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Author Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez
Title (down) 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 (down) 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 Jialuo Chen; Mohamed Ali Souibgui; Alicia Fornes; Beata Megyesi
Title (down) 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 Lei Kang; Marçal Rusiñol; Alicia Fornes; Pau Riba; Mauricio Villegas
Title (down) 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 Juan Andrade; T. Alejandra Vidal; A. Sanfeliu
Title (down) 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 Carlo Gatta; Adriana Romero; Joost Van de Weijer
Title (down) 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 Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
Title (down) 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 Kaida Xiao; Chenyang Fu; D.Mylonas; Dimosthenis Karatzas; S. Wuerger
Title (down) 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 Kaida Xiao; Sophie Wuerger; Chenyang Fu; Dimosthenis Karatzas
Title (down) Unique Hue Data for Colour Appearance Models. Part i: Loci of Unique Hues and Hue Uniformity Type Journal Article
Year 2011 Publication Color Research & Application Abbreviated Journal CRA
Volume 36 Issue 5 Pages 316-323
Keywords unique hues; colour appearance models; CIECAM02; hue uniformity
Abstract Psychophysical experiments were conducted to assess unique hues on a CRT display for a large sample of colour-normal observers (n 1⁄4 185). These data were then used to evaluate the most commonly used colour appear- ance model, CIECAM02, by transforming the CIEXYZ tris- timulus values of the unique hues to the CIECAM02 colour appearance attributes, lightness, chroma and hue angle. We report two findings: (1) the hue angles derived from our unique hue data are inconsistent with the commonly used Natural Color System hues that are incorporated in the CIECAM02 model. We argue that our predicted unique hue angles (derived from our large dataset) provide a more reliable standard for colour management applications when the precise specification of these salient colours is im- portant. (2) We test hue uniformity for CIECAM02 in all four unique hues and show significant disagreements for all hues, except for unique red which seems to be invariant under lightness changes. Our dataset is useful to improve the CIECAM02 model as it provides reliable data for benchmarking.
Address
Corporate Author Thesis
Publisher Wiley Periodicals Inc 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 @ XWF2011 Serial 1816
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Author Xavier Perez Sala; Laura Igual; Sergio Escalera; Cecilio Angulo
Title (down) Uniform Sampling of Rotations for Discrete and Continuous Learning of 2D Shape Models Type Book Chapter
Year 2012 Publication Vision Robotics: Technologies for Machine Learning and Vision Applications Abbreviated Journal
Volume Issue 2 Pages 23-42
Keywords
Abstract Different methodologies of uniform sampling over the rotation group, SO(3), for building unbiased 2D shape models from 3D objects are introduced and reviewed in this chapter. State-of-the-art non uniform sampling approaches are discussed, and uniform sampling methods using Euler angles and quaternions are introduced. Moreover, since presented work is oriented to model building applications, it is not limited to general discrete methods to obtain uniform 3D rotations, but also from a continuous point of view in the case of Procrustes Analysis.
Address
Corporate Author Thesis
Publisher IGI-Global 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;HuPBA Approved no
Call Number Admin @ si @ PIE2012 Serial 2064
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Author Soumya Jahagirdar; Minesh Mathew; Dimosthenis Karatzas; CV Jawahar
Title (down) Understanding Video Scenes Through Text: Insights from Text-Based Video Question Answering Type Conference Article
Year 2023 Publication Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Researchers have extensively studied the field of vision and language, discovering that both visual and textual content is crucial for understanding scenes effectively. Particularly, comprehending text in videos holds great significance, requiring both scene text understanding and temporal reasoning. This paper focuses on exploring two recently introduced datasets, NewsVideoQA and M4-ViteVQA, which aim to address video question answering based on textual content. The NewsVideoQA dataset contains question-answer pairs related to the text in news videos, while M4- ViteVQA comprises question-answer pairs from diverse categories like vlogging, traveling, and shopping. We provide an analysis of the formulation of these datasets on various levels, exploring the degree of visual understanding and multi-frame comprehension required for answering the questions. Additionally, the study includes experimentation with BERT-QA, a text-only model, which demonstrates comparable performance to the original methods on both datasets, indicating the shortcomings in the formulation of these datasets. Furthermore, we also look into the domain adaptation aspect by examining the effectiveness of training on M4-ViteVQA and evaluating on NewsVideoQA and vice-versa, thereby shedding light on the challenges and potential benefits of out-of-domain training.
