| |
Records |
Links |
|
Author |
Ferran Poveda; Enric Marti; Debora Gil; Francesc Carreras; Manel Ballester |
|
| |
Title |
Helical Structure of Ventricular Anatomy by Diffusion Tensor Cardiac MR Tractography |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
Journal of American College of Cardiology |
Abbreviated Journal |
JACC |
|
| |
Volume |
5 |
Issue  |
7 |
Pages |
754-755 |
|
| |
Keywords |
|
|
| |
Abstract |
It is widely accepted that myocardial fiber architecture plays a critical role in myocardial contractility and relaxation (1). However, there is a lack of consensus about the distribution of the myocardial fibers and their spatial arrangement in the left and right ventricles. An understanding of the cardiac architecture should benefit the ventricular functional assessment, left ventricular reconstructive surgery planning, or resynchronization therapy in heart failure. Researchers have proposed several conceptual models to describe the architecture of the heart, ranging from gross dissection to histological presentation. The cardiac mesh model (2) proposes that the myocytes are arranged longitudinally and radially change their angulation along the myocardial depth. By contrast, the helical ventricular myocardial model states that the ventricular myocardium is a continuous anatomical helical layout of myocardial fibers (1 |
|
| |
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 |
1936-878X |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
IAM |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ PMG2012 |
Serial |
1985 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell |
|
| |
Title |
Spectral sharpening by spherical sampling |
Type |
Journal Article |
| |
Year |
2012 |
Publication |
Journal of the Optical Society of America A |
Abbreviated Journal |
JOSA A |
|
| |
Volume |
29 |
Issue |
7 |
Pages |
1199-1210 |
|
| |
Keywords |
|
|
| |
Abstract |
There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art. |
|
| |
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 |
1084-7529 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
CIC |
Approved |
no |
|
| |
Call Number |
Admin @ si @ FVS2012 |
Serial |
2000 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Albert Clapes; Miguel Reyes; Sergio Escalera |
|
| |
Title |
Multi-modal User Identification and Object Recognition Surveillance System |
Type |
Journal Article |
| |
Year |
2013 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
34 |
Issue |
7 |
Pages |
799-808 |
|
| |
Keywords |
Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning |
|
| |
Abstract |
We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Elsevier |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA; 600.046; 605.203;MILAB |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CRE2013 |
Serial |
2248 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Svebor Karaman; Andrew Bagdanov; Lea Landucci; Gianpaolo D'Amico; Andrea Ferracani; Daniele Pezzatini; Alberto del Bimbo |
|
| |
Title |
Personalized multimedia content delivery on an interactive table by passive observation of museum visitors |
Type |
Journal Article |
| |
Year |
2016 |
Publication |
Multimedia Tools and Applications |
Abbreviated Journal |
MTAP |
|
| |
Volume |
75 |
Issue |
7 |
Pages |
3787-3811 |
|
| |
Keywords |
Computer vision; Video surveillance; Cultural heritage; Multimedia museum; Personalization; Natural interaction; Passive profiling |
|
| |
Abstract |
The amount of multimedia data collected in museum databases is growing fast, while the capacity of museums to display information to visitors is acutely limited by physical space. Museums must seek the perfect balance of information given on individual pieces in order to provide sufficient information to aid visitor understanding while maintaining sparse usage of the walls and guaranteeing high appreciation of the exhibit. Moreover, museums often target the interests of average visitors instead of the entire spectrum of different interests each individual visitor might have. Finally, visiting a museum should not be an experience contained in the physical space of the museum but a door opened onto a broader context of related artworks, authors, artistic trends, etc. In this paper we describe the MNEMOSYNE system that attempts to address these issues through a new multimedia museum experience. Based on passive observation, the system builds a profile of the artworks of interest for each visitor. These profiles of interest are then used to drive an interactive table that personalizes multimedia content delivery. The natural user interface on the interactive table uses the visitor’s profile, an ontology of museum content and a recommendation system to personalize exploration of multimedia content. At the end of their visit, the visitor can take home a personalized summary of their visit on a custom mobile application. In this article we describe in detail each component of our approach as well as the first field trials of our prototype system built and deployed at our permanent exhibition space at LeMurate (http://www.lemurate.comune.fi.it/lemurate/) in Florence together with the first results of the evaluation process during the official installation in the National Museum of Bargello (http://www.uffizi.firenze.it/musei/?m=bargello). |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Springer US |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1380-7501 |
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
LAMP; 601.240; 600.079 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ KBL2016 |
Serial |
2520 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli |
|
| |
Title |
Recurrent Neural Networks for Remote Sensing Image Classification |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
IET Computer Vision |
Abbreviated Journal |
IETCV |
|
| |
Volume |
12 |
Issue |
7 |
Pages |
1040 - 1045 |
|
| |
Keywords |
|
|
| |
Abstract |
Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA; no proj |
Approved |
no |
|
| |
Call Number |
Admin @ si @ LÇE2018 |
Serial |
3119 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Xavier Soria; Angel Sappa; Riad I. Hammoud |
|
| |
Title |
Wide-Band Color Imagery Restoration for RGB-NIR Single Sensor Images |
Type |
Journal Article |
| |
Year |
2018 |
Publication |
Sensors |
Abbreviated Journal |
SENS |
|
| |
Volume |
18 |
Issue |
7 |
Pages |
2059 |
|
| |
Keywords |
RGB-NIR sensor; multispectral imaging; deep learning; CNNs |
|
| |
Abstract |
Multi-spectral RGB-NIR sensors have become ubiquitous in recent years. These sensors allow the visible and near-infrared spectral bands of a given scene to be captured at the same time. With such cameras, the acquired imagery has a compromised RGB color representation due to near-infrared bands (700–1100 nm) cross-talking with the visible bands (400–700 nm).
