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	Author	Francesco Ciompi; Oriol Pujol; O. Rodriguez-Leor; Angel Serrano; J. Mauri; Petia Radeva
	Title	On in-vitro and in-vivo IVUS data fusion			Type	Conference Article
	Year	2009	Publication	12th International Conference of the Catalan Association for Artificial Intelligence	Abbreviated Journal
	Volume	202	Issue		Pages	147-156
	Keywords
	Abstract	The design and the validation of an automatic plaque characterization technique based on Intravascular Ultrasound (IVUS) usually requires a data ground-truth. The histological analysis of post-mortem coronary arteries is commonly assumed as the state-of-the-art process for the extraction of a reliable data-set of atherosclerotic plaques. Unfortunately, the amount of data provided by this technique is usually few, due to the difficulties in collecting post-mortem cases and phenomena of tissue spoiling during histological analysis. In this paper we tackle the process of fusing in-vivo and in-vitro IVUS data starting with the analysis of recently proposed approaches for the creation of an enhanced IVUS data-set; furthermore, we propose a new approach, named pLDS, based on semi-supervised learning with a data selection criterion. The enhanced data-set obtained by each one of the analyzed approaches is used to train a classifier for tissue characterization purposes. Finally, the discriminative power of each classifier is quantitatively assessed and compared by classifying a data-set of validated in-vitro IVUS data.
	Address	Cardona (Spain)
	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	978-1-60750-061-2	Medium
	Area		Expedition		Conference	CCIA
	Notes	MILAB;HuPBA			Approved	no
	Call Number	BCNPCL @ bcnpcl @ CPR2009d			Serial	1204
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	Author	Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Benedi
	Title	Structure Detection and Segmentation of Documents Using 2D Stochastic Context-Free Grammars			Type	Journal Article
	Year	2015	Publication	Neurocomputing	Abbreviated Journal	NEUCOM
	Volume	150	Issue	A	Pages	147-154
	Keywords	document image analysis; stochastic context-free grammars; text classication features
	Abstract	In this paper we dene a bidimensional extension of Stochastic Context-Free Grammars for structure detection and segmentation of images of documents. Two sets of text classication features are used to perform an initial classication of each zone of the page. Then, the document segmentation is obtained as the most likely hypothesis according to a stochastic grammar. We used a dataset of historical marriage license books to validate this approach. We also tested several inference algorithms for Probabilistic Graphical Models and the results showed that the proposed grammatical model outperformed the other methods. Furthermore, grammars also provide the document structure along with its segmentation.
	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	DAG; 601.158; 600.077; 600.061			Approved	no
	Call Number	Admin @ si @ ACS2015			Serial	2531
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	Author	Stepan Simsa; Milan Sulc; Michal Uricar; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas
	Title	DocILE Benchmark for Document Information Localization and Extraction			Type	Conference Article
	Year	2023	Publication	17th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume	14188	Issue		Pages	147–166
	Keywords	Document AI; Information Extraction; Line Item Recognition; Business Documents; Intelligent Document Processing
	Abstract	This paper introduces the DocILE benchmark with the largest dataset of business documents for the tasks of Key Information Localization and Extraction and Line Item Recognition. It contains 6.7k annotated business documents, 100k synthetically generated documents, and nearly 1M unlabeled documents for unsupervised pre-training. The dataset has been built with knowledge of domain- and task-specific aspects, resulting in the following key features: (i) annotations in 55 classes, which surpasses the granularity of previously published key information extraction datasets by a large margin; (ii) Line Item Recognition represents a highly practical information extraction task, where key information has to be assigned to items in a table; (iii) documents come from numerous layouts and the test set includes zero- and few-shot cases as well as layouts commonly seen in the training set. The benchmark comes with several baselines, including RoBERTa, LayoutLMv3 and DETR-based Table Transformer; applied to both tasks of the DocILE benchmark, with results shared in this paper, offering a quick starting point for future work. The dataset, baselines and supplementary material are available at https://github.com/rossumai/docile.
