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	Author	Kaida Xiao; Chenyang Fu; D.Mylonas; Dimosthenis Karatzas; S. Wuerger
	Title	Unique Hue Data for Colour Appearance Models. Part ii: Chromatic Adaptation Transform			Type	Journal Article
	Year	2013	Publication	Color Research & Application	Abbreviated Journal	CRA
	Volume	38	Issue	1	Pages	22-29
	Keywords
	Abstract	Unique hue settings of 185 observers under three room-lighting conditions were used to evaluate the accuracy of full and mixed chromatic adaptation transform models of CIECAM02 in terms of unique hue reproduction. Perceptual hue shifts in CIECAM02 were evaluated for both models with no clear difference using the current Commission Internationale de l'Éclairage (CIE) recommendation for mixed chromatic adaptation ratio. Using our large dataset of unique hue data as a benchmark, an optimised parameter is proposed for chromatic adaptation under mixed illumination conditions that produces more accurate results in unique hue reproduction. © 2011 Wiley Periodicals, Inc. Col Res Appl, 2013
	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			Approved	no
	Call Number	Admin @ si @ XFM2013			Serial	1822
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	Author	Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
	Title	Unraveling the enigmas of chromosome territoriality during spermatogenesis			Type	Conference Article
	Year	2017	Publication	IX Jornada del Departament de Biologia Cel•lular, Fisiologia i Immunologia	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	UAB; Barcelona; June 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
	Notes	IAM; 600.145			Approved	no
	Call Number	Admin @ si @ SBG2017b			Serial	2959
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	Author	Carlo Gatta; Adriana Romero; Joost Van de Weijer
	Title	Unrolling loopy top-down semantic feedback in convolutional deep networks			Type	Conference Article
	Year	2014	Publication	Workshop on Deep Vision: Deep Learning for Computer Vision	Abbreviated Journal
	Volume		Issue		Pages	498-505
	Keywords
	Abstract	In this paper, we propose a novel way to perform top-down semantic feedback in convolutional deep networks for efficient and accurate image parsing. We also show how to add global appearance/semantic features, which have shown to improve image parsing performance in state-of-the-art methods, and was not present in previous convolutional approaches. The proposed method is characterised by an efficient training and a sufficiently fast testing. We use the well known SIFTflow dataset to numerically show the advantages provided by our contributions, and to compare with state-of-the-art image parsing convolutional based approaches.
	Address	Columbus; Ohio; 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	CVPRW
	Notes	LAMP; MILAB; 601.160; 600.079			Approved	no
	Call Number	Admin @ si @ GRW2014			Serial	2490
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	Author	Juan Andrade; T. Alejandra Vidal; A. Sanfeliu
	Title	Unscented transformation of vehicle states in SLAM			Type	Miscellaneous
	Year	2005	Publication	Proceedings of the IEEE International Conference on Robotics and Automation, 324–329	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Barcelona (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		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	Admin @ si @ AVS2005c			Serial	591
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	Author	Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas
	Title	Unsupervised Adaptation for Synthetic-to-Real Handwritten Word Recognition			Type	Conference Article
	Year	2020	Publication	IEEE Winter Conference on Applications of Computer Vision	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.
	Address	Aspen; Colorado; USA; March 2020
	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	WACV
	Notes	DAG; 600.129; 600.140; 601.302; 601.312; 600.121			Approved	no
	Call Number	Admin @ si @ KRF2020			Serial	3446
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	Author	Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi
	Title	Unsupervised Alphabet Matching in Historical Encrypted Manuscript Images			Type	Conference Article
	Year	2021	Publication	4th International Conference on Historical Cryptology	Abbreviated Journal
	Volume		Issue		Pages	34-37
	Keywords
	Abstract	Historical ciphers contain a wide range ofsymbols from various symbol sets. Iden-tifying the cipher alphabet is a prerequi-site before decryption can take place andis a time-consuming process. In this workwe explore the use of image processing foridentifying the underlying alphabet in ci-pher images, and to compare alphabets be-tween ciphers. The experiments show thatciphers with similar alphabets can be suc-cessfully discovered through clustering.
	Address	Virtual; September 2021
	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	HistoCrypt
	Notes	DAG; 602.230; 600.140; 600.121			Approved	no
	Call Number	Admin @ si @ CSF2021			Serial	3617
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	Author	Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez
	Title	Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies			Type	Book Chapter
	Year	2014	Publication	Graphics Recognition. Current Trends and Challenges	Abbreviated Journal
	Volume	8746	Issue		Pages	109-121
	Keywords	Graphics recognition; Floor plan analysis; Object segmentation
	Abstract	In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions.
	Address
	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	978-3-662-44853-3	Medium
	Area		Expedition		Conference
	Notes	DAG; ADAS; 600.076; 600.077			Approved	no
	Call Number	Admin @ si @ HVS2014			Serial	2535
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	Author	Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez
	Title	Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies			Type	Conference Article
	Year	2013	Publication	10th IAPR International Workshop on Graphics Recognition	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Bethlehem; PA; 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		ISBN		Medium
	Area		Expedition		Conference	GREC
	Notes	DAG			Approved	no
	Call Number	Admin @ si @ HVS2013b			Serial	2696
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	Author	Huamin Ren; Weifeng Liu; Soren Ingvor Olsen; Sergio Escalera; Thomas B. Moeslund
	Title	Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection			Type	Conference Article
	Year	2015	Publication	26th British Machine Vision Conference	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract
	Address	Swansea; uk; September 2015
	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	HuPBA;MILAB			Approved	no
	Call Number	Admin @ si @ RLO2015			Serial	2658
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	Author	Jose Carlos Rubio; Joan Serrat; Antonio Lopez
	Title	Unsupervised co-segmentation through region matching			Type	Conference Article
	Year	2012	Publication	25th IEEE Conference on Computer Vision and Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	749-756
	Keywords
	Abstract	Co-segmentation is defined as jointly partitioning multiple images depicting the same or similar object, into foreground and background. Our method consists of a multiple-scale multiple-image generative model, which jointly estimates the foreground and background appearance distributions from several images, in a non-supervised manner. In contrast to other co-segmentation methods, our approach does not require the images to have similar foregrounds and different backgrounds to function properly. Region matching is applied to exploit inter-image information by establishing correspondences between the common objects that appear in the scene. Moreover, computing many-to-many associations of regions allow further applications, like recognition of object parts across images. We report results on iCoseg, a challenging dataset that presents extreme variability in camera viewpoint, illumination and object deformations and poses. We also show that our method is robust against large intra-class variability in the MSRC database.
