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Gemma Sanchez; Alicia Fornes; Joan Mas; Josep Llados |
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Computer Vision Tools for Visually Impaired Children Learning |
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2007 |
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DAG |
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DAG @ dag @ SFM2007b |
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892 |
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Author |
Jorge Bernal; David Vazquez (eds) |
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Title |
Computer vision Trends and Challenges |
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2013 |
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Computer vision Trends and Challenges |
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CVCRD; Computer Vision |
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This book contains the papers presented at the Eighth CVC Workshop on Computer Vision Trends and Challenges (CVCR&D'2013). The workshop was held at the Computer Vision Center (Universitat Autònoma de Barcelona), the October 25th, 2013. The CVC workshops provide an excellent opportunity for young researchers and project engineers to share new ideas and knowledge about the progress of their work, and also, to discuss about challenges and future perspectives. In addition, the workshop is the welcome event for new people that recently have joined the institute.
The program of CVCR&D is organized in a single-track single-day workshop. It comprises several sessions dedicated to specific topics. For each session, a doctor working on the topic introduces the general research lines. The PhD students expose their specific research. A poster session will be held for open questions. Session topics cover the current research lines and development projects of the CVC: Medical Imaging, Medical Imaging, Color & Texture Analysis, Object Recognition, Image Sequence Evaluation, Advanced Driver Assistance Systems, Machine Vision, Document Analysis, Pattern Recognition and Applications. We want to thank all paper authors and Program Committee members. Their contribution shows that the CVC has a dynamic, active, and promising scientific community.
We hope you all enjoy this Eighth workshop and we are looking forward to meeting you and new people next year in the Ninth CVCR&D. |
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Jorge Bernal; David Vazquez |
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978-84-940902-2-6 |
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ADAS @ adas @ BeV2013 |
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2339 |
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Author |
Debora Gil; Jordi Gonzalez; Gemma Sanchez (eds) |
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Title |
Computer Vision: Advances in Research and Development |
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2007 |
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Proceedings of the 2nd CVC International Workshop |
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UAB |
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Bellaterra (Spain) |
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Debora Gil; Jordi Gonzalez; Gemma Sanchez |
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2 |
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978-84-935251-4-9 |
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IAM; ISE; DAG |
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IAM @ iam @ GGS2007 |
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1493 |
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Author |
Josep Llados |
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Title |
Computer Vision: Progress of Research and Development |
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2006 |
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1st CVC Internal Workshop Computer Vision: Progress of Research and Development, |
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J. Llados (ed.), |
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84-933652-8-9 |
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CVCRD |
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DAG |
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no |
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DAG @ dag @ Lla2006b |
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766 |
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Author |
Francesco Ciompi; Simone Balocco; Juan Rigla; Xavier Carrillo; Josefina Mauri; Petia Radeva |
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Title |
Computer-Aided Detection of Intra-Coronary Stent in Intravascular Ultrasound Sequences |
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Journal Article |
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2016 |
Publication |
Medical Physics |
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MP |
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43 |
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10 |
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Abstract |
Purpose: An intraluminal coronary stent is a metal mesh tube deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI), in order to prevent acute vessel occlusion. The identi cation of struts location and the de nition of the stent shape are relevant for PCI planning 15 and for patient follow-up. We present a fully-automatic framework for Computer-Aided Detection
(CAD) of intra-coronary stents in Intravascular Ultrasound (IVUS) image sequences. The CAD system is able to detect stent struts and estimate the stent shape.
Methods: The proposed CAD uses machine learning to provide a comprehensive interpretation of the local structure of the vessel by means of semantic classi cation. The output of the classi cation 20 stage is then used to detect struts and to estimate the stent shape. The proposed approach is validated using a multi-centric data-set of 1,015 images from 107 IVUS sequences containing both metallic and bio-absorbable stents.
Results: The method was able to detect structs in both metallic stents with an overall F-measure of 77.7% and a mean distance of 0.15 mm from manually annotated struts, and in bio-absorbable 25 stents with an overall F-measure of 77.4% and a mean distance of 0.09 mm from manually annotated struts.