	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	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ SSU2023			Serial	3903
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	Author	Naveen Onkarappa; Angel Sappa
	Title	Space Variant Representations for Mobile Platform Vision Applications			Type	Conference Article
	Year	2011	Publication	14th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	6855	Issue	II	Pages	146-154
	Keywords
	Abstract	The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
	Address	Seville, Spain
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor	P. Real, D. Diaz, H. Molina, A. Berciano, W. Kropatsch
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-23677-8	Medium
	Area		Expedition		Conference	CAIP
	Notes	ADAS			Approved	no
	Call Number	NaS2011; ADAS @ adas @			Serial	1686
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	Author	Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
	Title	Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams			Type	Journal Article
	Year	2016	Publication	Computer Vision and Image Understanding	Abbreviated Journal	CVIU
	Volume	149	Issue		Pages	146-156
	Keywords
	Abstract	Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.
	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 @ ADR2016b			Serial	2742
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	Author	Alex Falcon; Swathikiran Sudhakaran; Giuseppe Serra; Sergio Escalera; Oswald Lanz
	Title	Relevance-based Margin for Contrastively-trained Video Retrieval Models			Type	Conference Article
	Year	2022	Publication	ICMR '22: Proceedings of the 2022 International Conference on Multimedia Retrieval	Abbreviated Journal
	Volume		Issue		Pages	146-157
	Keywords
	Abstract	Video retrieval using natural language queries has attracted increasing interest due to its relevance in real-world applications, from intelligent access in private media galleries to web-scale video search. Learning the cross-similarity of video and text in a joint embedding space is the dominant approach. To do so, a contrastive loss is usually employed because it organizes the embedding space by putting similar items close and dissimilar items far. This framework leads to competitive recall rates, as they solely focus on the rank of the groundtruth items. Yet, assessing the quality of the ranking list is of utmost importance when considering intelligent retrieval systems, since multiple items may share similar semantics, hence a high relevance. Moreover, the aforementioned framework uses a fixed margin to separate similar and dissimilar items, treating all non-groundtruth items as equally irrelevant. In this paper we propose to use a variable margin: we argue that varying the margin used during training based on how much relevant an item is to a given query, i.e. a relevance-based margin, easily improves the quality of the ranking lists measured through nDCG and mAP. We demonstrate the advantages of our technique using different models on EPIC-Kitchens-100 and YouCook2. We show that even if we carefully tuned the fixed margin, our technique (which does not have the margin as a hyper-parameter) would still achieve better performance. Finally, extensive ablation studies and qualitative analysis support the robustness of our approach. Code will be released at \urlhttps://github.com/aranciokov/RelevanceMargin-ICMR22.
	Address	Newwark, NJ, USA, 27 June 2022
	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	ICMR
	Notes	HuPBA; no menciona			Approved	no
	Call Number	Admin @ si @ FSS2022			Serial	3808
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	Author	Javier Marin; David Geronimo; David Vazquez; Antonio Lopez
	Title	Pedestrian Detection: Exploring Virtual Worlds			Type	Book Chapter
	Year	2012	Publication	Handbook of Pattern Recognition: Methods and Application	Abbreviated Journal
	Volume	5	Issue		Pages	145-162
	Keywords	Virtual worlds; Pedestrian Detection; Domain Adaptation
	Abstract	Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition.
	Address
	Corporate Author				Thesis
	Publisher	iConcept Press	Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-1-477554-82-1	Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ MGV2012			Serial	1979
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	Author	Md.Mostafa Kamal Sarker; Syeda Furruka Banu; Hatem A. Rashwan; Mohamed Abdel-Nasser; Vivek Kumar Singh; Sylvie Chambon; Petia Radeva; Domenec Puig
	Title	Food Places Classification in Egocentric Images Using Siamese Neural Networks			Type	Conference Article
	Year	2019	Publication	22nd International Conference of the Catalan Association of Artificial Intelligence	Abbreviated Journal
	Volume		Issue		Pages	145-151
	Keywords
	Abstract	Wearable cameras are become more popular in recent years for capturing the unscripted moments of the first-person that help to analyze the users lifestyle. In this work, we aim to recognize the places related to food in egocentric images during a day to identify the daily food patterns of the first-person. Thus, this system can assist to improve their eating behavior to protect users against food-related diseases. In this paper, we use Siamese Neural Networks to learn the similarity between images from corresponding inputs for one-shot food places classification. We tested our proposed method with ‘MiniEgoFoodPlaces’ with 15 food related places. The proposed Siamese Neural Networks model with MobileNet achieved an overall classification accuracy of 76.74% and 77.53% on the validation and test sets of the “MiniEgoFoodPlaces” dataset, respectively outperforming with the base models, such as ResNet50, InceptionV3, and InceptionResNetV2.
