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	Author	Marc Oliu; Ciprian Corneanu; Laszlo A. Jeni; Jeffrey F. Cohn; Takeo Kanade; Sergio Escalera
	Title	Continuous Supervised Descent Method for Facial Landmark Localisation			Type	Conference Article
	Year	2016	Publication	13th Asian Conference on Computer Vision	Abbreviated Journal
	Volume	10112	Issue		Pages	121-135
	Keywords
	Abstract	Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size.
	Address	Taipei; Taiwan; November 2016
	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		Medium
	Area		Expedition		Conference	ACCV
	Notes	HuPBA;MILAB;			Approved	no
	Call Number	Admin @ si @ OCJ2016			Serial	2838
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	Author	Cesar de Souza; Adrien Gaidon; Eleonora Vig; Antonio Lopez
	Title	Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition			Type	Conference Article
	Year	2016	Publication	14th European Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	697-716
	Keywords
	Abstract	Action recognition in videos is a challenging task due to the complexity of the spatio-temporal patterns to model and the difficulty to acquire and learn on large quantities of video data. Deep learning, although a breakthrough for image classification and showing promise for videos, has still not clearly superseded action recognition methods using hand-crafted features, even when training on massive datasets. In this paper, we introduce hybrid video classification architectures based on carefully designed unsupervised representations of hand-crafted spatio-temporal features classified by supervised deep networks. As we show in our experiments on five popular benchmarks for action recognition, our hybrid model combines the best of both worlds: it is data efficient (trained on 150 to 10000 short clips) and yet improves significantly on the state of the art, including recent deep models trained on millions of manually labelled images and videos.
	Address	Amsterdam; The Netherlands; October 2016
	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		Medium
	Area		Expedition		Conference	ECCV
	Notes	ADAS; 600.076; 600.085			Approved	no
	Call Number	Admin @ si @ SGV2016			Serial	2824
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	Author	Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio
	Title	EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game			Type	Conference Article
	Year	2016	Publication	5th International Conference Games and Learning Alliance	Abbreviated Journal
	Volume	10056	Issue		Pages	50-59
	Keywords	Simulation environment; Automated Driving; Driver-Vehicle interaction
	Abstract	Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
	Address
	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		Medium
	Area		Expedition		Conference	GALA
	Notes	ADAS;IAM;			Approved	no
	Call Number	HAC2016			Serial	2864
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	Author	Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier
	Title	LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode			Type	Conference Article
	Year	2016	Publication	14th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume	9915	Issue		Pages	894-900
	Keywords	Simulation environment; Automated Driving; Driver-Vehicle interaction
	Abstract	Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
	Address	Amsterdam; The Netherlands; October 2016
	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		Medium
	Area		Expedition		Conference	ECCVW
	Notes	ADAS;IAM; 600.085; 600.076			Approved	no
	Call Number	MHE2016			Serial	2865
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	Author	Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal
	Title	Local Binary Pattern for Word Spotting in Handwritten Historical Document			Type	Conference Article
	Year	2016	Publication	Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)	Abbreviated Journal
	Volume		Issue		Pages	574-583
	Keywords	Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data
	Abstract	Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm.
	Address	Merida; Mexico; December 2016
	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		Medium
	Area		Expedition		Conference	S+SSPR
	Notes	DAG; 600.097; 602.006; 603.053			Approved	no
	Call Number	Admin @ si @ DNL2016			Serial	2876
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	Author	Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados
	Title	Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling			Type	Conference Article
	Year	2016	Publication	Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)	Abbreviated Journal
	Volume	10029	Issue		Pages	543-552
	Keywords	Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection
	Abstract	The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results.
	Address	Merida; Mexico; December 2016
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-319-49054-0	Medium
	Area		Expedition		Conference	S+SSPR
	Notes	DAG; 600.097; 602.006			Approved	no
	Call Number	Admin @ si @ TSF2016			Serial	2877
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	Author	Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau
	Title	Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain			Type	Conference Article
	Year	2016	Publication	7th International Workshop on Statistical Atlases & Computational Modelling of the Heart	Abbreviated Journal
	Volume	10124	Issue		Pages	163-171
	Keywords	Laplacian; Constrained maps; Parameterization; Basal ring
	Abstract	Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries.