Conclusions: The results are close to the inter-observer variability and suggest that the system has the potential of being used as method for aiding percutaneous interventions. |
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MILAB |
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no |
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Call Number |
Admin @ si @ CBR2016 |
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2819 |
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Author |
Cristina Sanchez Montes; F. Javier Sanchez; Jorge Bernal; Henry Cordova; Maria Lopez Ceron; Miriam Cuatrecasas; Cristina Rodriguez de Miguel; Ana Garcia Rodriguez; Rodrigo Garces Duran; Maria Pellise; Josep Llach; Gloria Fernandez Esparrach |
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Title |
Computer-aided Prediction of Polyp Histology on White-Light Colonoscopy using Surface Pattern Analysis |
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Journal Article |
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Year |
2019 |
Publication |
Endoscopy |
Abbreviated Journal |
END |
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51 |
Issue |
3 |
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261-265 |
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Background and study aims: To evaluate a new computational histology prediction system based on colorectal polyp textural surface patterns using high definition white light images.
Patients and methods: Textural elements (textons) were characterized according to their contrast with respect to the surface, shape and number of bifurcations, assuming that dysplastic polyps are associated with highly contrasted, large tubular patterns with some degree of bifurcation. Computer-aided diagnosis (CAD) was compared with pathological diagnosis and the diagnosis by the endoscopists using Kudo and NICE classification.
Results: Images of 225 polyps were evaluated (142 dysplastic and 83 non-dysplastic). CAD system correctly classified 205 (91.1%) polyps, 131/142 (92.3%) dysplastic and 74/83 (89.2%) non-dysplastic. For the subgroup of 100 diminutive (<5 mm) polyps, CAD correctly classified 87 (87%) polyps, 43/50 (86%) dysplastic and 44/50 (88%) non-dysplastic. There were not statistically significant differences in polyp histology prediction based on CAD system and on endoscopist assessment.
Conclusion: A computer vision system based on the characterization of the polyp surface in the white light accurately predicts colorectal polyp histology. |
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Notes |
MV; 600.096; 600.119; 600.075 |
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no |
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Call Number |
Admin @ si @ SSB2019 |
Serial |
3164 |
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Author |
Simone Balocco; Maria Zuluaga; Guillaume Zahnd; Su-Lin Lee; Stefanie Demirci |
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Title |
Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting |
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2016 |
Publication |
Computing and Visualization for Intravascular Imaging and Computer-Assisted Stenting |
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Elsevier |
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9780128110188 |
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MILAB |
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no |
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Call Number |
Admin @ si @ BZZ2016 |
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2821 |
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Author |
Frederic Sampedro; Anna Domenech; Sergio Escalera; Ignasi Carrio |
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Title |
Computing quantitative indicators of structural renal damage in pediatric DMSA scans |
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Journal Article |
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Year |
2017 |
Publication |
Revista Española de Medicina Nuclear e Imagen Molecular |
Abbreviated Journal |
REMNIM |
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36 |
Issue |
2 |
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72-77 |
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Abstract |
OBJECTIVES:
The proposal and implementation of a computational framework for the quantification of structural renal damage from 99mTc-dimercaptosuccinic acid (DMSA) scans. The aim of this work is to propose, implement, and validate a computational framework for the quantification of structural renal damage from DMSA scans and in an observer-independent manner.
MATERIALS AND METHODS:
From a set of 16 pediatric DMSA-positive scans and 16 matched controls and using both expert-guided and automatic approaches, a set of image-derived quantitative indicators was computed based on the relative size, intensity and histogram distribution of the lesion. A correlation analysis was conducted in order to investigate the association of these indicators with other clinical data of interest in this scenario, including C-reactive protein (CRP), white cell count, vesicoureteral reflux, fever, relative perfusion, and the presence of renal sequelae in a 6-month follow-up DMSA scan.
RESULTS:
A fully automatic lesion detection and segmentation system was able to successfully classify DMSA-positive from negative scans (AUC=0.92, sensitivity=81% and specificity=94%). The image-computed relative size of the lesion correlated with the presence of fever and CRP levels (p<0.05), and a measurement derived from the distribution histogram of the lesion obtained significant performance results in the detection of permanent renal damage (AUC=0.86, sensitivity=100% and specificity=75%).
CONCLUSIONS:
The proposal and implementation of a computational framework for the quantification of structural renal damage from DMSA scans showed a promising potential to complement visual diagnosis and non-imaging indicators. |
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HuPBA;MILAB; no menciona |
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no |
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Call Number |
Admin @ si @ SDE2017 |
Serial |
2842 |
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Author |
E. Pastor; A. Agueda; Juan Andrade; M. Muñoz; Y. Perez; E. Planas |
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Computing the rate of spread of linear flame fronts by thermal image processing |
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Journal |
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2006 |
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Fire Safety Journal, 41(8):569–579 |
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no |
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Admin @ si @ PAA2006 |
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679 |
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Author |
Ciprian Corneanu; Sergio Escalera; Aleix M. Martinez |
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Title |
Computing the Testing Error Without a Testing Set |
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Conference Article |
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2020 |
Publication |
33rd IEEE Conference on Computer Vision and Pattern Recognition |
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Abstract |
Oral. Paper award nominee.
