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	Author	Partha Pratim Roy; Umapada Pal; Josep Llados
	Title	Touching Text Character Localization in Graphical Documents using SIFT			Type	Book Chapter
	Year	2010	Publication	Graphics Recognition. Achievements, Challenges, and Evolution. 8th International Workshop, GREC 2009. Selected Papers	Abbreviated Journal
	Volume	6020	Issue		Pages	199-211
	Keywords	Support Vector Machine; Text Component; Graphical Line; Document Image; Scale Invariant Feature Transform
	Abstract	Interpretation of graphical document images is a challenging task as it requires proper understanding of text/graphics symbols present in such documents. Difficulties arise in graphical document recognition when text and symbol overlapped/touched. Intersection of text and symbols with graphical lines and curves occur frequently in graphical documents and hence separation of such symbols is very difficult. Several pattern recognition and classification techniques exist to recognize isolated text/symbol. But, the touching/overlapping text and symbol recognition has not yet been dealt successfully. An interesting technique, Scale Invariant Feature Transform (SIFT), originally devised for object recognition can take care of overlapping problems. Even if SIFT features have emerged as a very powerful object descriptors, their employment in graphical documents context has not been investigated much. In this paper we present the adaptation of the SIFT approach in the context of text character localization (spotting) in graphical documents. We evaluate the applicability of this technique in such documents and discuss the scope of improvement by combining some state-of-the-art approaches.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-13727-3	Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ RPL2010c			Serial	2408
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	Author	Mirko Arnold; Anarta Ghosh; Stephen Ameling; G Lacey
	Title	Automatic segmentation and inpainting of specular highlights for endoscopic imaging			Type	Journal Article
	Year	2010	Publication	EURASIP Journal on Image and Video Processing	Abbreviated Journal	EURASIP JIVP
	Volume	2010	Issue	9	Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	800	Expedition		Conference
	Notes	MV			Approved	no
	Call Number	fernando @ fernando @			Serial	2423
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	Author	Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu
	Title	3D Texton Spaces for color-texture retrieval			Type	Conference Article
	Year	2010	Publication	7th International Conference on Image Analysis and Recognition	Abbreviated Journal
	Volume	6111	Issue		Pages	354–363
	Keywords
	Abstract	Color and texture are visual cues of different nature, their integration in an useful visual descriptor is not an easy problem. One way to combine both features is to compute spatial texture descriptors independently on each color channel. Another way is to do the integration at the descriptor level. In this case the problem of normalizing both cues arises. In this paper we solve the latest problem by fusing color and texture through distances in texton spaces. Textons are the attributes of image blobs and they are responsible for texture discrimination as defined in Julesz’s Texton theory. We describe them in two low-dimensional and uniform spaces, namely, shape and color. The dissimilarity between color texture images is computed by combining the distances in these two spaces. Following this approach, we propose our TCD descriptor which outperforms current state of art methods in the two different approaches mentioned above, early combination with LBP and late combination with MPEG-7. This is done on an image retrieval experiment over a highly diverse texture dataset from Corel.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	A.C. Campilho and M.S. Kamel
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-13771-6	Medium
	Area		Expedition		Conference	ICIAR
	Notes	CIC			Approved	no
	Call Number	CAT @ cat @ ASV2010a			Serial	1325
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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
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	Sciyo	Place of Publication		Editor	Ahmed Rebai
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	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	Angel Sappa (ed)
	Title	Computer Graphics and Imaging			Type	Book Whole
	Year	2010	Publication	Computer Graphics and Imaging	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Angel Sappa
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	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 Geronimo
	Title	A Global Approach to Vision-Based Pedestrian Detection for Advanced Driver Assistance Systems			Type	Book Whole
	Year	2010	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	At the beginning of the 21th century, traffic accidents have become a major problem not only for developed countries but also for emerging ones. As in other scientific areas in which Artificial Intelligence is becoming a key actor, advanced driver assistance systems, and concretely pedestrian protection systems based on Computer Vision, are becoming a strong topic of research aimed at improving the safety of pedestrians. However, the challenge is of considerable complexity due to the varying appearance of humans (e.g., clothes, size, aspect ratio, shape, etc.), the dynamic nature of on-board systems and the unstructured moving environments that urban scenarios represent. In addition, the required performance is demanding both in terms of computational time and detection rates. In this thesis, instead of focusing on improving specific tasks as it is frequent in the literature, we present a global approach to the problem. Such a global overview starts by the proposal of a generic architecture to be used as a framework both to review the literature and to organize the studied techniques along the thesis. We then focus the research on tasks such as foreground segmentation, object classification and refinement following a general viewpoint and exploring aspects that are not usually analyzed. In order to perform the experiments, we also present a novel pedestrian dataset that consists of three subsets, each one addressed to the evaluation of a different specific task in the system. The results presented in this thesis not only end with a proposal of a pedestrian detection system but also go one step beyond by pointing out new insights, formalizing existing and proposed algorithms, introducing new techniques and evaluating their performance, which we hope will provide new foundations for future research in the area.
