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	Author	Raul Gomez; Yahui Liu; Marco de Nadai; Dimosthenis Karatzas; Bruno Lepri; Nicu Sebe
	Title	Retrieval Guided Unsupervised Multi-domain Image to Image Translation			Type	Conference Article
	Year	2020	Publication	28th ACM International Conference on Multimedia	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Image to image translation aims to learn a mapping that transforms an image from one visual domain to another. Recent works assume that images descriptors can be disentangled into a domain-invariant content representation and a domain-specific style representation. Thus, translation models seek to preserve the content of source images while changing the style to a target visual domain. However, synthesizing new images is extremely challenging especially in multi-domain translations, as the network has to compose content and style to generate reliable and diverse images in multiple domains. In this paper we propose the use of an image retrieval system to assist the image-to-image translation task. First, we train an image-to-image translation model to map images to multiple domains. Then, we train an image retrieval model using real and generated images to find images similar to a query one in content but in a different domain. Finally, we exploit the image retrieval system to fine-tune the image-to-image translation model and generate higher quality images. Our experiments show the effectiveness of the proposed solution and highlight the contribution of the retrieval network, which can benefit from additional unlabeled data and help image-to-image translation models in the presence of scarce data.
	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	ACM
	Notes	DAG; 600.121			Approved	no
	Call Number	Admin @ si @ GLN2020			Serial	3497
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	Author	Henry Velesaca; Steven Araujo; Patricia Suarez; Angel Sanchez; Angel Sappa
	Title	Off-the-Shelf Based System for Urban Environment Video Analytics			Type	Conference Article
	Year	2020	Publication	27th International Conference on Systems, Signals and Image Processing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	greenhouse gases; carbon footprint; object detection; object tracking; website framework; off-the-shelf video analytics
	Abstract	This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
	Address	Virtual IWSSIP
	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	IWSSIP
	Notes	MSIAU; 600.130; 601.349; 600.122			Approved	no
	Call Number	Admin @ si @ VAS2020			Serial	3429
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	Author	Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras
	Title	The Photometry of Intrinsic Images			Type	Conference Article
	Year	2014	Publication	27th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1494-1501
	Keywords
	Abstract	Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images.
	Address	Columbus; Ohio; USA; June 2014
	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	CVPR
	Notes	CIC; 600.052; 600.051; 600.074			Approved	no
	Call Number	Admin @ si @ SPB2014			Serial	2506
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	Author	M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer
	Title	Adaptive color attributes for real-time visual tracking			Type	Conference Article
	Year	2014	Publication	27th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1090 - 1097
	Keywords
	Abstract	Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power. This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms state-of-the-art tracking methods while running at more than 100 frames per second.
	Address	Nottingham; UK; September 2014
	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	CVPR
	Notes	CIC; LAMP; 600.074; 600.079			Approved	no
	Call Number	Admin @ si @ DKF2014			Serial	2509
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	Author	Vassileios Balntas; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk
	Title	Learning local feature descriptors with triplets and shallow convolutional neural networks			Type	Conference Article
	Year	2016	Publication	27th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives. We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets.
	Address	York; UK; September 2016
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	BMVC
	Notes	ADAS; 600.086			Approved	no
	Call Number	Admin @ si @ BRP2016			Serial	2818
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	Author	Arash Akbarinia; C. Alejandro Parraga
	Title	Biologically plausible boundary detection			Type	Conference Article
	Year	2016	Publication	27th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.
	Address	York; UK; September 2016
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	BMVC
	Notes	NEUROBIT; 600.068; 600.072			Approved	no
	Call Number	Admin @ si @ AkP2016a			Serial	2867
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	Author	Corina Krauter; Ursula Reiter; Albrecht Schmidt; Marc Masana; Rudolf Stollberger; Michael Fuchsjager; Gert Reiter
	Title	Objective extraction of the temporal evolution of the mitral valve vortex ring from 4D flow MRI			Type	Conference Article
	Year	2019	Publication	27th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	The mitral valve vortex ring is a promising flow structure for analysis of diastolic function, however, methods for objective extraction of its formation to dissolution are lacking. We present a novel algorithm for objective extraction of the temporal evolution of the mitral valve vortex ring from magnetic resonance 4D flow data and validated the method against visual analysis. The algorithm successfully extracted mitral valve vortex rings during both early- and late-diastolic filling and agreed substantially with visual assessment. Early-diastolic mitral valve vortex ring properties differed between healthy subjects and patients with ischemic heart disease.
