digital information in our daily lives has positioned
video as a favored information vehicle, and given rise to an astonishing generation of social media and surveillance footage. This raises a series of technological demands for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research community to guide its steps towards a better attainment of such capabilities. As a result, current trends on cognitive vision promise to recognize complex events and self-adapt to different environments, while managing and integrating several types of knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users. In this thesis we tackle the problem of recognizing and describing meaningful events in video sequences from different domains, and communicating the resulting knowledge to end-users by means of advanced interfaces for human–computer interaction. This problem is addressed by designing the high-level modules of a cognitive vision framework exploiting ontological knowledge. Ontologies allow us to define the relevant concepts in a domain and the relationships among them; we prove that the use of ontologies to organize, centralize, link, and reuse different types of knowledge is a key factor in the materialization of our objectives. The proposed framework contributes to: (i) automatically learn the characteristics of different scenarios in a domain; (ii) reason about uncertain, incomplete, or vague information from visual –camera’s– or linguistic –end-user’s– inputs; (iii) derive plausible interpretations of complex events from basic spatiotemporal developments; (iv) facilitate natural interfaces that adapt to the needs of end-users, and allow them to communicate efficiently with the system at different levels of interaction; and finally, (v) find mechanisms to guide modeling processes, maintain and extend the resulting models, and to exploit multimodal resources synergically to enhance the former tasks. We describe a holistic methodology to achieve these goals. First, the use of prior taxonomical knowledge is proved useful to guide MAP-MRF inference processes in the automatic identification of semantic regions, with independence of a particular scenario. Towards the recognition of complex video events, we combine fuzzy metric-temporal reasoning with SGTs, thus assessing high-level interpretations from spatiotemporal data. Here, ontological resources like T–Boxes, onomasticons, or factual databases become useful to derive video indexing and retrieval capabilities, and also to forward highlighted content to smart user interfaces. There, we explore the application of ontologies to discourse analysis and cognitive linguistic principles, or scene augmentation techniques towards advanced communication by means of natural language dialogs and synthetic visualizations. Ontologies become fundamental to coordinate, adapt, and reuse the different modules in the system. The suitability of our ontological framework is demonstrated by a series of applications that especially benefit the field of smart video surveillance, viz. automatic generation of linguistic reports about the content of video sequences in multiple natural languages; content-based filtering and summarization of these reports; dialogue-based interfaces to query and browse video contents; automatic learning of semantic regions in a scenario; and tools to evaluate the performance of components and models in the system, via simulation and augmented reality. |
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Address | |||||
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 | 978-84-937261-2-6 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | Admin @ si @ Fer2010a | Serial | 1333 | ||
Permanent link to this record |