	Address	Providence, Rhode Island
	Corporate Author				Thesis
	Publisher	IEEE Xplore	Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1063-6919	ISBN	978-1-4673-1226-4	Medium
	Area		Expedition		Conference	CVPR
	Notes	ADAS			Approved	no
	Call Number	Admin @ si @ RSL2012b; ADAS @ adas @			Serial	2033
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	Author	Angel Sappa
	Title	Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation			Type	Journal
	Year	2006	Publication	IEEE Transactions on Image Processing, 15(2):377–384	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	ADAS			Approved	no
	Call Number	ADAS @ adas @ Sap2006a			Serial	637
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	Author	Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
	Title	Unsupervised Deep Feature Extraction for Remote Sensing Image Classification			Type	Journal Article
	Year	2016	Publication	IEEE Transaction on Geoscience and Remote Sensing	Abbreviated Journal	TGRS
	Volume	54	Issue	3	Pages	1349 - 1362
	Keywords
	Abstract	This paper introduces the use of single-layer and deep convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of greedy layerwise unsupervised pretraining coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on sparse representations and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.
	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	0196-2892	ISBN		Medium
	Area		Expedition		Conference
	Notes	LAMP; 600.079;MILAB			Approved	no
	Call Number	Admin @ si @ RGC2016			Serial	2723
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	Author	Adriana Romero; Carlo Gatta; Gustavo Camps-Valls
	Title	Unsupervised Deep Feature Extraction Of Hyperspectral Images			Type	Conference Article
	Year	2014	Publication	6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing	Abbreviated Journal
	Volume		Issue		Pages
	Keywords	Convolutional networks; deep learning; sparse learning; feature extraction; hyperspectral image classification
	Abstract	This paper presents an effective unsupervised sparse feature learning algorithm to train deep convolutional networks on hyperspectral images. Deep convolutional hierarchical representations are learned and then used for pixel classification. Features in lower layers present less abstract representations of data, while higher layers represent more abstract and complex characteristics. We successfully illustrate the performance of the extracted representations in a challenging AVIRIS hyperspectral image classification problem, compared to standard dimensionality reduction methods like principal component analysis (PCA) and its kernel counterpart (kPCA). The proposed method largely outperforms the previous state-ofthe-art results on the same experimental setting. Results show that single layer networks can extract powerful discriminative features only when the receptive field accounts for neighboring pixels. Regarding the deep architecture, we can conclude that: (1) additional layers in a deep architecture significantly improve the performance w.r.t. single layer variants; (2) the max-pooling step in each layer is mandatory to achieve satisfactory results; and (3) the performance gain w.r.t. the number of layers is upper bounded, since the spatial resolution is reduced at each pooling, resulting in too spatially coarse output features.
	Address	Lausanne; Switzerland; 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	WHISPERS
	Notes	MILAB; LAMP; 600.079			Approved	no
	Call Number	Admin @ si @ RGC2014			Serial	2513
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	Author	David Vazquez; Antonio Lopez; Daniel Ponsa
	Title	Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection			Type	Conference Article
	Year	2012	Publication	21st International Conference on Pattern Recognition	Abbreviated Journal
	Volume		Issue		Pages	3492 - 3495
	Keywords	Pedestrian Detection; Domain Adaptation; Virtual worlds
	Abstract	Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1).
	Address	Tsukuba Science City, Japan
	Corporate Author				Thesis
	Publisher	IEEE	Place of Publication	Tsukuba Science City, JAPAN	Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1051-4651	ISBN	978-1-4673-2216-4	Medium
	Area		Expedition		Conference	ICPR
	Notes	ADAS			Approved	no
	Call Number	ADAS @ adas @ VLP2012			Serial	1981
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	Author	Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz
	Title	Unsupervised Domain Adaptation without Source Data by Casting a BAIT			Type	Miscellaneous
	Year	2020	Publication	Arxiv	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	arXiv:2010.12427 Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from a labeled source domain to an unlabeled target domain. Existing UDA methods require access to source data during adaptation, which may not be feasible in some real-world applications. In this paper, we address the source-free unsupervised domain adaptation (SFUDA) problem, where only the source model is available during the adaptation. We propose a method named BAIT to address SFUDA. Specifically, given only the source model, with the source classifier head fixed, we introduce a new learnable classifier. When adapting to the target domain, class prototypes of the new added classifier will act as a bait. They will first approach the target features which deviate from prototypes of the source classifier due to domain shift. Then those target features are pulled towards the corresponding prototypes of the source classifier, thus achieving feature alignment with the source classifier in the absence of source data. Experimental results show that the proposed method achieves state-of-the-art performance on several benchmark datasets compared with existing UDA and SFUDA methods.
	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	LAMP; 600.120			Approved	no
	Call Number	Admin @ si @ YWW2020			Serial	3539
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