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	Author	Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari
	Title	SASE: RGB-Depth Database for Human Head Pose Estimation			Type	Conference Article
	Year	2016	Publication	14th European Conference on Computer Vision Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Slides
	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
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ECCVW
	Notes	HuPBA;MILAB;			Approved	no
	Call Number	Admin @ si @ LEA2016a			Serial	2840
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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	Pau Riba; Josep Llados; Alicia Fornes
	Title	Error-tolerant coarse-to-fine matching model for hierarchical graphs			Type	Conference Article
	Year	2017	Publication	11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition	Abbreviated Journal
	Volume	10310	Issue		Pages	107-117
	Keywords	Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching
	Abstract	Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.
	Address	Anacapri; Italy; May 2017
	Corporate Author				Thesis
	Publisher	Springer International Publishing	Place of Publication		Editor	Pasquale Foggia; Cheng-Lin Liu; Mario Vento
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	GbRPR
	Notes	DAG; 600.097; 601.302; 600.121			Approved	no
	Call Number	Admin @ si @ RLF2017a			Serial	2951
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	Cycle Generative Adversarial Network: Towards A Low-Cost Vegetation Index Estimation			Type	Conference Article
	Year	2021	Publication	28th IEEE International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages	19-22
	Keywords
	Abstract	This paper presents a novel unsupervised approach to estimate the Normalized Difference Vegetation Index (NDVI). The NDVI is obtained as the ratio between information from the visible and near infrared spectral bands; in the current work, the NDVI is estimated just from an image of the visible spectrum through a Cyclic Generative Adversarial Network (CyclicGAN). This unsupervised architecture learns to estimate the NDVI index by means of an image translation between the red channel of a given RGB image and the NDVI unpaired index’s image. The translation is obtained by means of a ResNET architecture and a multiple loss function. Experimental results obtained with this unsupervised scheme show the validity of the implemented model. Additionally, comparisons with the state of the art approaches are provided showing improvements with the proposed approach.
	Address	Anchorage-Alaska; USA; September 2021
	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	ICIP
	Notes	MSIAU; 600.130; 600.122; 601.349			Approved	no
	Call Number	Admin @ si @ SSV2021b			Serial	3579
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	Author	Bogdan Raducanu; Fadi Dornaika
	Title	Dynamic Facial Expression Recognition Using Laplacian Eigenmaps-Based Manifold Learning			Type	Conference Article
	Year	2010	Publication	IEEE International Conference on Robotics and Automation	Abbreviated Journal
	Volume		Issue		Pages	156–161
	Keywords
	Abstract	In this paper, we propose an integrated framework for tracking, modelling and recognition of facial expressions. The main contributions are: (i) a view- and texture independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker; (ii) the complexity of the non-linear facial expression space is modelled through a manifold, whose structure is learned using Laplacian Eigenmaps. The projected facial expressions are afterwards recognized based on Nearest Neighbor classifier; (iii) with the proposed approach, we developed an application for an AIBO robot, in which it mirrors the perceived facial expression.
	Address	Anchorage; AK; USA;
	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	1050-4729	ISBN	978-1-4244-5038-1	Medium
	Area		Expedition		Conference	ICRA
	Notes	OR; MV			Approved	no
	Call Number	BCNPCL @ bcnpcl @ RaD2010			Serial	1310
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	Author	Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Julio C. S. Jacques Junior; Xavier Baro; Evelyne Viegas; Yagmur Gucluturk; Umut Guclu; Marcel A. J. van Gerven; Rob van Lier; Meysam Madadi; Stephane Ayache
	Title	Design of an Explainable Machine Learning Challenge for Video Interviews			Type	Conference Article
	Year	2017	Publication	International Joint Conference on Neural Networks	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration.
	Address	Anchorage; Alaska; USA; May 2017
	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	IJCNN
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ EGE2017			Serial	2922
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	Author	Sergio Escalera; Xavier Baro; Hugo Jair Escalante; Isabelle Guyon
	Title	ChaLearn Looking at People: A Review of Events and Resources			Type	Conference Article
	Year	2017	Publication	30th International Joint Conference on Neural Networks	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper reviews the historic of ChaLearn Looking at People (LAP) events. We started in 2011 (with the release of the first Kinect device) to run challenges related to human action/activity and gesture recognition. Since then we have regularly organized events in a series of competitions covering all aspects of visual analysis of humans. So far we have organized more than 10 international challenges and events in this field. This paper reviews associated events, and introduces the ChaLearn LAP platform where public resources (including code, data and preprints of papers) related to the organized events are available. We also provide a discussion on perspectives of ChaLearn LAP activities.
