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	Author	Veronica Romero; Emilio Granell; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez
	Title	Information Extraction in Handwritten Marriage Licenses Books			Type	Conference Article
	Year	2019	Publication	5th International Workshop on Historical Document Imaging and Processing	Abbreviated Journal
	Volume		Issue		Pages	66-71
	Keywords
	Abstract	Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach.
	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	HIP
	Notes	DAG; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ RGF2019			Serial	3352
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	Author	Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez
	Title	Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology			Type	Conference Article
	Year	2017	Publication	8th Iberian Conference on Pattern Recognition and Image Analysis	Abbreviated Journal
	Volume	10255	Issue		Pages	287-294
	Keywords	Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model
	Abstract	Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.
	Address	Faro; Portugal; June 2017
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN		ISBN	978-3-319-58837-7	Medium
	Area		Expedition		Conference	IbPRIA
	Notes	DAG; 602.006; 600.097; 600.121			Approved	no
	Call Number	Admin @ si @ RFV2017			Serial	2952
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	Author	Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras
	Title	Information Theoretic Rotationwise Robust Binary Descriptor Learning			Type	Conference Article
	Year	2016	Publication	Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)	Abbreviated Journal
	Volume		Issue		Pages	368-378
	Keywords
	Abstract	In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications.
	Address	Mérida; Mexico; November 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	S+SSPR
	Notes	DAG; ADAS; 600.097; 600.086			Approved	no
	Call Number	Admin @ si @ RLL2016			Serial	2871
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla
	Title	Infrared Image Colorization based on a Triplet DCGAN Architecture			Type	Conference Article
	Year	2017	Publication	IEEE Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the color channels are obtained at the same time.
	Address	Honolulu; Hawaii; USA; July 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	CVPRW
	Notes	ADAS; 600.086; 600.118			Approved	no
	Call Number	Admin @ si @ SSV2017b			Serial	2920
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	Author	Debora Gil; Petia Radeva
	Title	Inhibition of false landmarks			Type	Journal Article
	Year	2006	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	27	Issue	9	Pages	1022-1030
	Keywords
	Abstract	Corners and junctions are landmarks characterized by the lack of differentiability in the unit tangent to the image level curve. Detectors based on differential operators are not, by their own definition, the best posed as they require a higher degree of differentiability to yield a reliable response. We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our inhibition orientation energy (IOE) landmark locator.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier Science Inc.	Place of Publication	New York, NY, USA	Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0167-8655	ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ GiR2006			Serial	1529
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	Author	Debora Gil; Petia Radeva
	Title	Inhibition of False Landmarks			Type	Book Chapter
	Year	2004	Publication	Recent Advances in Artificial Intelligence Research and Development	Abbreviated Journal
	Volume		Issue		Pages	233-244
	Keywords
	Abstract	We argue that a corner detector should be based on the degree of continuity of the tangent vector to the image level sets, work on the image domain and need no assumptions on neither the image local structure nor the particular geometry of the corner/junction. An operator measuring the degree of differentiability of the projection matrix on the image gradient fulfills the above requirements. Its high sensitivity to changes in vector directions makes it suitable for landmark location in real images prone to need smoothing to reduce the impact of noise. Because using smoothing kernels leads to corner misplacement, we suggest an alternative fake response remover based on the receptive field inhibition of spurious details. The combination of both orientation discontinuity detection and noise inhibition produce our Inhibition Orientation Energy (IOE) landmark locator.
	Address
	Corporate Author				Thesis
	Publisher	IOS Press	Place of Publication	Barcelona (Spain)	Editor	al, J.V. et
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	IAM;MILAB			Approved	no
	Call Number	IAM @ iam @ GiR2004a			Serial	1533
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	Author	Sergio Escalera; Stephane Ayache; Jun Wan; Meysam Madadi; Umut Guçlu; Xavier Baro
	Title	Inpainting and Denoising Challenges			Type	Book Whole
	Year	2019	Publication	The Springer Series on Challenges in Machine Learning	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The problem of dealing with missing or incomplete data in machine learning and computer vision arises in many applications. Recent strategies make use of generative models to impute missing or corrupted data. Advances in computer vision using deep generative models have found applications in image/video processing, such as denoising, restoration, super-resolution, or inpainting. Inpainting and Denoising Challenges comprises recent efforts dealing with image and video inpainting tasks. This includes winning solutions to the ChaLearn Looking at People inpainting and denoising challenges: human pose recovery, video de-captioning and fingerprint restoration. This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants. The following chapters present results from the Challenge, including three competition tasks at WCCI and ECML 2018. The top best approaches submitted by participants are described, showing interesting contributions and innovating methods. The last two chapters propose novel contributions and highlight new applications that benefit from image/video inpainting.
	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; no menciona			Approved	no
	Call Number	Admin @ si @ EAW2019			Serial	3398
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	Author	Fadi Dornaika; Angel Sappa
	Title	Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression			Type	Journal Article
	Year	2009	Publication	Pattern Recognition Letters	Abbreviated Journal	PRL
	Volume	30	Issue	5	Pages	535–543
	Keywords
	Abstract	This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes.
