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	Author	Pejman Rasti; Tonis Uiboupin; Sergio Escalera; Gholamreza Anbarjafari
	Title	Convolutional Neural Network Super Resolution for Face Recognition in Surveillance Monitoring			Type	Conference Article
	Year	2016	Publication	9th Conference on Articulated Motion and Deformable Objects	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Palma de Mallorca; Spain; July 2016
	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	AMDO
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ RUE2016			Serial	2846
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	Author	Mark Philip Philipsen; Anders Jorgensen; Thomas B. Moeslund; Sergio Escalera
	Title	RGB-D Segmentation of Poultry Entrails			Type	Conference Article
	Year	2016	Publication	9th Conference on Articulated Motion and Deformable Objects	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Best commercial paper award.
	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	AMDO
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ PJM2016			Serial	2848
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	Author	Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
	Title	Stable Airway Center Tracking for Bronchoscopic Navigation			Type	Conference Article
	Year	2016	Publication	28th Conference of the international Society for Medical Innovation and Technology	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Bronchoscopists use X‐ray fluoroscopy to guide bronchoscopes to the lesion to be biopsied without any kind of incisions. Reducing exposure to X‐ray is important for both patients and doctors but alternatives like electromagnetic navigation require specific equipment and increase the cost of the clinical procedure. We propose a guiding system based on the extraction of airway centers from intra‐operative videos. Such anatomical landmarks could be matched to the airway centerline extracted from a pre‐planned CT to indicate the best path to the lesion. We present an extraction of lumen centers from intra‐operative videos based on tracking of maximal stable regions of energy maps.
	Address	Delft; Rotterdam; Leiden; The Netherlands; October 2016
	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	SMIT
	Notes	IAM;			Approved	no
	Call Number	Admin @ si @ LSB2016a			Serial	2856
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	Author	Sergio Escalera; Jordi Gonzalez; Xavier Baro; Fernando Alonso; Martha Mackay
	Title	Care Respite: a remote monitoring eHealth system for improving ambient assisted living			Type	Conference Article
	Year	2016	Publication	Human Motion Analysis for Healthcare Applications	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Advances in technology that capture human motion have been quite remarkable during the last five years. New sensors have been developed, such as the Microsoft Kinect, Asus Xtion Pro live, PrimeSense Carmine and Leap Motion. Their main advantages are their non-intrusive nature, low cost and widely available support for developers offered by large corporations or Open Communities. Although they were originally developed for computer games, they have inspired numerous healthcare related ideas and projects in areas such as Medical Disorder Diagnosis, Assisted Living, Rehabilitation and Surgery. In Assisted Living, human motion analysis allows continuous monitoring of elderly and vulnerable people and their activities to potentially detect life-threatening events such as falls. Human motion analysis in rehabilitation provides the opportunity for motivating patients through gamification, evaluating prescribed programmes of exercises and assessing patients’ progress. In operating theatres, surgeons may use a gesture-based interface to access medical information or control a tele-surgery system. Human motion analysis may also be used to diagnose a range of mental and physical diseases and conditions. This event will discuss recent advances in human motion sensing and provide an application to healthcare for networking and exploring potential synergies and collaborations.
	Address	Savoy Place; London; uk; May 2016
	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	HMAHA
	Notes	HuPBA; ISE;			Approved	no
	Call Number	Admin @ si @ EGB2016			Serial	2852
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	Author	Jose Ramirez Moreno; Juan R Revilla; Miguel Reyes; Sergio Escalera
	Title	Validación del Software ADIBAS asociado al sensor Kinect de Microsoft para la evaluación de la posición corporal			Type	Conference Article
	Year	2016	Publication	4th Congreso WCPT-SAR	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Buenos Aires; Argentina; June 2016
	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	WCPT-SAR
	Notes	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ RRR2016			Serial	2853
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	Author	Fernando Alonso; Xavier Baro; Sergio Escalera; Jordi Gonzalez; Martha Mackay; Anna Serrahima
	Title	CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions			Type	Conference Article
	Year	2016	Publication	16th International Conference for Integrated Care	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Poster
	Address	Barcelona; Spain; May 2016
	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	ICIC
	Notes	HuPBA; ISE;MV			Approved	no
	Call Number	Admin @ si @ ABE2016			Serial	2855
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	Author	Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil
	Title	Stable Anatomical Structure Tracking for video-bronchoscopy Navigation			Type	Conference Article
	Year	2016	Publication	19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking
	Abstract	Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at dierent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations.