	Address	Anchorage; Alaska; USA; May 2017
	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	IJCNN
	Notes	HuPBA; 602.143			Approved	no
	Call Number	Admin @ si @ EBE2017			Serial	3012
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	Author	Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo
	Title	Comparing Combinations of Feature Regions for Panoramic VSLAM			Type	Conference Article
	Year	2007	Publication	4th International Conference on Informatics in Control, Automation and Robotics	Abbreviated Journal
	Volume		Issue		Pages	292–297
	Keywords
	Abstract
	Address	Angers (France)
	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	ICINCO
	Notes	RV;ADAS			Approved	no
	Call Number	Admin @ si @ RLA2007			Serial	900
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	Author	Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti
	Title	A Case Study of Pattern Recognition: Symbol Recognition in Graphic Documentsa			Type	Conference Article
	Year	2003	Publication	Proceedings of Pattern Recognition in Information Systems	Abbreviated Journal
	Volume		Issue		Pages	1-13
	Keywords
	Abstract
	Address	Angers, France
	Corporate Author				Thesis
	Publisher	ICEIS Press	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	972-98816-3-4	Medium
	Area		Expedition		Conference	PRIS'03
	Notes	DAG;IAM;			Approved	no
	Call Number	IAM @ iam @ LVS2003			Serial	1576
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	Author	Francisco Jose Perales; Juan J. Villanueva; Yuhua Luo
	Title	Matching Criteria			Type	Conference Article
	Year	1991	Publication	Computer and Information Sciences VI, Proceedings of the 1991 International Symposium on Computer and Information Sciences	Abbreviated Journal
	Volume	1	Issue		Pages	1029-1038
	Keywords
	Abstract
	Address	Antalya, Turkey, 30 October-2 November 1991
	Corporate Author				Thesis
	Publisher	Elsevier Science Pub.	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0097-8493	ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	ISE @ ise @ PVL1991a			Serial	264
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	Author	Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca
	Title	Towards Visual Personality Questionnaires based on Deep Learning and Social Media			Type	Conference Article
	Year	2019	Publication	21st International Conference on Social Influence and Social Psychology	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	April 2019; Tokio; Japan
	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	ICSISP
	Notes	ISE; 600.119			Approved	no
	Call Number	Admin @ si @ RGG2020			Serial	3554
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	Author	Angel Morera; Angel Sanchez; Angel Sappa; Jose F. Velez
	Title	Robust Detection of Outdoor Urban Advertising Panels in Static Images			Type	Conference Article
	Year	2019	Publication	18th International Conference on Practical Applications of Agents and Multi-Agent Systems	Abbreviated Journal
	Volume		Issue		Pages	246-256
	Keywords	Object detection; Urban ads panels; Deep learning; Single Shot Detector (SSD) architecture; Intersection over Union (IoU) metric; Augmented Reality
	Abstract	One interesting publicity application for Smart City environments is recognizing brand information contained in urban advertising panels. For such a purpose, a previous stage is to accurately detect and locate the position of these panels in images. This work presents an effective solution to this problem using a Single Shot Detector (SSD) based on a deep neural network architecture that minimizes the number of false detections under multiple variable conditions regarding the panels and the scene. Achieved experimental results using the Intersection over Union (IoU) accuracy metric make this proposal applicable in real complex urban images.
	Address	Aquila; Italia; June 2019
	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	PAAMS
	Notes	MSIAU; 600.130; 600.122			Approved	no
	Call Number	Admin @ si @ MSS2019			Serial	3270
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	Author	Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas
	Title	Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.
	Address	Aspen; Colorado; USA; March 2020
	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	WACV
	Notes	DAG; 600.129; 600.140; 601.302; 601.312; 600.121			Approved	no
	Call Number	Admin @ si @ KRF2020			Serial	3446
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	Author	Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski
	Title	Kornia: an Open Source Differentiable Computer Vision Library for PyTorch			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Aspen; Colorado; USA; March 2020
	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	WACV
	Notes	MSIAU; 600.122; 600.130			Approved	no
	Call Number	Admin @ si @ RMP2020			Serial	3291
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	Author	Andres Mafla; Sounak Dey; Ali Furkan Biten; Lluis Gomez; Dimosthenis Karatzas
	Title	Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual Features			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding. In particular, the mere presence of text provides strong guiding content that should be employed to tackle a diversity of computer vision tasks such as image retrieval, fine-grained classification, and visual question answering. In this paper, we address the problem of fine-grained classification and image retrieval by leveraging textual information along with visual cues to comprehend the existing intrinsic relation between the two modalities. The novelty of the proposed model consists of the usage of a PHOC descriptor to construct a bag of textual words along with a Fisher Vector Encoding that captures the morphology of text. This approach provides a stronger multimodal representation for this task and as our experiments demonstrate, it achieves state-of-the-art results on two different tasks, fine-grained classification and image retrieval.
	Address	Aspen; Colorado; USA; March 2020
	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	WACV
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ MDB2020			Serial	3334
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