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	Author	Simone Balocco; Mauricio Gonzalez; Ricardo Ñancule; Petia Radeva; Gabriel Thomas
	Title	Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets			Type	Conference Article
	Year	2018	Publication	International Workshop on Artificial Intelligence and Pattern Recognition	Abbreviated Journal
	Volume	11047	Issue		Pages	34-42
	Keywords	Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis
	Abstract	The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall.
	Address	Cuba; September 2018
	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	IWAIPR
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ BGÑ2018			Serial	3237
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	Author	Sumit K. Banchhor; Narendra D. Londhe; Tadashi Araki; Luca Saba; Petia Radeva; Narendra N. Khanna; Jasjit S. Suri
	Title	Calcium detection, its quantification, and grayscale morphology-based risk stratification using machine learning in multimodality big data coronary and carotid scans: A review.			Type	Journal Article
	Year	2018	Publication	Computers in Biology and Medicine	Abbreviated Journal	CBM
	Volume	101	Issue		Pages	184-198
	Keywords	Heart disease; Stroke; Atherosclerosis; Intravascular; Coronary; Carotid; Calcium; Morphology; Risk stratification
	Abstract	Purpose of review Atherosclerosis is the leading cause of cardiovascular disease (CVD) and stroke. Typically, atherosclerotic calcium is found during the mature stage of the atherosclerosis disease. It is therefore often a challenge to identify and quantify the calcium. This is due to the presence of multiple components of plaque buildup in the arterial walls. The American College of Cardiology/American Heart Association guidelines point to the importance of calcium in the coronary and carotid arteries and further recommend its quantification for the prevention of heart disease. It is therefore essential to stratify the CVD risk of the patient into low- and high-risk bins. Recent finding Calcium formation in the artery walls is multifocal in nature with sizes at the micrometer level. Thus, its detection requires high-resolution imaging. Clinical experience has shown that even though optical coherence tomography offers better resolution, intravascular ultrasound still remains an important imaging modality for coronary wall imaging. For a computer-based analysis system to be complete, it must be scientifically and clinically validated. This study presents a state-of-the-art review (condensation of 152 publications after examining 200 articles) covering the methods for calcium detection and its quantification for coronary and carotid arteries, the pros and cons of these methods, and the risk stratification strategies. The review also presents different kinds of statistical models and gold standard solutions for the evaluation of software systems useful for calcium detection and quantification. Finally, the review concludes with a possible vision for designing the next-generation system for better clinical outcomes.
	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	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ BLA2018			Serial	3188
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	Author	Ole Larsen; Petia Radeva; Enric Marti
	Title	Calculating the Bounds on the Optimal Parameters of Elasticity for a Snake			Type	Report
	Year	1994	Publication	Technical Report	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Aalborg University
	Corporate Author				Thesis
	Publisher	Aalborg University, Laboratory of image Analysis.	Place of Publication	Denmark	Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area	Aalborg University, Laboratory of image Analysis.	Expedition		Conference
	Notes	MILAB;IAM			Approved	no
	Call Number	IAM @ iam @ LRM1994			Serial	1560
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	Author	J. Pladellorens; M.J. Yzuel; J. Castell; Joan Serrat
	Title	Calculo automatico del volumen del ventriculo izquierdo. Comparacion con expertos.			Type	Journal
	Year	1993	Publication	Optica Pura y Aplicada.	Abbreviated Journal
	Volume	26	Issue	3	Pages	685–691
	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		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ PYC1993			Serial	149
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	Author	Fares Alnajar; Theo Gevers; Roberto Valenti; Sennay Ghebreab
	Title	Calibration-free Gaze Estimation using Human Gaze Patterns			Type	Conference Article
	Year	2013	Publication	15th IEEE International Conference on Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	137-144
	Keywords
	Abstract	We present a novel method to auto-calibrate gaze estimators based on gaze patterns obtained from other viewers. Our method is based on the observation that the gaze patterns of humans are indicative of where a new viewer will look at [12]. When a new viewer is looking at a stimulus, we first estimate a topology of gaze points (initial gaze points). Next, these points are transformed so that they match the gaze patterns of other humans to find the correct gaze points. In a flexible uncalibrated setup with a web camera and no chin rest, the proposed method was tested on ten subjects and ten images. The method estimates the gaze points after looking at a stimulus for a few seconds with an average accuracy of 4.3 im. Although the reported performance is lower than what could be achieved with dedicated hardware or calibrated setup, the proposed method still provides a sufficient accuracy to trace the viewer attention. This is promising considering the fact that auto-calibration is done in a flexible setup , without the use of a chin rest, and based only on a few seconds of gaze initialization data. To the best of our knowledge, this is the first work to use human gaze patterns in order to auto-calibrate gaze estimators.