	Address	Athens; October 2016
	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		Medium
	Area		Expedition		Conference	STACOM
	Notes	IAM;			Approved	no
	Call Number	Admin @ si @ GGM2016			Serial	2884
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	Author	Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell
	Title	Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches			Type	Conference Article
	Year	2016	Publication	19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops	Abbreviated Journal
	Volume	9401	Issue		Pages	62-70
	Keywords	Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy
	Abstract	Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface.
	Address	Quebec; Canada; September 2016
	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		Medium
	Area		Expedition		Conference	MICCAIW
	Notes	IAM; MV; 600.060; 600.075			Approved	no
	Call Number	Admin @ si @ SGB2016			Serial	2885
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	Author	Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa
	Title	Fine-tuning based deep convolutional networks for lepidopterous genus recognition			Type	Conference Article
	Year	2016	Publication	21st Ibero American Congress on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	467-475
	Keywords
	Abstract	This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%.
	Address	Lima; Perú; November 2016
	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		Medium
	Area		Expedition		Conference	CIARP
	Notes	ADAS; 600.086			Approved	no
	Call Number	Admin @ si @ CRS2016			Serial	2913
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	Author	Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari
	Title	Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images			Type	Conference Article
	Year	2016	Publication	23rd International Conference on Pattern Recognition	Abbreviated Journal
	Volume	10165	Issue		Pages
	Keywords
	Abstract	Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution.
	Address	Cancun; Mexico; December 2016
	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		Medium
	Area		Expedition		Conference	ICPR
	Notes	HuPBA; ISE; 600.098; 600.119			Approved	no
	Call Number	Admin @ si @ BHR2016			Serial	2902
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	Author	Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari
	Title	Human Head Pose Estimation on SASE database using Random Hough Regression Forests			Type	Conference Article
	Year	2016	Publication	23rd International Conference on Pattern Recognition Workshops	Abbreviated Journal
	Volume	10165	Issue		Pages
	Keywords
	Abstract	In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests.
	Address	Cancun; Mexico; December 2016
	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		Medium
	Area		Expedition		Conference	ICPRW
	Notes	HuPBA;			Approved	no
	Call Number	Admin @ si @ LEA2016b			Serial	2910
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	Author	L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip
	Title	Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation			Type	Journal Article
	Year	2016	Publication	Computers & Industrial Engineering	Abbreviated Journal	CIE
	Volume	94	Issue		Pages	93-104
	Keywords	Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning
	Abstract	In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial oer and customers show dierent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that dierent customer-depot assignment maps will lead to dierent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diers in terms of the proposed solutions from the traditional one.
	Address
	Corporate Author				Thesis
	Publisher	PERGAMON-ELSEVIER SCIENCE LTD	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	CIE
	Series Volume		Series Issue		Edition
	ISSN	0360-8352	ISBN		Medium
	Area		Expedition		Conference
	Notes	OR;MV;			Approved	no
	Call Number	Admin @ si @ CFG2016			Serial	2749
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	Author	C. Butakoff; Simone Balocco; F.M. Sukno; C. Hoogendoorn; C. Tobon-Gomez; G. Avegliano; A.F. Frangi
	Title	Left-ventricular Epi- and Endocardium Extraction from 3D Ultrasound Images Using an Automatically Constructed 3D ASM			Type	Journal Article
	Year	2016	Publication	Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization	Abbreviated Journal	CMBBE
	Volume	4	Issue	5	Pages	265-280
	Keywords	ASM; cardiac segmentation; statistical model; shape model; 3D ultrasound; cardiac segmentation
	Abstract	In this paper, we propose an automatic method for constructing an active shape model (ASM) to segment the complete cardiac left ventricle in 3D ultrasound (3DUS) images, which avoids costly manual landmarking. The automatic construction of the ASM has already been addressed in the literature; however, the direct application of these methods to 3DUS is hampered by a high level of noise and artefacts. Therefore, we propose to construct the ASM by fusing the multidetector computed tomography data, to learn the shape, with the artificially generated 3DUS, in order to learn the neighbourhood of the boundaries. Our artificial images were generated by two approaches: a faster one that does not take into account the geometry of the transducer, and a more comprehensive one, implemented in Field II toolbox. The segmentation accuracy of our ASM was evaluated on 20 patients with left-ventricular asynchrony, demonstrating plausibility of the approach.
