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| Author | Ekain Artola | ||||
| Title | Human Attention Map Prediction Combining Visual Features | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume  |
160 | Issue | Pages | ||
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| Corporate Author | Thesis | Bachelor's thesis | |||
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| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | Approved | no | |||
| Call Number | Admin @ si @ Art2010 | Serial | 1352 | ||
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| Author | Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi | ||||
| Title | Few shots are all you need: A progressive learning approach for low resource handwritten text recognition | Type | Journal Article | ||
| Year | 2022 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume |
160 | Issue | Pages | 43-49 | |
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| Abstract | Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Area | Expedition | Conference | |||
| Notes | DAG; 600.121; 600.162; 602.230 | Approved | no | ||
| Call Number | Admin @ si @ SFK2022 | Serial | 3736 | ||
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| Author | David Fernandez | ||||
| Title | Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
161 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Editor | |||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Fer2010b | Serial | 1353 | ||
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| Author | Monica Piñol | ||||
| Title | Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
162 | Issue | Pages | ||
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| Address | Bellaterra (Barcelona) | ||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Editor | |||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Piñ2010 | Serial | 1936 | ||
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| Author | David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri | ||||
| Title | Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion | Type | Conference Article | ||
| Year | 2007 | Publication | Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA | Abbreviated Journal | |
| Volume |
163 | Issue | Pages | 197–204 | |
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-58603-798-7 | Medium | ||
| Area | Expedition | Conference | CCIA’07 | ||
| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ RRF2007a | Serial | 831 | ||
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| Author | Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo | ||||
| Title | Using the Average Landmark Vector Method for Robot Homing | Type | Conference Article | ||
| Year | 2007 | Publication | Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA | Abbreviated Journal | |
| Volume |
163 | Issue | Pages | 331–338 | |
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978–1–58603–798–7 | Medium | ||
| Area | Expedition | Conference | CCIA’07 | ||
| Notes | RV;ADAS | Approved | no | ||
| Call Number | Admin @ si @ GRL2007 | Serial | 899 | ||
| Permanent link to this record | |||||
| Author | Jon Almazan | ||||
| Title | Deforming the Blurred Shape Model for Shape Description and Recognition | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
163 | Issue | Pages | ||
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| Corporate Author | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Editor | |||
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| Series Editor | Series Title | Abbreviated Series Title | |||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Alm2010 | Serial | 1354 | ||
| Permanent link to this record | |||||
| Author | Sergio Vera | ||||
| Title | Finger joint modelling from hand X-ray images for assessing rheumatoid arthritis | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
164 | Issue | Pages | ||
| Keywords | Rheumatoid arthritis; joint detection; X-ray; Van der Heijde score | ||||
| Abstract | Rheumatoid arthritis is an autoimmune, systemic, inflammatory disorder that mainly af- fects bone joints. While there is no cure for this disease, continuous advances on palliative treatments require frequent verification of patient’s illness evolution. Such evolution is mea- sured through several available semi-quantitative methods that require evaluation of hand and foot X-ray images. Accurate assessment is a time consuming task that requires highly trained personnel. This hinders a generalized use in clinical practice for early diagnose and disease follow-up. In the context of the automatization of such evaluation methods we present a method for detection and characterization of finger joints in hand radiography images. Several measures for assessing the reduction of joint space width are proposed. We compare for the first time such measures to the Van der Heijde score, the gold standard method for rheumatoid arthritis assessment. The proposed method outperforms existing strategies with a detection rate above 95%. Our comparison to Van der Heijde index shows a promising correlation that encourages further research. | ||||
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| Corporate Author | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Bellaterra 01893, Barcelona, Spain | Editor | ||
| Language | Summary Language | Original Title | |||
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| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ Ver2010 | Serial | 1661 | ||
| Permanent link to this record | |||||
| Author | Razieh Rastgoo; Kourosh Kiani; Sergio Escalera | ||||
| Title | Sign Language Recognition: A Deep Survey | Type | Journal Article | ||
| Year | 2021 | Publication | Expert Systems With Applications | Abbreviated Journal | ESWA |
| Volume |
164 | Issue | Pages | 113794 | |
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| Abstract | Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ RKE2021a | Serial | 3521 | ||
| Permanent link to this record | |||||
| Author | Joan M. Nuñez | ||||
| Title | Computer vision techniques for characterization of finger joints in X-ray image | Type | Report | ||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
165 | Issue | Pages | ||