	Address	Sydney
	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	ICCV
	Notes	ALTRES;ISE			Approved	no
	Call Number	Admin @ si @ AGV2013			Serial	2365
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	Author	Pierdomenico Fiadino; Victor Ponce; Juan Antonio Torrero-Gonzalez; Marc Torrent-Moreno
	Title	Call Detail Records for Human Mobility Studies: Taking Stock of the Situation in the “Always Connected Era"			Type	Conference Article
	Year	2017	Publication	Workshop on Big Data Analytics and Machine Learning for Data Communication Networks	Abbreviated Journal
	Volume		Issue		Pages	43-48
	Keywords	mobile networks; call detail records; human mobility
	Abstract	The exploitation of cellular network data for studying human mobility has been a popular research topic in the last decade. Indeed, mobile terminals could be considered ubiquitous sensors that allow the observation of human movements on large scale without the need of relying on non-scalable techniques, such as surveys, or dedicated and expensive monitoring infrastructures. In particular, Call Detail Records (CDRs), collected by operators for billing purposes, have been extensively employed due to their rather large availability, compared to other types of cellular data (e.g., signaling). Despite the interest aroused around this topic, the research community has generally agreed about the scarcity of information provided by CDRs: the position of mobile terminals is logged when some kind of activity (calls, SMS, data connections) occurs, which translates in a picture of mobility somehow biased by the activity degree of users. By studying two datasets collected by a Nation-wide operator in 2014 and 2016, we show that the situation has drastically changed in terms of data volume and quality. The increase of flat data plans and the higher penetration of “ always connected” terminals have driven up the number of recorded CDRs, providing higher temporal accuracy for users’ locations.
	Address	UCLA; USA; August 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	978-1-4503-5054-9	Medium
	Area		Expedition		Conference	ACMW (SIGCOMM)
	Notes	HuPBA; no menciona			Approved	no
	Call Number	Admin @ si @ FPT2017			Serial	2980
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	Author	Diego Alejandro Cheda; Daniel Ponsa; Antonio Lopez
	Title	Camera Egomotion Estimation in the ADAS Context			Type	Conference Article
	Year	2010	Publication	13th International IEEE Annual Conference on Intelligent Transportation Systems	Abbreviated Journal
	Volume		Issue		Pages	1415–1420
	Keywords
	Abstract	Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain.
	Address	Madeira Island (Portugal)
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2153-0009	ISBN	978-1-4244-7657-2	Medium
	Area		Expedition		Conference	ITSC
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ CPL2010			Serial	1425
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	Author	Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca
	Title	Camera pose estimation in multi-view environments: From virtual scenarios to the real world			Type	Journal Article
	Year	2021	Publication	Image and Vision Computing	Abbreviated Journal	IVC
	Volume	110	Issue		Pages	104182
	Keywords
	Abstract	This paper presents a domain adaptation strategy to efficiently train network architectures for estimating the relative camera pose in multi-view scenarios. The network architectures are fed by a pair of simultaneously acquired images, hence in order to improve the accuracy of the solutions, and due to the lack of large datasets with pairs of overlapped images, a domain adaptation strategy is proposed. The domain adaptation strategy consists on transferring the knowledge learned from synthetic images to real-world scenarios. For this, the networks are firstly trained using pairs of synthetic images, which are captured at the same time by a pair of cameras in a virtual environment; and then, the learned weights of the networks are transferred to the real-world case, where the networks are retrained with a few real images. Different virtual 3D scenarios are generated to evaluate the relationship between the accuracy on the result and the similarity between virtual and real scenarios—similarity on both geometry of the objects contained in the scene as well as relative pose between camera and objects in the scene. Experimental results and comparisons are provided showing that the accuracy of all the evaluated networks for estimating the camera pose improves when the proposed domain adaptation strategy is used, highlighting the importance on the similarity between virtual-real scenarios.
