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	Author	Yaxing Wang; Joost Van de Weijer; Luis Herranz
	Title	Mix and match networks: encoder-decoder alignment for zero-pair image translation			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	5467 - 5476
	Keywords
	Abstract	We address the problem of image translation between domains or modalities for which no direct paired data is available (i.e. zero-pair translation). We propose mix and match networks, based on multiple encoders and decoders aligned in such a way that other encoder-decoder pairs can be composed at test time to perform unseen image translation tasks between domains or modalities for which explicit paired samples were not seen during training. We study the impact of autoencoders, side information and losses in improving the alignment and transferability of trained pairwise translation models to unseen translations. We show our approach is scalable and can perform colorization and style transfer between unseen combinations of domains. We evaluate our system in a challenging cross-modal setting where semantic segmentation is estimated from depth images, without explicit access to any depth-semantic segmentation training pairs. Our model outperforms baselines based on pix2pix and CycleGAN models.
	Address	Salt Lake City; USA; June 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	CVPR
	Notes	LAMP; 600.109; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ WWH2018b			Serial	3131
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	Author	Adrian Galdran; Aitor Alvarez-Gila; Alessandro Bria; Javier Vazquez; Marcelo Bertalmio
	Title	On the Duality Between Retinex and Image Dehazing			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	8212–8221
	Keywords	Image color analysis; Task analysis; Atmospheric modeling; Computer vision; Computational modeling; Lighting
	Abstract	Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.
	Address	Salt Lake City; USA; June 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	CVPR
	Notes	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ GAB2018			Serial	3146
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	Author	Xialei Liu; Joost Van de Weijer; Andrew Bagdanov
	Title	Leveraging Unlabeled Data for Crowd Counting by Learning to Rank			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	7661 - 7669
	Keywords	Task analysis; Training; Computer vision; Visualization; Estimation; Head; Context modeling
	Abstract	We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and queryby-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-ofthe-art results.
	Address	Salt Lake City; USA; June 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	CVPR
	Notes	LAMP; 600.109; 600.106; 600.120			Approved	no
	Call Number	Admin @ si @ LWB2018			Serial	3159
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	Author	Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov
	Title	Word Spotting in Scene Images based on Character Recognition			Type	Conference Article
	Year	2018	Publication	IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops	Abbreviated Journal
	Volume		Issue		Pages	1872-1874
	Keywords
	Abstract	In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images.
	Address	Salt Lake City; USA; June 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	CVPRW
	Notes	DAG; 600.129; 600.121			Approved	no
	Call Number	BKB2018a			Serial	3179
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	Author	Patricia Suarez; Angel Sappa; Boris X. Vintimilla; Riad I. Hammoud
	Title	Deep Learning based Single Image Dehazing			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition Workhsop	Abbreviated Journal
	Volume		Issue		Pages	1250 - 12507
	Keywords	Gallium nitride; Atmospheric modeling; Generators; Generative adversarial networks; Convergence; Image color analysis
	Abstract	This paper proposes a novel approach to remove haze degradations in RGB images using a stacked conditional Generative Adversarial Network (GAN). It employs a triplet of GAN to remove the haze on each color channel independently. A multiple loss functions scheme, applied over a conditional probabilistic model, is proposed. The proposed GAN architecture learns to remove the haze, using as conditioned entrance, the images with haze from which the clear images will be obtained. Such formulation ensures a fast model training convergence and a homogeneous model generalization. Experiments showed that the proposed method generates high-quality clear images.
	Address	Salt Lake City; USA; June 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	CVPRW
	Notes	MSIAU; 600.086; 600.130; 600.122			Approved	no
	Call Number	Admin @ si @ SSV2018d			Serial	3197
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	Author	Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi
	Title	WiCV 2018: The Fourth Women In Computer Vision Workshop			Type	Conference Article
	Year	2018	Publication	4th Women in Computer Vision Workshop	Abbreviated Journal
	Volume		Issue		Pages	1941-19412
	Keywords	Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
	Abstract	We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration, and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
	Address	Salt Lake City; USA; June 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	WiCV
	Notes	DAG; 600.121; 600.129			Approved	no
	Call Number	Admin @ si @ DBR2018			Serial	3222
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	Author	Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari
	Title	Objects as context for detecting their semantic parts			Type	Conference Article
	Year	2018	Publication	31st IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	6907 - 6916
	Keywords	Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection
	Abstract	We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets.
	Address	Salt Lake City; USA; June 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	CVPR
	Notes	LAMP; 600.109; 600.120			Approved	no
	Call Number	Admin @ si @ GMF2018			Serial	3229
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	Author	Bojana Gajic; Ramon Baldrich
	Title	Cross-domain fashion image retrieval			Type	Conference Article
	Year	2018	Publication	CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition)	Abbreviated Journal
	Volume		Issue		Pages	19500-19502
	Keywords
	Abstract	Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task.