	Address	Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Antonio Lopez;Krystian Mikolajczyk;Jaume Amores;Dariu M. Gavrila;Oriol Pujol;Felipe Lumbreras
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-936529-5-1	Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ Ger2010			Serial	1279
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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
	Keywords
	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.
	Address
	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	Jaume Garcia; Debora Gil; Aura Hernandez-Sabate
	Title	Endowing Canonical Geometries to Cardiac Structures			Type	Book Chapter
	Year	2010	Publication	Statistical Atlases And Computational Models Of The Heart	Abbreviated Journal
	Volume	6364	Issue		Pages	124-133
	Keywords
	Abstract	International conference on Cardiac electrophysiological simulation challenge In this paper, we show that canonical (shape-based) geometries can be endowed to cardiac structures using tubular coordinates defined over their medial axis. We give an analytic formulation of these geometries by means of B-Splines. Since B-Splines present vector space structure PCA can be applied to their control points and statistical models relating boundaries and the interior of the anatomical structures can be derived. We demonstrate the applicability in two cardiac structures, the 3D Left Ventricular volume, and the 2D Left-Right ventricle set in 2D Short Axis view.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin / Heidelberg	Place of Publication		Editor	Camara, O.; Pop, M.; Rhode, K.; Sermesant, M.; Smith, N.; Young, A.
	Language		Summary Language		Original Title
	Series Editor		Series Title	Lecture Notes in Computer Science	Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM			Approved	no
	Call Number	IAM @ iam @ GGH2010b			Serial	1515
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	Author	Cesar Isaza; Joaquin Salas; Bogdan Raducanu
	Title	Toward the Detection of Urban Infrastructures Edge Shadows			Type	Conference Article
	Year	2010	Publication	12th International Conference on Advanced Concepts for Intelligent Vision Systems	Abbreviated Journal
	Volume	6474	Issue	I	Pages	30–37
	Keywords
	Abstract	In this paper, we propose a novel technique to detect the shadows cast by urban infrastructure, such as buildings, billboards, and traffic signs, using a sequence of images taken from a fixed camera. In our approach, we compute two different background models in parallel: one for the edges and one for the reflected light intensity. An algorithm is proposed to train the system to distinguish between moving edges in general and edges that belong to static objects, creating an edge background model. Then, during operation, a background intensity model allow us to separate between moving and static objects. Those edges included in the moving objects and those that belong to the edge background model are subtracted from the current image edges. The remaining edges are the ones cast by urban infrastructure. Our method is tested on a typical crossroad scene and the results show that the approach is sound and promising.
	Address	Sydney, Australia
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	eds. Blanc–Talon et al
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-17687-6	Medium
	Area		Expedition		Conference	ACIVS
	Notes	OR;MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ ISR2010			Serial	1458
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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
	Keywords
	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.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Gemma Sanchez;Josep Llados
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	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	Jaume Gibert; Ernest Valveny
	Title	Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation.			Type	Conference Article
	Year	2010	Publication	13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition	Abbreviated Journal
	Volume	6218	Issue		Pages	223–232
	Keywords
	Abstract	Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster.
	Address
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	In E.R. Hancock, R.C. Wilson, T. Windeatt, I. Ulusoy and F. Escolano,
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-14979-5	Medium
	Area		Expedition		Conference	S+SSPR
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ GiV2010			Serial	1416
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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.
	Address
	Corporate Author				Thesis
	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	Jean-Marc Ogier; Wenyin Liu; Josep Llados (eds)
	Title	Graphics Recognition: Achievements, Challenges, and Evolution			Type	Book Whole
	Year	2010	Publication	8th International Workshop GREC 2009.	Abbreviated Journal
	Volume	6020	Issue		Pages
	Keywords
	Abstract
	Address	La Rochelle
	Corporate Author				Thesis
	Publisher	Springer Link	Place of Publication		Editor	Jean-Marc Ogier; Wenyin Liu; Josep Llados
	Language		Summary Language		Original Title
	Series Editor		Series Title	Lecture Notes in Computer Science	Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-642-13727-3	Medium
	Area		Expedition		Conference	GREC
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ OLL2010			Serial	1976
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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
	Keywords
	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.
	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	978-84-937261-9-5	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ Riu2010			Serial	1331
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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
	Volume		Issue		Pages
	Keywords
	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.
	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	978-84-937261-3-3	Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	ISE @ ise @ Hue2010			Serial	1332
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