	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	ISMRM
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ KRS2019			Serial	3300
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	Author	Joan Codina-Filba; Sergio Escalera; Joan Escudero; Coen Antens; Pau Buch-Cardona; Mireia Farrus
	Title	Mobile eHealth Platform for Home Monitoring of Bipolar Disorder			Type	Conference Article
	Year	2021	Publication	27th ACM International Conference on Multimedia Modeling	Abbreviated Journal
	Volume	12573	Issue		Pages	330-341
	Keywords
	Abstract	People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur. MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.
	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	MMM
	Notes	HUPBA; no proj			Approved	no
	Call Number	Admin @ si @ CEE2021			Serial	3659
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	Author	Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz
	Title	SDIT: Scalable and Diverse Cross-domain Image Translation			Type	Conference Article
	Year	2019	Publication	27th ACM International Conference on Multimedia	Abbreviated Journal
	Volume		Issue		Pages	1267–1276
	Keywords
	Abstract	Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.
	Address	Nice; Francia; 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	ACM-MM
	Notes	LAMP; 600.106; 600.109; 600.141; 600.120			Approved	no
	Call Number	Admin @ si @ WGW2019			Serial	3363
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	Author	Mohamed Ali Souibgui; Sanket Biswas; Sana Khamekhem Jemni; Yousri Kessentini; Alicia Fornes; Josep Llados; Umapada Pal
	Title	DocEnTr: An End-to-End Document Image Enhancement Transformer			Type	Conference Article
	Year	2022	Publication	26th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	1699-1705
	Keywords	Degradation; Head; Optical character recognition; Self-supervised learning; Benchmark testing; Transformers; Magnetic heads
	Abstract	Document images can be affected by many degradation scenarios, which cause recognition and processing difficulties. In this age of digitization, it is important to denoise them for proper usage. To address this challenge, we present a new encoder-decoder architecture based on vision transformers to enhance both machine-printed and handwritten document images, in an end-to-end fashion. The encoder operates directly on the pixel patches with their positional information without the use of any convolutional layers, while the decoder reconstructs a clean image from the encoded patches. Conducted experiments show a superiority of the proposed model compared to the state-of the-art methods on several DIBCO benchmarks. Code and models will be publicly available at: https://github.com/dali92002/DocEnTR
	Address	August 21-25, 2022 , Montréal Québec
	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	ICPR
	Notes	DAG; 600.121; 600.162; 602.230; 600.140			Approved	no
	Call Number	Admin @ si @ SBJ2022			Serial	3730
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	Author	Carlos Boned Riera; Oriol Ramos Terrades
	Title	Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph			Type	Conference Article
	Year	2022	Publication	26th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	2186-2191
	Keywords	Measurement; Couplings; Semantics; Ear; Benchmark testing; Data models; Pattern recognition
	Abstract	Nowadays the paradigm of link discovery problems has shown significant improvements on Knowledge Graphs. However, method performances are harmed by the unbalanced nature of this classification problem, since many methods are easily biased to not find proper links. In this paper we present a discriminative neural variational auto-encoder model, called DNVAE from now on, in which we have introduced latent variables to serve as embedding vectors. As a result, the learnt generative model approximate better the underlying distribution and, at the same time, it better differentiate the type of relations in the knowledge graph. We have evaluated this approach on benchmark knowledge graph and Census records. Results in this last data set are quite impressive since we reach the highest possible score in the evaluation metrics. However, further experiments are still needed to deeper evaluate the performance of the method in more challenging tasks.