	Address
	Corporate Author				Thesis
	Publisher	Elsevier Science Inc.	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0167-8655	ISBN		Medium
	Area		Expedition		Conference
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ DoS2009a			Serial	1115
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	Author	Vishwesh Pillai; Pranav Mehar; Manisha Das; Deep Gupta; Petia Radeva
	Title	Integrated Hierarchical and Flat Classifiers for Food Image Classification using Epistemic Uncertainty			Type	Conference Article
	Year	2022	Publication	IEEE International Conference on Signal Processing and Communications	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The problem of food image recognition is an essential one in today’s context because health conditions such as diabetes, obesity, and heart disease require constant monitoring of a person’s diet. To automate this process, several models are available to recognize food images. Due to a considerable number of unique food dishes and various cuisines, a traditional flat classifier ceases to perform well. To address this issue, prediction schemes consisting of both flat and hierarchical classifiers, with the analysis of epistemic uncertainty are used to switch between the classifiers. However, the accuracy of the predictions made using epistemic uncertainty data remains considerably low. Therefore, this paper presents a prediction scheme using three different threshold criteria that helps to increase the accuracy of epistemic uncertainty predictions. The performance of the proposed method is demonstrated using several experiments performed on the MAFood-121 dataset. The experimental results validate the proposal performance and show that the proposed threshold criteria help to increase the overall accuracy of the predictions by correctly classifying the uncertainty distribution of the samples.
	Address	Bangalore; India; July 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	SPCOM
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ PMD2022			Serial	3796
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	Author	I. King; Zhong Jin
	Title	Integrated Probability Function and Its Application to Content-Based Image Retrieval By Relevance Feedback			Type	Journal
	Year	2003	Publication	Pattern Recognition, 36(9): 2177–2186 (IF: 1.611)	Abbreviated Journal
	Volume		Issue		Pages
	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				Approved	no
	Call Number	Admin @ si @ KiJ2003			Serial	427
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	Author	Danna Xue; Javier Vazquez; Luis Herranz; Yang Zhang; Michael S Brown
	Title	Integrating High-Level Features for Consistent Palette-based Multi-image Recoloring			Type	Journal Article
	Year	2023	Publication	Computer Graphics Forum	Abbreviated Journal	CGF
	Volume		Issue		Pages
	Keywords
	Abstract	Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi-image color consistency compared to one-to-one color transfer techniques. Furthermore, existing methods do not incorporate high-level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette-based color consistency method and incorporates three high-level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi-consistency workflow and showcase the user-friendly nature of our framework.
	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	CIC; MACO			Approved	no
	Call Number	Admin @ si @ XVH2023			Serial	3883
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	Author	Jelena Gorbova; Egils Avots; Iiris Lusi; Mark Fishel; Sergio Escalera; Gholamreza Anbarjafari
	Title	Integrating Vision and Language for First Impression Personality Analysis			Type	Journal Article
	Year	2018	Publication	IEEE Multimedia	Abbreviated Journal	MULTIMEDIA
	Volume	25	Issue	2	Pages	24 - 33
	Keywords
	Abstract	The authors present a novel methodology for analyzing integrated audiovisual signals and language to assess a persons personality. An evaluation of their proposed multimodal method using a job candidate screening system that predicted five personality traits from a short video demonstrates the methods effectiveness.
	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; 602.133			Approved	no
	Call Number	Admin @ si @ GAL2018			Serial	3124
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	Author	Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca
	Title	Integrating Vision and Language in Social Networks for Identifying Visual Patterns of Personality Traits			Type	Journal
	Year	2019	Publication	International Journal of Social Science and Humanity	Abbreviated Journal	IJSSH
	Volume	9	Issue	1	Pages	6-12
	Keywords
	Abstract	Social media, as a major platform for communication and information exchange, is a rich repository of the opinions and sentiments of 2.3 billion users about a vast spectrum of topics. In this sense, user text interactions are widely used to sense the whys of certain social user’s demands and cultural- driven interests. However, the knowledge embedded in the 1.8 billion pictures which are uploaded daily in public profiles has just started to be exploited. Following this trend on visual-based social analysis, we present a novel methodology based on neural networks to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits. So, the key contribution in this work is to explore whether OCEAN personality trait modeling can be addressed based on images, here called MindPics, appearing with certain tags with psychological insights. We found that there is a correlation between posted images and the personality estimated from their accompanying texts. Thus, the experimental results are consistent with previous cyber-psychology results based on texts, suggesting that images could also be used for personality estimation: classification results on some personality traits show that specific and characteristic visual patterns emerge, in essence representing abstract concepts. These results open new avenues of research for further refining the proposed personality model under the supervision of psychology experts, and to further substitute current textual personality questionnaires by image-based ones.
	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	ISE; 600.119			Approved	no
	Call Number	Admin @ si @ RGG2019			Serial	3414
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	Author	David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados
	Title	Integrating Visual and Textual Cues for Query-by-String Word Spotting			Type	Conference Article
	Year	2013	Publication	12th International Conference on Document Analysis and Recognition	Abbreviated Journal
	Volume		Issue		Pages	511 - 515
	Keywords
	Abstract	In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances.
	Address	Washington; USA; August 2013
	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	1520-5363	ISBN		Medium
	Area		Expedition		Conference	ICDAR
	Notes	DAG; ADAS; 600.045; 600.055; 600.061			Approved	no
	Call Number	Admin @ si @ ART2013			Serial	2224
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	Author	Petia Radeva; Maya Dimitrova; Ch. Roumenin; David Rotger; D. Nikolov; Juan J. Villanueva
	Title	Integration of Multiple Sensor Modalities in ActiveVessel Cardiology Workstation			Type	Miscellaneous
	Year	2004	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Varna (Bulgaria)
	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	BCNPCL @ bcnpcl @ RDR2004			Serial	473
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