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Author |
Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner |
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Title |
Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation |
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Journal |
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Year |
2011 |
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e-Perimetron |
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ePER |
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Volume |
6 |
Issue |
4 |
Pages |
219-229 |
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By means of computer vision algorithms scanned images of maps are processed in order to extract relevant geographic information from printed coordinate pairs. The meaningful information is then transformed into georeferencing information for each single map sheet, and the complete set is compiled to produce a graphical index sheet for the map series along with relevant metadata. The whole process is fully automated and trained to attain maximum effectivity and throughput. |
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DAG |
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no |
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Admin @ si @ RRL2011a |
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1765 |
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Author |
Miguel Oliveira; Angel Sappa; V.Santos |
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Title |
Unsupervised Local Color Correction for Coarsely Registered Images |
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Conference Article |
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Year |
2011 |
Publication |
IEEE conference on Computer Vision and Pattern Recognition |
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201-208 |
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The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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CVPR |
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ADAS |
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no |
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Admin @ si @ OSS2011; ADAS @ adas @ |
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1766 |
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Author |
Francesco Ciompi; Oriol Pujol; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
Automatic Key Frames Detection in Intravascular Ultrasound Sequences |
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Conference Article |
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Year |
2011 |
Publication |
In MICCAI 2011 Workshop on Computing and Visualization for Intra Vascular Imaging |
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We present a method for the automatic detection of key frames in Intravascular Ultrasound (IVUS) sequences. The key frames are markers delimiting morphological changes along the vessel. The aim of defining key frames is two-fold: (1) they allow to summarize the content of the pullback into few representative frames; (2) they represent the basis for the automatic detection of clinical events in IVUS. The proposed approach achieved a compression ratio of 0.016 with respect to the original sequence and an average inter-frame distance of 61.76 frame, minimizing the number of missed clinical events. |
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CVII |
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MILAB;HuPBA |
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Admin @ si @ CPB2011 |
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1767 |
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Author |
Miguel Reyes; Jose Ramirez Moreno; Juan R Revilla; Petia Radeva; Sergio Escalera |
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Title |
ADiBAS: Sistema Multisensor de Adquisicion Automatica de Datos Corporales Objetivos, Robustos y Fiables para el Analisis de la Postura y el Movimiento |
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Conference Article |
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Year |
2011 |
Publication |
6th Congreso Iberoamericano de Tecnologia de Apoyo a la Discapacidad |
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939-944 |
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El análisis de la postura y del rango de movimiento son fundamentales para conocer la optimización del gesto y mejorar, de este modo, el rendimiento y la detección de posibles lesiones. Esta cuantificación es especialmente interesante en deportistas o en pacientes que presentan alguna lesión neurológica o del sistema musculo-esquelético, ya que permite conocer el proceso evolutivo de estos pacientes, evaluar la eficacia de la terapia aplicada y proponer, en caso necesario, una modificación del protocolo de tratamiento.