	Address	Illes Balears; October 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	CCIA
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ SBR2019			Serial	3368
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	Author	Michal Drozdzal; Santiago Segui; Carolina Malagelada; Fernando Azpiroz; Jordi Vitria; Petia Radeva
	Title	Interactive Labeling of WCE Images			Type	Conference Article
	Year	2011	Publication	5th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	6669	Issue		Pages	143-150
	Keywords
	Abstract	A high quality labeled training set is necessary for any supervised machine learning algorithm. Labeling of the data can be a very expensive process, specially while dealing with data of high variability and complexity. A good example of such data are the videos from Wireless Capsule Endoscopy. Building a representative WCE data set means many videos to be labeled by an expert. The problem that occurs is the data diversity, in the space of the features, from different WCE studies. That means that when new data arrives it is highly probable that it will not be represented in the training set, thus getting a high probability of performing an error when applying machine learning schemes. In this paper an interactive labeling scheme that allows reducing expert effort in the labeling process is presented. It is shown that the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of the WCE video with less than 100 clicks
	Address	Las Palmas de Gran Canaria. Spain
	Corporate Author				Thesis
	Publisher	Springer	Place of Publication		Editor	Vitria, Jordi; Sanches, João Miguel Raposo; Hernández, Mario
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	IbPRIA
	Notes	MILAB;OR;MV			Approved	no
	Call Number	Admin @ si @ DSM2011			Serial	1734
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	Author	Santiago Segui; Michal Drozdzal; Petia Radeva; Jordi Vitria
	Title	An Integrated Approach to Contextual Face Detection			Type	Conference Article
	Year	2012	Publication	1st International Conference on Pattern Recognition Applications and Methods	Abbreviated Journal
	Volume		Issue		Pages	143-150
	Keywords
	Abstract	Face detection is, in general, based on content-based detectors. Nevertheless, the face is a non-rigid object with well defined relations with respect to the human body parts. In this paper, we propose to take benefit of the context information in order to improve content-based face detections. We propose a novel framework for integrating multiple content- and context-based detectors in a discriminative way. Moreover, we develop an integrated scoring procedure that measures the ’faceness’ of each hypothesis and is used to discriminate the detection results. Our approach detects a higher rate of faces while minimizing the number of false detections, giving an average increase of more than 10% in average precision when comparing it to state-of-the art face detectors
	Address	Vilamoura, Algarve, Portugal
	Corporate Author				Thesis
	Publisher	Springer	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	ICPRAM
	Notes	MILAB; OR;MV			Approved	no
	Call Number	Admin @ si @ SDR2012			Serial	1895
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	Author	Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez
	Title	Embedded real-time stereo estimation via Semi-Global Matching on the GPU			Type	Conference Article
	Year	2016	Publication	16th International Conference on Computational Science	Abbreviated Journal
	Volume	80	Issue		Pages	143-153
	Keywords	Autonomous Driving; Stereo; CUDA; 3d reconstruction
	Abstract	Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
	Address	San Diego; CA; USA; 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	ICCS
	Notes	ADAS; 600.085; 600.082; 600.076			Approved	no
	Call Number	ADAS @ adas @ HCE2016a			Serial	2740
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	Author	Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon
	Title	Looking at People Special Issue			Type	Journal Article
	Year	2018	Publication	International Journal of Computer Vision	Abbreviated Journal	IJCV
	Volume	126	Issue	2-4	Pages	141-143
	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	HUPBA; ISE; 600.119			Approved	no
	Call Number	Admin @ si @ EGJ2018			Serial	3093
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	Author	Pau Torras; Mohamed Ali Souibgui; Jialuo Chen; Alicia Fornes
	Title	A Transcription Is All You Need: Learning to Align through Attention			Type	Conference Article
	Year	2021	Publication	14th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume	12916	Issue		Pages	141–146