Address Paris; France; October 2023
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 ICCVW
Notes DAG Approved no
Call Number Admin @ si @ JMK2023 Serial 3946
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Author Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias
Title (down) Understanding trained CNNs by indexing neuron selectivity Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 136 Issue Pages 318-325
Keywords
Abstract The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.
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; 600.087; 600.140; 600.118 Approved no
Call Number Admin @ si @ RVL2019 Serial 3310
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Author Jose Manuel Alvarez; Felipe Lumbreras; Antonio Lopez; Theo Gevers
Title (down) Understanding Road Scenes using Visual Cues Type Miscellaneous
Year 2012 Publication European Conference on Computer Vision Abbreviated Journal
Volume Issue Pages
Keywords
Abstract DEMO
Address Florence; Italy
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 Admin @ si @ ALL2012 Serial 2795
Permanent link to this record
 
 
Author Carles Fernandez
Title (down) Understanding Image Sequences: the Role of Ontologies in Cognitive Vision Type Book Whole
Year 2010 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The increasing ubiquitousness of digital information in our daily lives has positioned
video as a favored information vehicle, and given rise to an astonishing generation of
social media and surveillance footage. This raises a series of technological demands
for automatic video understanding and management, which together with the compromising attentional limitations of human operators, have motivated the research
community to guide its steps towards a better attainment of such capabilities. As
a result, current trends on cognitive vision promise to recognize complex events and
self-adapt to different environments, while managing and integrating several types of
knowledge. Future directions suggest to reinforce the multi-modal fusion of information sources and the communication with end-users.
In this thesis we tackle the problem of recognizing and describing meaningful
events in video sequences from different domains, and communicating the resulting
knowledge to end-users by means of advanced interfaces for human–computer interaction. This problem is addressed by designing the high-level modules of a cognitive
vision framework exploiting ontological knowledge. Ontologies allow us to define the
relevant concepts in a domain and the relationships among them; we prove that the
use of ontologies to organize, centralize, link, and reuse different types of knowledge
is a key factor in the materialization of our objectives.
The proposed framework contributes to: (i) automatically learn the characteristics
of different scenarios in a domain; (ii) reason about uncertain, incomplete, or vague
information from visual –camera’s– or linguistic –end-user’s– inputs; (iii) derive plausible interpretations of complex events from basic spatiotemporal developments; (iv)
facilitate natural interfaces that adapt to the needs of end-users, and allow them to
communicate efficiently with the system at different levels of interaction; and finally,
(v) find mechanisms to guide modeling processes, maintain and extend the resulting
models, and to exploit multimodal resources synergically to enhance the former tasks.
We describe a holistic methodology to achieve these goals. First, the use of prior
taxonomical knowledge is proved useful to guide MAP-MRF inference processes in
the automatic identification of semantic regions, with independence of a particular scenario. Towards the recognition of complex video events, we combine fuzzy
metric-temporal reasoning with SGTs, thus assessing high-level interpretations from
spatiotemporal data. Here, ontological resources like T–Boxes, onomasticons, or factual databases become useful to derive video indexing and retrieval capabilities, and
also to forward highlighted content to smart user interfaces. There, we explore the
application of ontologies to discourse analysis and cognitive linguistic principles, or scene augmentation techniques towards advanced communication by means of natural language dialogs and synthetic visualizations. Ontologies become fundamental to
coordinate, adapt, and reuse the different modules in the system.