	Address	Montreal; Quebec; Canada; August 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	ICPR
	Notes	DAG; 600.121; 600.162			Approved	no
	Call Number	Admin @ si @ BoR2022			Serial	3741
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	Author	Vacit Oguz Yazici; Joost Van de Weijer; Longlong Yu
	Title	Visual Transformers with Primal Object Queries for Multi-Label Image Classification			Type	Conference Article
	Year	2022	Publication	26th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Multi-label image classification is about predicting a set of class labels that can be considered as orderless sequential data. Transformers process the sequential data as a whole, therefore they are inherently good at set prediction. The first vision-based transformer model, which was proposed for the object detection task introduced the concept of object queries. Object queries are learnable positional encodings that are used by attention modules in decoder layers to decode the object classes or bounding boxes using the region of interests in an image. However, inputting the same set of object queries to different decoder layers hinders the training: it results in lower performance and delays convergence. In this paper, we propose the usage of primal object queries that are only provided at the start of the transformer decoder stack. In addition, we improve the mixup technique proposed for multi-label classification. The proposed transformer model with primal object queries improves the state-of-the-art class wise F1 metric by 2.1% and 1.8%; and speeds up the convergence by 79.0% and 38.6% on MS-COCO and NUS-WIDE datasets respectively.
	Address	Montreal; Quebec; Canada; August 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	ICPR
	Notes	LAMP; 600.147; 601.309			Approved	no
	Call Number	Admin @ si @ YWY2022			Serial	3786
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	Author	Ayan Banerjee; Palaiahnakote Shivakumara; Parikshit Acharya; Umapada Pal; Josep Llados
	Title	TWD: A New Deep E2E Model for Text Watermark Detection in Video Images			Type	Conference Article
	Year	2022	Publication	26th International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Deep learning; U-Net; FCENet; Scene text detection; Video text detection; Watermark text detection
	Abstract	Text watermark detection in video images is challenging because text watermark characteristics are different from caption and scene texts in the video images. Developing a successful model for detecting text watermark, caption, and scene texts is an open challenge. This study aims at developing a new Deep End-to-End model for Text Watermark Detection (TWD), caption and scene text in video images. To standardize non-uniform contrast, quality, and resolution, we explore the U-Net3+ model for enhancing poor quality text without affecting high-quality text. Similarly, to address the challenges of arbitrary orientation, text shapes and complex background, we explore Stacked Hourglass Encoded Fourier Contour Embedding Network (SFCENet) by feeding the output of the U-Net3+ model as input. Furthermore, the proposed work integrates enhancement and detection models as an end-to-end model for detecting multi-type text in video images. To validate the proposed model, we create our own dataset (named TW-866), which provides video images containing text watermark, caption (subtitles), as well as scene text. The proposed model is also evaluated on standard natural scene text detection datasets, namely, ICDAR 2019 MLT, CTW1500, Total-Text, and DAST1500. The results show that the proposed method outperforms the existing methods. This is the first work on text watermark detection in video images to the best of our knowledge
	Address	Montreal; Quebec; Canada; August 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	ICPR
	Notes	DAG;			Approved	no
	Call Number	Admin @ si @ BSA2022			Serial	3788
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	Author	Emanuel Sanchez Aimar; Petia Radeva; Mariella Dimiccoli
	Title	Social Relation Recognition in Egocentric Photostreams			Type	Conference Article
	Year	2019	Publication	26th International Conference on Image Processing	Abbreviated Journal
	Volume		Issue		Pages	3227-3231
	Keywords
	Abstract	This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.
	Address	Taipei; Taiwan; 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	ICIP
	Notes	MILAB; no menciona			Approved	no
	Call Number	Admin @ si @ SRD2019			Serial	3370
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	Author	Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell
	Title	Color-based data augmentation for Reflectance Estimation			Type	Conference Article
	Year	2018	Publication	26th Color Imaging Conference	Abbreviated Journal
	Volume		Issue		Pages	284-289
	Keywords
	Abstract	Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods.
	Address	Vancouver; November 2018
	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	CIC
	Notes	CIC			Approved	no
	Call Number	Admin @ si @ SSB2018a			Serial	3129
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