| Keywords | Rheumatoid arthritis, X-ray, Sharp Van der Heijde, joint characterization, sclerosis detection, bone detection, edge, ridge | ||||
| Abstract | Rheumatoid arthritis (RA) is an autoimmune inflammatory type of arthritis which mainly affects hands on its first stages. Though it is a chronic disease and there is no cure for it, treatments require an accurate assessment of illness evolution. Such assessment is based on evaluation of hand X-ray images by using one of the several available semi-quantitative methods. This task requires highly trained medical personnel. That is why the automation of the assessment would allow professionals to save time and effort. Two stages are involved in this task. Firstly, the joint detection, afterwards, the joint characterization. Unlike the little existing previous work, this contribution clearly separates those two stages and sets the foundations of a modular assessment system focusing on the characterization stage. A hand joint dataset is created and an accurate data analysis is achieved in order to identify relevant features. Since the sclerosis and the lower bone were decided to be the most important features, different computer vision techniques were used in order to develop a detector system for both of them. Joint space width measures are provided and their correlation with Sharp-Van der Heijde is verified | ||||
| Address | Bellaterra (Barcelona) | ||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Editor | Dr. Fernando Vilariño and Dra. Debora Gil | ||
| Language | Summary Language | Original Title | |||
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| Notes | MV;IAM; | Approved | no | ||
| Call Number | IAM @ iam @ Nuñ2011 | Serial | 1795 | ||
| Permanent link to this record | |||||
| Author | Alejandro Gonzalez Alzate | ||||
| Title | Evaluation of spatiotemporal descriptors for pedestrian detection in video sequences | Type | Report | ||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
166 | Issue | Pages | ||
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| Abstract | |||||
| Address | Bellaterra (Spain) | ||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Gon2011 | Serial | 1932 | ||
| Permanent link to this record | |||||
| Author | Yainuvis Socarras | ||||
| Title | Image segmentation for improving pedestrian detection | Type | Report | ||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
167 | Issue | Pages | ||
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| Abstract | |||||
| Address | Bellaterra (Spain) | ||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
| Publisher | Place of Publication | Editor | |||
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| Series Editor | Series Title | Abbreviated Series Title | |||
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| Notes | ADAS; | Approved | no | ||
| Call Number | Admin @ si @ Soc2011 | Serial | 1933 | ||
| Permanent link to this record | |||||
| Author | Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon | ||||
| Title | Incremental model learning for spectroscopy-based food analysis | Type | Journal Article | ||
| Year | 2017 | Publication | Chemometrics and Intelligent Laboratory Systems | Abbreviated Journal | CILS |
| Volume |
167 | Issue | Pages | 123-131 | |
| Keywords | Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy | ||||
| Abstract | In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. | ||||
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| Area | Expedition | Conference | |||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DGK2017 | Serial | 3002 | ||
| Permanent link to this record | |||||
| Author | Carles Sanchez | ||||
| Title | Tracheal ring detection in bronchoscopy | Type | Report | ||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
168 | Issue | Pages | ||
| Keywords | Bronchoscopy, tracheal ring, segmentation | ||||
| Abstract | Endoscopy is the process in which a camera is introduced inside a human.
Given that endoscopy provides realistic images (in contrast to other modalities) and allows non-invase minimal intervention procedures (which can aid in diagnosis and surgical interventions), its use has spreaded during last decades. In this project we will focus on bronchoscopic procedures, during which the camera is introduced through the trachea in order to have a diagnostic of the patient. The diagnostic interventions are focused on: degree of stenosis (reduction in tracheal area), prosthesis or early diagnosis of tumors. In the first case, assessment of the luminal area and the calculation of the diameters of the tracheal rings are required. A main limitation is that all the process is done by hand, which means that the doctor takes all the measurements and decisions just by looking at the screen. As far as we know there is no computational framework for helping the doctors in the diagnosis. This project will consist of analysing bronchoscopic videos in order to extract useful information for the diagnostic of the degree of stenosis. In particular we will focus on segmentation of the tracheal rings. As a result of this project several strategies (for detecting tracheal rings) had been implemented in order to compare their performance. |
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| Address | |||||
| Corporate Author | Thesis | Master's thesis | |||
| Publisher | Place of Publication | Editor | Debora Gil, F.Javier Sanchez | ||
| Language | english | Summary Language | english | Original Title | |
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| Notes | IAM;MV | Approved | no | ||
| Call Number | IAM @ iam @ San2011 | Serial | 1841 | ||
| Permanent link to this record | |||||
| Author | Maria del Camp Davesa | ||||
| Title | Human action categorization in image sequences | Type | Report | ||
| Year | 2011 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume |
169 | Issue | Pages | ||
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| Address | Bellaterra (Spain) | ||||
| Corporate Author | Computer Vision Center | Thesis | Master's thesis | ||
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| Notes | CiC;CIC | Approved | no | ||
| Call Number | Admin @ si @ Dav2011 | Serial | 1934 | ||
| Permanent link to this record | |||||