En este trabajo presentamos un sistema automático que permite, mediante una tecnología no invasiva, la captación automática de marcadores LED situados sobre el paciente y su posterior análisis con el fin de mostrar al especialista datos objetivos que permitan un mejor soporte diagnóstico. También se describe un
sistema analítico de la postura corporal sin marcadores, donde su ejecución durante secuencias dinámicas aporta un alto grado de naturalidad al paciente a la hora de realizar los ejercicios funcionales. |
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Palma de Mallorca |
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IBERDISCAP |
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MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ RRR2011 |
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1768 |
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Author |
Victor Ponce; Mario Gorga; Xavier Baro; Sergio Escalera |
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Title |
Human Behavior Analysis from Video Data Using Bag-of-Gestures |
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Conference Article |
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Year |
2011 |
Publication |
22nd International Joint Conference on Artificial Intelligence |
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Volume |
3 |
Issue |
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Pages |
2836-2837 |
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Abstract |
Human Behavior Analysis in Uncontrolled Environments can be categorized in two main challenges: 1) Feature extraction and 2) Behavior analysis from a set of corporal language vocabulary. In this work, we present our achievements characterizing some simple behaviors from visual data on different real applications and discuss our plan for future work: low level vocabulary definition from bag-of-gesture units and high level modelling and inference of human behaviors. |
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Barcelona |
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978-1-57735-516-8 |
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IJCAI |
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Notes |
HuPBA;MV |
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no |
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Call Number |
Admin @ si @ PGB2011b |
Serial |
1770 |
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Author |
Oscar Amoros; Sergio Escalera; Anna Puig |
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Title |
Adaboost GPU-based Classifier for Direct Volume Rendering |
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Conference Article |
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Year |
2011 |
Publication |
International Conference on Computer Graphics Theory and Applications |
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Volume |
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Pages |
215-219 |
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In volume visualization, the voxel visibitity and materials are carried out through an interactive editing of Transfer Function. In this paper, we present a two-level GPU-based labeling method that computes in times of rendering a set of labeled structures using the Adaboost machine learning classifier. In a pre-processing step, Adaboost trains a binary classifier from a pre-labeled dataset and, in each sample, takes into account a set of features. This binary classifier is a weighted combination of weak classifiers, which can be expressed as simple decision functions estimated on a single feature values. Then, at the testing stage, each weak classifier is independently applied on the features of a set of unlabeled samples. We propose an alternative representation of these classifiers that allow a GPU-based parallelizated testing stage embedded into the visualization pipeline. The empirical results confirm the OpenCL-based classification of biomedical datasets as a tough problem where an opportunity for further research emerges. |
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Algarve, Portugal |
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GRAPP |
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Notes |
MILAB; HuPBA |
Approved |
no |
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Call Number |
Admin @ si @ AEP2011 |
Serial |
1774 |
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Author |
Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras |
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Title |
The IIIA30 MObile Robot Object Recognition Datset |
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Conference Article |
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Year |
2011 |
Publication |
11th Portuguese Robotics Open |
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Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones. |
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Lisboa |
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Robotica |
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Notes |
RV;ADAS |
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no |
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Call Number |
Admin @ si @ RAV2011 |
Serial |
1777 |
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Permanent link to this record |
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Author |
Koen E.A. van de Sande; Jasper Uilings; Theo Gevers; Arnold Smeulders |
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Title |
Segmentation as Selective Search for Object Recognition |
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Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
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Pages |
1879-1886 |
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For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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ISE |
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no |
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Admin @ si @ SUG2011 |
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1780 |
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Author |
Shida Beigpour; Joost Van de Weijer |
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Title |
Object Recoloring Based on Intrinsic Image Estimation |
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Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference in Computer Vision |
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327 - 334 |
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Object recoloring is one of the most popular photo-editing tasks. The problem of object recoloring is highly under-constrained, and existing recoloring methods limit their application to objects lit by a white illuminant. Application of these methods to real-world scenes lit by colored illuminants, multiple illuminants, or interreflections, results in unrealistic recoloring of objects. In this paper, we focus on the recoloring of single-colored objects presegmented from their background. The single-color constraint allows us to fit a more comprehensive physical model to the object. We demonstrate that this permits us to perform realistic recoloring of objects lit by non-white illuminants, and multiple illuminants. Moreover, the model allows for more realistic handling of illuminant alteration of the scene. Recoloring results captured by uncalibrated cameras demonstrate that the proposed framework obtains realistic recoloring for complex natural images. Furthermore we use the model to transfer color between objects and show that the results are more realistic than existing color transfer methods. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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CIC |