This paper proposes two deep learning-based architectures to recover the full RGB color images, thus removing the NIR information from the visible bands. The proposed approaches directly restore the high-resolution RGB image by means of convolutional neural networks. They are evaluated with several outdoor images; both architectures reach a similar performance when evaluated in different
scenarios and using different similarity metrics. Both of them improve the state of the art approaches. |
|
| |
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; MSIAU; 600.086; 600.130; 600.122; 600.118 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ SSH2018 |
Serial |
3145 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Diana Ramirez Cifuentes; Ana Freire; Ricardo Baeza Yates; Joaquim Punti Vidal; Pilar Medina Bravo; Diego Velazquez; Josep M. Gonfaus; Jordi Gonzalez |
|
| |
Title |
Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis |
Type |
Journal Article |
| |
Year |
2020 |
Publication |
Journal of Medical Internet Research |
Abbreviated Journal |
JMIR |
|
| |
Volume |
22 |
Issue |
7 |
Pages |
e17758 |
|
| |
Keywords |
|
|
| |
Abstract |
Background:
Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users.
Objective:
This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk.
Methods:
We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group).
Results:
We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users.
Conclusions:
The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE; 600.098; 600.119 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RFB2020 |
Serial |
3552 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Diana Ramirez Cifuentes; Ana Freire; Ricardo Baeza Yates; Nadia Sanz Lamora; Aida Alvarez; Alexandre Gonzalez; Meritxell Lozano; Roger Llobet; Diego Velazquez; Josep M. Gonfaus; Jordi Gonzalez |
|
| |
Title |
Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
Journal of Medical Internet Research |
Abbreviated Journal |
JMIR |
|
| |
Volume |
23 |
Issue |
7 |
Pages |
e25925 |
|
| |
Keywords |
|
|
| |
Abstract |
Background: Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia nervosa (AN) is defined as the belief of being overweight despite being dangerously underweight. The psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can manifest on social media, wherein both harmful and beneficial content is shared daily. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
ISE |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RFB2021 |
Serial |
3665 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Zhen Xu; Sergio Escalera; Adrien Pavao; Magali Richard; Wei-Wei Tu; Quanming Yao; Huan Zhao; Isabelle Guyon |
|
| |
Title |
Codabench: Flexible, easy-to-use, and reproducible meta-benchmark platform |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
Patterns |
Abbreviated Journal |
PATTERNS |
|
| |
Volume |
3 |
Issue |
7 |
Pages |
100543 |
|
| |
Keywords |
Machine learning; data science; benchmark platform; reproducibility; competitions |
|
| |
Abstract |
Obtaining a standardized benchmark of computational methods is a major issue in data-science communities. Dedicated frameworks enabling fair benchmarking in a unified environment are yet to be developed. Here, we introduce Codabench, a meta-benchmark platform that is open sourced and community driven for benchmarking algorithms or software agents versus datasets or tasks. A public instance of Codabench is open to everyone free of charge and allows benchmark organizers to fairly compare submissions under the same setting (software, hardware, data, algorithms), with custom protocols and data formats. Codabench has unique features facilitating easy organization of flexible and reproducible benchmarks, such as the possibility of reusing templates of benchmarks and supplying compute resources on demand. Codabench has been used internally and externally on various applications, receiving more than 130 users and 2,500 submissions. As illustrative use cases, we introduce four diverse benchmarks covering graph machine learning, cancer heterogeneity, clinical diagnosis, and reinforcement learning. |
|
| |
Address |
June 24, 2022 |