	Address	Salt Lake City, USA; 22 June 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	CVPRW
	Notes	CIC; 600.087			Approved	no
	Call Number	Admin @ si @			Serial	3709
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	Author	Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva
	Title	Multi-class Binary Symbol Classification with Circular Blurred Shape Models			Type	Conference Article
	Year	2009	Publication	15th International Conference on Image Analysis and Processing	Abbreviated Journal
	Volume	5716	Issue		Pages	1005–1014
	Keywords
	Abstract	Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements.
	Address	Salerno, Italy
	Corporate Author				Thesis
	Publisher	Springer Berlin Heidelberg	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title	LNCS
	Series Volume		Series Issue		Edition
	ISSN	0302-9743	ISBN	978-3-642-04145-7	Medium
	Area		Expedition		Conference	ICIAP
	Notes	MILAB;HuPBA;DAG			Approved	no
	Call Number	BCNPCL @ bcnpcl @ EFP2009c			Serial	1186
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	Author	Joan Arnedo-Moreno; D. Bañeres; Xavier Baro; S. Caballe; S. Guerrero; L. Porta; J. Prieto
	Title	Va-ID: A trust-based virtual assessment system			Type	Conference Article
	Year	2014	Publication	6th International Conference on Intelligent Networking and Collaborative Systems	Abbreviated Journal
	Volume		Issue		Pages	328 - 335
	Keywords
	Abstract	Even though online education is a very important pillar of lifelong education, institutions are still reluctant to wager for a fully online educational model. At the end, they keep relying on on-site assessment systems, mainly because fully virtual alternatives do not have the deserved social recognition or credibility. Thus, the design of virtual assessment systems that are able to provide effective proof of student authenticity and authorship and the integrity of the activities in a scalable and cost efficient manner would be very helpful. This paper presents ValID, a virtual assessment approach based on a continuous trust level evaluation between students and the institution. The current trust level serves as the main mechanism to dynamically decide which kind of controls a given student should be subjected to, across different courses in a degree. The main goal is providing a fair trade-off between security, scalability and cost, while maintaining the perceived quality of the educational model.
	Address	Salerna; Italy; 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	978-1-4799-6386-7	Medium
	Area		Expedition		Conference	INCOS
	Notes	OR; HuPBA;MV			Approved	no
	Call Number	Admin @ si @ ABB2014			Serial	2620
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	Author	Eduardo Aguilar; Petia Radeva
	Title	Class-Conditional Data Augmentation Applied to Image Classification			Type	Conference Article
	Year	2019	Publication	18th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	11679	Issue		Pages	182-192
	Keywords	CNNs; Data augmentation; Deep learning; Epistemic uncertainty; Image classification; Food recognition
	Abstract	Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty.
	Address	Salermo; Italy; September 2019
	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	CAIP
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ AgR2019			Serial	3366
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	Author	Estefania Talavera; Nicolai Petkov; Petia Radeva
	Title	Unsupervised Routine Discovery in Egocentric Photo-Streams			Type	Conference Article
	Year	2019	Publication	18th International Conference on Computer Analysis of Images and Patterns	Abbreviated Journal
	Volume	11678	Issue		Pages	576-588
	Keywords	Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis
	Abstract	The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people.
	Address	Salermo; Italy; September 2019
	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	CAIP
	Notes	MILAB; no proj			Approved	no
	Call Number	Admin @ si @ TPR2019a			Serial	3367
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	Author	Xavier Otazu; Maria Vanrell
	Title	Several lightness induction effects with a computational multiresolution wavelet framework			Type	Journal
	Year	2006	Publication	29th European Conference on Visual Perception (ECVP’06), Perception Suppl s, 32: 56–56	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Saint-Petersburg (Russia)
	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			Approved	no
	Call Number	CAT @ cat @ OtV2006			Serial	659
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	Author	V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich
	Title	Pattern Recognition of Fragmented Objects with Adaptive Correlation Filters			Type	Miscellaneous
	Year	2006	Publication	Topical Meeting on Optoinformatics / Information Photonics, 150–151	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Saint-Petersburg (Russia)
	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			Approved	no
	Call Number	ISE @ ise @ KMA2006b			Serial	674
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	Author	Mikhail Mozerov; V. Kober; I.A. Ovseyevich
	Title	A Stereo Matching Algorithm with Global Smoothness Criterion			Type	Miscellaneous
	Year	2006	Publication	Topical Meeting on Optoinformatics / Information Photonics, 133–135	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Saint-Petersburg (Russia)
	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			Approved	no
	Call Number	ISE @ ise @ MKO2006			Serial	675
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