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no |
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Call Number |
Admin @ si @ BeW2011 |
Serial |
1781 |
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Author |
Joan M. Nuñez |
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Title |
Computer vision techniques for characterization of finger joints in X-ray image |
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Report |
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Year |
2011 |
Publication |
CVC Technical Report |
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165 |
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Keywords |
Rheumatoid arthritis, X-ray, Sharp Van der Heijde, joint characterization, sclerosis detection, bone detection, edge, ridge |
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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 |
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Bellaterra (Barcelona) |
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Corporate Author |
Computer Vision Center |
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Master's thesis |
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Dr. Fernando Vilariño and Dra. Debora Gil |
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MV;IAM; |
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no |
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IAM @ iam @ Nuñ2011 |
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1795 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Implicit B-Spline Fitting Using the 3L Algorithm |
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Conference Article |
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2011 |
Publication |
18th IEEE International Conference on Image Processing |
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893-896 |
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Brussels, Belgium |
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ICIP |
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ADAS |
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no |
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Admin @ si @ RoS2011a; ADAS @ adas @ |
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1782 |
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Permanent link to this record |
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Author |
Victor Ponce; Mario Gorga; Xavier Baro; Petia Radeva; Sergio Escalera |
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Title |
Analisis de la Expresion Oral y Gestual en Proyectos Fin de Carrera Via un Sistema de Vision Artificial |
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Miscellaneous |
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Year |
2011 |
Publication |
Revista electronica de la asociacion de enseñantes universitarios de la informatica AENUI |
Abbreviated Journal |
ReVision |
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4 |
Issue |
1 |
Pages |
8-18 |
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La comunicación y expresión oral es una competencia de especial relevancia en el EEES. No obstante, en muchas enseñanzas superiores la puesta en práctica de esta competencia ha sido relegada principalmente a la presentación de proyectos fin de carrera. Dentro de un proyecto de innovación docente, se ha desarrollado una herramienta informática para la extracción de información objetiva para el análisis de la expresión oral y gestual de los alumnos. El objetivo es dar un “feedback” a los estudiantes que les permita mejorar la calidad de sus presentaciones. El prototipo inicial que se presenta en este trabajo permite extraer de forma automática información audiovisual y analizarla mediante técnicas de aprendizaje. El sistema ha sido aplicado a 15 proyectos fin de carrera y 15 exposiciones dentro de una asignatura de cuarto curso. Los resultados obtenidos muestran la viabilidad del sistema para sugerir factores que ayuden tanto en el éxito de la comunicación así como en los criterios de evaluación. |
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1989-1199 |
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MILAB;HuPBA;MV |
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no |
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Admin @ si @ PGB2011c |
Serial |
1783 |
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Permanent link to this record |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
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Title |
Circular Blurred Shape Model for Multiclass Symbol Recognition |
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Journal Article |
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Year |
2011 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
Abbreviated Journal |
TSMCB |
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Volume |
41 |
Issue |
2 |
Pages |
497-506 |
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In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. |
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1083-4419 |
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MILAB; DAG;HuPBA |
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no |
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Call Number |
Admin @ si @ EFP2011 |
Serial |
1784 |
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Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Title |
Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method |
Type |
Conference Article |
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Year |
2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Pages |
63-67 |
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Abstract |
In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts. |
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Beijing, China |
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ICDAR |
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Notes |
DAG;ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RAT2011 |
Serial |
1788 |
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Author |
Volkmar Frinken; Andreas Fischer; Horst Bunke; Alicia Fornes |
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Title |
Co-training for Handwritten Word Recognition |
Type |
Conference Article |
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Year |
2011 |
Publication |
11th International Conference on Document Analysis and Recognition |
Abbreviated Journal |
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Pages |
314-318 |
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Abstract |
To cope with the tremendous variations of writing styles encountered between different individuals, unconstrained automatic handwriting recognition systems need to be trained on large sets of labeled data. Traditionally, the training data has to be labeled manually, which is a laborious and costly process. Semi-supervised learning techniques offer methods to utilize unlabeled data, which can be obtained cheaply in large amounts in order, to reduce the need for labeled data. In this paper, we propose the use of Co-Training for improving the recognition accuracy of two weakly trained handwriting recognition systems. The first one is based on Recurrent Neural Networks while the second one is based on Hidden Markov Models. On the IAM off-line handwriting database we demonstrate a significant increase of the recognition accuracy can be achieved with Co-Training for single word recognition. |
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Beijing, China |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ FFB2011 |
Serial |
1789 |
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Permanent link to this record |