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Science Direct |
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HuPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ XEP2022 |
Serial |
3764 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Carlos Martin Isla; Victor M Campello; Cristian Izquierdo; Kaisar Kushibar; Carla Sendra Balcells; Polyxeni Gkontra; Alireza Sojoudi; Mitchell J Fulton; Tewodros Weldebirhan Arega; Kumaradevan Punithakumar; Lei Li; Xiaowu Sun; Yasmina Al Khalil; Di Liu; Sana Jabbar; Sandro Queiros; Francesco Galati; Moona Mazher; Zheyao Gao; Marcel Beetz; Lennart Tautz; Christoforos Galazis; Marta Varela; Markus Hullebrand; Vicente Grau; Xiahai Zhuang; Domenec Puig; Maria A Zuluaga; Hassan Mohy Ud Din; Dimitris Metaxas; Marcel Breeuwer; Rob J van der Geest; Michelle Noga; Stephanie Bricq; Mark E Rentschler; Andrea Guala; Steffen E Petersen; Sergio Escalera; Jose F Rodriguez Palomares; Karim Lekadir |
|
| |
Title |
Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&ms Challenge |
Type |
Journal Article |
| |
Year |
2023 |
Publication |
IEEE Journal of Biomedical and Health Informatics |
Abbreviated Journal |
JBHI |
|
| |
Volume |
27 |
Issue |
7 |
Pages |
3302-3313 |
|
| |
Keywords |
|
|
| |
Abstract |
In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Summary Language |
|
Original Title |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Medium |
|
|
| |
Area |
|
Expedition |
|
Conference |
|
|
| |
Notes |
HUPBA |
Approved |
no |
|
| |
Call Number |
Admin @ si @ MCI2023 |
Serial |
3880 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Antoni Rosell; Sonia Baeza; S. Garcia-Reina; JL. Mate; Ignasi Guasch; I. Nogueira; I. Garcia-Olive; Guillermo Torres; Carles Sanchez; Debora Gil |
|
| |
Title |
Radiomics to increase the effectiveness of lung cancer screening programs. Radiolung preliminary results. |
Type |
Journal Article |
| |
Year |
2022 |
Publication |
European Respiratory Journal |
Abbreviated Journal |
ERJ |
|
| |
Volume |
60 |
Issue |
66 |
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 |
IAM |
Approved |
no |
|
| |
Call Number |
Admin @ si @ RBG2022c |
Serial |
3835 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Jaume Garcia |
|
| |
Title |
Propagacio de fronts per a la segmentacio en imatges IVUS |
Type |
Report |
| |
Year |
2002 |
Publication |
Technical Report |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
65 |
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
CVC (UAB) |
|
| |
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 |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ Gar2002 |
Serial |
328 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Debora Gil |
|
| |
Title |
Regularized Curvature Flow |
Type |
Report |
| |
Year |
2002 |
Publication |
CVC Technical Report |
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
63 |
Pages |
|
|
| |
Keywords |
|
|
| |
Abstract |
|
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
Computer Vision Centre |
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; |
Approved |
no |
|
| |
Call Number |
IAM @ iam @ Gil2002 |
Serial |
1518 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva |
|
| |
Title |
Topological principal component analysis for face encoding and recognition |
Type |
Journal Article |
| |
Year |
2001 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
|
| |
Volume |
22 |
Issue |
6-7 |
Pages |
769–776 |
|
| |
Keywords |
|
|
| |
Abstract |
IF: 0.552 |
|
| |
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;OR;MV |
Approved |
no |
|
| |
Call Number |
ADAS @ adas @ PVL2001 |
Serial |
155 |
|
| Permanent link to this record |
| |
|
| |
|
Author |
Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío |
|
| |
Title |
Matching visual induction effects on screens of different size |
Type |
Journal Article |
| |
Year |
2021 |
Publication |
Journal of Vision |
Abbreviated Journal |
JOV |
|
| |
Volume |
21 |
Issue |
6(10) |
Pages |
1-22 |
|
| |
Keywords |
|
|
| |
Abstract |
In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images. |
|
| |
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 |
Approved |
no |
|
| |
Call Number |
Admin @ si @ CVM2021 |
Serial |
3595 |
|
| Permanent link to this record |