	Address	Athens; Greece; October 2016
	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	MICCAIW
	Notes	IAM; 600.075			Approved	no
	Call Number	Admin @ si @ LSB2016b			Serial	2857
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	Author	German Ros
	Title	Visual Scene Understanding for Autonomous Vehicles: Understanding Where and What			Type	Book Whole
	Year	2016	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Making Ground Autonomous Vehicles (GAVs) a reality as a service for the society is one of the major scientific and technological challenges of this century. The potential benefits of autonomous vehicles include reducing accidents, improving traffic congestion and better usage of road infrastructures, among others. These vehicles must operate in our cities, towns and highways, dealing with many different types of situations while respecting traffic rules and protecting human lives. GAVs are expected to deal with all types of scenarios and situations, coping with an uncertain and chaotic world. Therefore, in order to fulfill these demanding requirements GAVs need to be endowed with the capability of understanding their surrounding at many different levels, by means of affordable sensors and artificial intelligence. This capacity to understand the surroundings and the current situation that the vehicle is involved in is called scene understanding. In this work we investigate novel techniques to bring scene understanding to autonomous vehicles by combining the use of cameras as the main source of information—due to their versatility and affordability—and algorithms based on computer vision and machine learning. We investigate different degrees of understanding of the scene, starting from basic geometric knowledge about where is the vehicle within the scene. A robust and efficient estimation of the vehicle location and pose with respect to a map is one of the most fundamental steps towards autonomous driving. We study this problem from the point of view of robustness and computational efficiency, proposing key insights to improve current solutions. Then we advance to higher levels of abstraction to discover what is in the scene, by recognizing and parsing all the elements present on a driving scene, such as roads, sidewalks, pedestrians, etc. We investigate this problem known as semantic segmentation, proposing new approaches to improve recognition accuracy and computational efficiency. We cover these points by focusing on key aspects such as: (i) how to leverage computation moving semantics to an offline process, (ii) how to train compact architectures based on deconvolutional networks to achieve their maximum potential, (iii) how to use virtual worlds in combination with domain adaptation to produce accurate models in a cost-effective fashion, and (iv) how to use transfer learning techniques to prepare models to new situations. We finally extend the previous level of knowledge enabling systems to reasoning about what has change in a scene with respect to a previous visit, which in return allows for efficient and cost-effective map updating.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Angel Sappa;Julio Guerrero;Antonio Lopez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-945373-1-8	Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ Ros2016			Serial	2860
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	Author	Francisco Cruz
	Title	Probabilistic Graphical Models for Document Analysis			Type	Book Whole
	Year	2016	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Latest advances in digitization techniques have fostered the interest in creating digital copies of collections of documents. Digitized documents permit an easy maintenance, loss-less storage, and efficient ways for transmission and to perform information retrieval processes. This situation has opened a new market niche to develop systems able to automatically extract and analyze information contained in these collections, specially in the ambit of the business activity. Due to the great variety of types of documents this is not a trivial task. For instance, the automatic extraction of numerical data from invoices differs substantially from a task of text recognition in historical documents. However, in order to extract the information of interest, is always necessary to identify the area of the document where it is located. In the area of Document Analysis we refer to this process as layout analysis, which aims at identifying and categorizing the different entities that compose the document, such as text regions, pictures, text lines, or tables, among others. To perform this task it is usually necessary to incorporate a prior knowledge about the task into the analysis process, which can be modeled by defining a set of contextual relations between the different entities of the document. The use of context has proven to be useful to reinforce the recognition process and improve the results on many computer vision tasks. It presents two fundamental questions: What kind of contextual information is appropriate for a given task, and how to incorporate this information into the models. In this thesis we study several ways to incorporate contextual information to the task of document layout analysis, and to the particular case of handwritten text line segmentation. We focus on the study of Probabilistic Graphical Models and other mechanisms for this purpose, and propose several solutions to these problems. First, we present a method for layout analysis based on Conditional Random Fields. With this model we encode local contextual relations between variables, such as pair-wise constraints. Besides, we encode a set of structural relations between different classes of regions at feature level. Second, we present a method based on 2D-Probabilistic Context-free Grammars to encode structural and hierarchical relations. We perform a comparative study between Probabilistic Graphical Models and this syntactic approach. Third, we propose a method for structured documents based on Bayesian Networks to represent the document structure, and an algorithm based in the Expectation-Maximization to find the best configuration of the page. We perform a thorough evaluation of the proposed methods on two particular collections of documents: a historical collection composed of ancient structured documents, and a collection of contemporary documents. In addition, we present a general method for the task of handwritten text line segmentation. We define a probabilistic framework where we combine the EM algorithm with variational approaches for computing inference and parameter learning on a Markov Random Field. We evaluate our method on several collections of documents, including a general dataset of annotated administrative documents. Results demonstrate the applicability of our method to real problems, and the contribution of the use of contextual information to this kind of problems.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher	Ediciones Graficas Rey	Place of Publication		Editor	Oriol Ramos Terrades
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-84-945373-2-5	Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ Cru2016			Serial	2861
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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	Arash Akbarinia; C. Alejandro Parraga
	Title	Biologically plausible boundary detection			Type	Conference Article
	Year	2016	Publication	27th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.
	Address	York; UK; September 2016
	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	BMVC
	Notes	NEUROBIT; 600.068; 600.072			Approved	no
	Call Number	Admin @ si @ AkP2016a			Serial	2867
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	Author	Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez
	Title	Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest			Type	Miscellaneous
	Year	2016	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Domain Adaptation; Pedestrian detection; Random Forest
	Abstract	Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.
	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ MVJ2016			Serial	2868
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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	Lluis Gomez
	Title	Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding			Type	Book Whole
	Year	2016	Publication	PhD Thesis, Universitat Autonoma de Barcelona-CVC	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes. For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision. Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which text emerges as a perceptually significant group of atomic objects. In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods. Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve the overall tracking performance of the system. Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme.
	Address
	Corporate Author				Thesis	Ph.D. thesis
	Publisher		Place of Publication		Editor	Dimosthenis Karatzas
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ Gom2016			Serial	2891
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