	Keywords
	Abstract	Historical ciphered manuscripts are a type of document where graphical symbols are used to encrypt their content instead of regular text. Nowadays, expert transcriptions can be found in libraries alongside the corresponding manuscript images. However, those transcriptions are not aligned, so these are barely usable for training deep learning-based recognition methods. To solve this issue, we propose a method to align each symbol in the transcript of an image with its visual representation by using an attention-based Sequence to Sequence (Seq2Seq) model. The core idea is that, by learning to recognise symbols sequence within a cipher line image, the model also identifies their position implicitly through an attention mechanism. Thus, the resulting symbol segmentation can be later used for training algorithms. The experimental evaluation shows that this method is promising, especially taking into account the small size of the cipher dataset.
	Address	Virtual; September 2021
	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	GREC
	Notes	DAG; 602.230; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ TSC2021			Serial	3619
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	Author	Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
	Title	Rank Estimation in Missing Data Matrix Problems			Type	Journal Article
	Year	2011	Publication	Journal of Mathematical Imaging and Vision	Abbreviated Journal	JMIV
	Volume	39	Issue	2	Pages	140-160
	Keywords
	Abstract	A novel technique for missing data matrix rank estimation is presented. It is focused on matrices of trajectories, where every element of the matrix corresponds to an image coordinate from a feature point of a rigid moving object at a given frame; missing data are represented as empty entries. The objective of the proposed approach is to estimate the rank of a missing data matrix in order to fill in empty entries with some matrix completion method, without using or assuming neither the number of objects contained in the scene nor the kind of their motion. The key point of the proposed technique consists in studying the frequency behaviour of the individual trajectories, which are seen as 1D signals. The main assumption is that due to the rigidity of the moving objects, the frequency content of the trajectories will be similar after filling in their missing entries. The proposed rank estimation approach can be used in different computer vision problems, where the rank of a missing data matrix needs to be estimated. Experimental results with synthetic and real data are provided in order to empirically show the good performance of the proposed 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	0924-9907	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ JSL2011;			Serial	1710
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	Author	Debora Gil; F. Javier Sanchez; Gloria Fernandez Esparrach; Jorge Bernal
	Title	3D Stable Spatio-temporal Polyp Localization in Colonoscopy Videos			Type	Book Chapter
	Year	2015	Publication	Computer-Assisted and Robotic Endoscopy. Revised selected papers of Second International Workshop, CARE 2015, Held in Conjunction with MICCAI 2015	Abbreviated Journal
	Volume	9515	Issue		Pages	140-152
	Keywords	Colonoscopy, Polyp Detection, Polyp Localization, Region Extraction, Watersheds
	Abstract	Computational intelligent systems could reduce polyp miss rate in colonoscopy for colon cancer diagnosis and, thus, increase the efficiency of the procedure. One of the main problems of existing polyp localization methods is a lack of spatio-temporal stability in their response. We propose to explore the response of a given polyp localization across temporal windows in order to select those image regions presenting the highest stable spatio-temporal response. Spatio-temporal stability is achieved by extracting 3D watershed regions on the temporal window. Stability in localization response is statistically determined by analysis of the variance of the output of the localization method inside each 3D region. We have explored the benefits of considering spatio-temporal stability in two different tasks: polyp localization and polyp detection. Experimental results indicate an average improvement of 21:5% in polyp localization and 43:78% in polyp detection.
	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	CARE
	Notes	IAM; MV; 600.075			Approved	no
	Call Number	Admin @ si @ GSF2015			Serial	2733
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