	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	2168-1163	ISBN		Medium
	Area		Expedition		Conference
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ BBS2016			Serial	2449
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	Author	Svebor Karaman; Andrew Bagdanov; Lea Landucci; Gianpaolo D'Amico; Andrea Ferracani; Daniele Pezzatini; Alberto del Bimbo
	Title	Personalized multimedia content delivery on an interactive table by passive observation of museum visitors			Type	Journal Article
	Year	2016	Publication	Multimedia Tools and Applications	Abbreviated Journal	MTAP
	Volume	75	Issue	7	Pages	3787-3811
	Keywords	Computer vision; Video surveillance; Cultural heritage; Multimedia museum; Personalization; Natural interaction; Passive profiling
	Abstract	The amount of multimedia data collected in museum databases is growing fast, while the capacity of museums to display information to visitors is acutely limited by physical space. Museums must seek the perfect balance of information given on individual pieces in order to provide sufficient information to aid visitor understanding while maintaining sparse usage of the walls and guaranteeing high appreciation of the exhibit. Moreover, museums often target the interests of average visitors instead of the entire spectrum of different interests each individual visitor might have. Finally, visiting a museum should not be an experience contained in the physical space of the museum but a door opened onto a broader context of related artworks, authors, artistic trends, etc. In this paper we describe the MNEMOSYNE system that attempts to address these issues through a new multimedia museum experience. Based on passive observation, the system builds a profile of the artworks of interest for each visitor. These profiles of interest are then used to drive an interactive table that personalizes multimedia content delivery. The natural user interface on the interactive table uses the visitor’s profile, an ontology of museum content and a recommendation system to personalize exploration of multimedia content. At the end of their visit, the visitor can take home a personalized summary of their visit on a custom mobile application. In this article we describe in detail each component of our approach as well as the first field trials of our prototype system built and deployed at our permanent exhibition space at LeMurate (http://www.lemurate.comune.fi.it/lemurate/) in Florence together with the first results of the evaluation process during the official installation in the National Museum of Bargello (http://www.uffizi.firenze.it/musei/?m=bargello).
	Address
	Corporate Author				Thesis
	Publisher	Springer US	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1380-7501	ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; 601.240; 600.079			Approved	no
	Call Number	Admin @ si @ KBL2016			Serial	2520
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	Author	Miguel Angel Bautista; Antonio Hernandez; Sergio Escalera; Laura Igual; Oriol Pujol; Josep Moya; Veronica Violant; Maria Teresa Anguera
	Title	A Gesture Recognition System for Detecting Behavioral Patterns of ADHD			Type	Journal Article
	Year	2016	Publication	IEEE Transactions on System, Man and Cybernetics, Part B	Abbreviated Journal	TSMCB
	Volume	46	Issue	1	Pages	136-147
	Keywords	Gesture Recognition; ADHD; Gaussian Mixture Models; Convex Hulls; Dynamic Time Warping; Multi-modal RGB-Depth data
	Abstract	We present an application of gesture recognition using an extension of Dynamic Time Warping (DTW) to recognize behavioural patterns of Attention Deficit Hyperactivity Disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either GMMs or an approximation of Convex Hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intra-class gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioural patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multi-modal dataset (RGB plus Depth) of ADHD children recordings with behavioural patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	HuPBA; MILAB;			Approved	no
	Call Number	Admin @ si @ BHE2016			Serial	2566
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