The suitability of our ontological framework is demonstrated by a series of applications that especially benefit the field of smart video surveillance, viz. automatic generation of linguistic reports about the content of video sequences in multiple natural
languages; content-based filtering and summarization of these reports; dialogue-based
interfaces to query and browse video contents; automatic learning of semantic regions
in a scenario; and tools to evaluate the performance of components and models in the
system, via simulation and augmented reality.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Jordi Gonzalez;Xavier Roca
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-937261-2-6 Medium
Area Expedition Conference
Notes Approved no
Call Number Admin @ si @ Fer2010a Serial 1333
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Author David Berga
Title (down) Understanding Eye Movements: Psychophysics and a Model of Primary Visual Cortex Type Book Whole
Year 2019 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Humansmove their eyes in order to learn visual representations of the world. These eye movements depend on distinct factors, either by the scene that we perceive or by our own decisions. To select what is relevant to attend is part of our survival mechanisms and the way we build reality, as we constantly react both consciously and unconsciously to all the stimuli that is projected into our eyes. In this thesis we try to explain (1) how we move our eyes, (2) how to build machines that understand visual information and deploy eyemovements, and (3) how to make these machines understand tasks in order to decide for eye movements.
(1) We provided the analysis of eye movement behavior elicited by low-level feature distinctiveness with a dataset of 230 synthetically-generated image patterns. A total of 15 types of stimuli has been generated (e.g. orientation, brightness, color, size, etc.), with 7 feature contrasts for each feature category. Eye-tracking data was collected from 34 participants during the viewing of the dataset, using Free-Viewing and Visual Search task instructions. Results showed that saliency is predominantly and distinctively influenced by: 1. feature type, 2. feature contrast, 3. Temporality of fixations, 4. task difficulty and 5. center bias. From such dataset (SID4VAM), we have computed a benchmark of saliency models by testing performance using psychophysical patterns. Model performance has been evaluated considering model inspiration and consistency with human psychophysics. Our study reveals that state-of-the-art Deep Learning saliency models do not performwell with synthetic pattern images, instead, modelswith Spectral/Fourier inspiration outperform others in saliency metrics and are more consistent with human psychophysical experimentation.
(2) Computations in the primary visual cortex (area V1 or striate cortex) have long been hypothesized to be responsible, among several visual processing mechanisms, of bottom-up visual attention (also named saliency). In order to validate this hypothesis, images from eye tracking datasets have been processed with a biologically plausible model of V1 (named Neurodynamic SaliencyWaveletModel or NSWAM). Following Li’s neurodynamic model, we define V1’s lateral connections with a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation and scale. Early subcortical processes (i.e. retinal and thalamic) are functionally simulated. The resulting saliency maps are generated from the model output, representing the neuronal activity of V1 projections towards brain areas involved in eye movement control. We want to pinpoint that our unified computational architecture is able to reproduce several visual processes (i.e. brightness, chromatic induction and visual discomfort) without applying any type of training or optimization and keeping the same parametrization. The model has been extended (NSWAM-CM) with an implementation of the cortical magnification function to define the retinotopical projections towards V1, processing neuronal activity for each distinct view during scene observation. Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions. Results show that our model outperforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences, specifically for nature and synthetic images. We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) predict attention at distinct image contexts.
(3) Although previous scanpath models have been able to efficiently predict saccades during Free-Viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-termmemory in combination with stimulus-driven eyemovement neuronal correlates. In our latest study we proposed an extension of the Selective Tuning Attentive Reference Fixation ControllerModel based on task demands (STAR-FCT), describing novel computational definitions of Long-TermMemory, Visual Task Executive and Task Working Memory. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memorymodel by processing a visual hierarchy of low- and high-level features. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting object localizationmaps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions compared to saliency.
Address July 2019
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor Xavier Otazu
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-948531-8-0 Medium
Area Expedition Conference
Notes NEUROBIT Approved no
Call Number Admin @ si @ Ber2019 Serial 3390
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