Deep Neural Networks (DNNs) have revolutionized computer vision. We now have DNNs that achieve top (performance) results in many problems, including object recognition, facial expression analysis, and semantic segmentation, to name but a few. The design of the DNNs that achieve top results is, however, non-trivial and mostly done by trailand-error. That is, typically, researchers will derive many DNN architectures (i.e., topologies) and then test them on multiple datasets. However, there are no guarantees that the selected DNN will perform well in the real world. One can use a testing set to estimate the performance gap between the training and testing sets, but avoiding overfitting-to-thetesting-data is almost impossible. Using a sequestered testing dataset may address this problem, but this requires a constant update of the dataset, a very expensive venture. Here, we derive an algorithm to estimate the performance gap between training and testing that does not require any testing dataset. Specifically, we derive a number of persistent topology measures that identify when a DNN is learning to generalize to unseen samples. This allows us to compute the DNN’s testing error on unseen samples, even when we do not have access to them. We provide extensive experimental validation on multiple networks and datasets to demonstrate the feasibility of the proposed approach. |
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Virtual CVPR |
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HuPBA; no proj |
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no |
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Admin @ si @ CEM2020 |
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3437 |
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Author |
Sezer Karaoglu; Jan van Gemert; Theo Gevers |
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Con-text: text detection using background connectivity for fine-grained object classification |
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2013 |
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21ST ACM International Conference on Multimedia |
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757-760 |
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ALTRES;ISE |
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Admin @ si @ KGG2013 |
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2369 |
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Author |
Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom |
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Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains |
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2015 |
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International Conference on Intelligent Robots and Systems |
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2488 - 2495 |
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Visual Learning; Computer Vision; Autonomous Agents |
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In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks. |
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Hamburg; Germany; October 2015 |
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IROS |
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ADAS; 600.076 |
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Admin @ si @ OSL2015 |
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2664 |
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Author |
Francesco Ciompi; Oriol Pujol; E Fernandez-Nofrerias; J. Mauri; Petia Radeva |
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Conditional Random Fields for image segmentation in Intravascular Ultrasound |
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Conference Article |
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2010 |
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Medical Image Computing in Catalunya: Graduate Student Workshop |
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13–14 |
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We present a Conditional Random Fields based approach for segmenting Intravascular Ultrasond (IVUS) images. The presented method uses a contextual discriminative graphical model to deal with the presence of distorsions and artifacts in IVUS images, that turns the segmentation of interesting regions into a difficult task. An accurate lumen segmentation on IVUS longitudinal images is achieved. |
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Girona |
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MICCAT |
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MILAB;HUPBA |
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BCNPCL @ bcnpcl @ CPF2010 |
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1453 |
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Author |
Patricia Marquez |
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Conditions Ensuring Accuracy of Local Optical Flow Schemes |
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Report |
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2010 |
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CVC Tehcnical Report |
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157 |
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Accurate computation of optical flow is a key-point in many image processing fields. Detection of anomalous and unpredicted agents (such as pedestrians, bikers or cars) in urban scenes or pathology discrimination in medical imaging sequences, to mention just a two. The above kinds sequences present two main difficulties for standard optical flow techniques. On one hand, variability in acquisition conditions (illuminance, medical imaging modality, ...) force an alterantive representation for images fulfilling the britghtness constancy constrain. On the hand, current variational schemes produce oversmoothed fields unable to properly model discontinuous behaviours such as collisions or functionless pathological areas. This master project explores the abilities and limitations of local and global optical flow approaches. The master student will put especial emphasis in the theoretical grounds behind in order to design a variational framework combining the theoretical advantages of the considered techniques. In particular an optical flow based on Gabor phase tracking (developed in the group for medical imaging) will be generalized to urban scenes. |
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Master's thesis |
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Bellaterra 08193, Barcelona, Spain |
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IAM; |
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IAM @ iam @ Mar2010 |
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1582 |
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Author |
Francisco Javier Orozco; Jordi Gonzalez |
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Title |
Confidence Assessment on Eyelid and Eyebrow Expression Recognition |
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Conference Article |
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2008 |
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2008 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008) |
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Amsterdam (Holanda) |
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ISE |
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ISE @ ise @ OrG2008 |
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1111 |
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