	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	MSIAU; 600.130; 600.122			Approved	no
	Call Number	Admin @ si @ CSV2021			Serial	3577
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	Author	Marçal Rusiñol; Josep Llados; Philippe Dosch
	Title	Camera-Based Graphical Symbol Detection			Type	Conference Article
	Year	2007	Publication	9th IEEE International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	2	Issue		Pages	884–888
	Keywords
	Abstract
	Address	Curitiba (Brazil)
	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	ICDAR
	Notes	DAG			Approved	no
	Call Number	DAG @ dag @ RLD2007			Serial	848
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	Author	Adria Rico; Alicia Fornes
	Title	Camera-based Optical Music Recognition using a Convolutional Neural Network			Type	Conference Article
	Year	2017	Publication	12th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages	27-28
	Keywords	optical music recognition; document analysis; convolutional neural network; deep learning
	Abstract	Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results
	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	GREC
	Notes	DAG;600.097; 600.121			Approved	no
	Call Number	Admin @ si @ RiF2017			Serial	3059
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	Author	Pedro Herruzo; Marc Bolaños; Petia Radeva
	Title	Can a CNN Recognize Catalan Diet?			Type	Book Chapter
	Year	2016	Publication	AIP Conference Proceedings	Abbreviated Journal
	Volume	1773	Issue		Pages
	Keywords
	Abstract	CoRR abs/1607.08811 Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient’s behavior, allowing specialists to discover unhealthy food patterns and understand the user’s lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes.
	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	MILAB			Approved	no
	Call Number	Admin @ si @ HBR2016			Serial	2837
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	Author	Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu
	Title	Can biological solutions help computers detect symmetry?			Type	Conference Article
	Year	2017	Publication	40th European Conference on Visual Perception	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Berlin; Germany; August 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	ECVP
	Notes	NEUROBIT			Approved	no
	Call Number	Admin @ si @ APE2017			Serial	2995
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	Author	Petia Radeva
	Title	Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better?			Type	Conference Article
	Year	2016	Publication	19th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume	4	Issue		Pages
	Keywords
	Abstract
	Address	Barcelona; 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	CCIA
	Notes	MILAB			Approved	no
	Call Number	Admin @ si @ Rad2016			Serial	2832
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	Author	Mohammed Al Rawi; Ernest Valveny; Dimosthenis Karatzas
	Title	Can One Deep Learning Model Learn Script-Independent Multilingual Word-Spotting?			Type	Conference Article
	Year	2019	Publication	15th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	260-267
	Keywords
	Abstract	Word spotting has gained increased attention lately as it can be used to extract textual information from handwritten documents and scene-text images. Current word spotting approaches are designed to work on a single language and/or script. Building intelligent models that learn script-independent multilingual word-spotting is challenging due to the large variability of multilingual alphabets and symbols. We used ResNet-152 and the Pyramidal Histogram of Characters (PHOC) embedding to build a one-model script-independent multilingual word-spotting and we tested it on Latin, Arabic, and Bangla (Indian) languages. The one-model we propose performs on par with the multi-model language-specific word-spotting system, and thus, reduces the number of models needed for each script and/or language.
	Address	Sydney; Australia; September 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	ICDAR
	Notes	DAG; 600.129; 600.121			Approved	no
	Call Number	Admin @ si @ RVK2019			Serial	3337
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	Author	Adarsh Tiwari; Sanket Biswas; Josep Llados
	Title	Can Pre-trained Language Models Help in Understanding Handwritten Symbols?			Type	Conference Article
	Year	2023	Publication	17th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	14193	Issue		Pages	199–211
	Keywords
	Abstract	The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets.
	Address	San Jose; CA; USA; August 2023
	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	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ TBL2023			Serial	3908
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