Bhaskar Chakraborty, Ognjen Rudovic, & Jordi Gonzalez. (2008). View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component-Wise HMM of Body Parts. In 8th IEEE International Conference on Automatic Face and Gesture Recognition.
|
Enric Marti, J. Rocarias, A. Sanchez, Petia Radeva, Ricardo Toledo, & Jordi Vitria. (2006). Caronte: una propuesta de entorno de gestion documental para asignaturas de Ingenieria Informatica.
|
Enric Marti, Carme Julia, & Debora Gil. (2006). Una experiencia de PBL en la docencia de la asignatura de Graficos por Computador en Ingenieria Informatica.
|
Enric Marti, J. Rocarias, A. Sanchez, Petia Radeva, Ricardo Toledo, & Jordi Vitria. (2006). Caronte: un gestor documental para asignaturas del EEES.
|
Enric Marti, J. Rocarias, Petia Radeva, H. Tizon, & Jordi Vitria. (2007). Caronte. Un gestor documental para asignaturas de universidad en el EEES.
|
Niki Aifanti, Angel Sappa, N. Grammalidis, & Sotiris Malassiotis. (2009). Advances in Tracking and Recognition of Human Motion. In Encyclopedia of Information Science and Technology (Vol. I, 65–71).
|
Arnau Ramisa, Adriana Tapus, Ramon Lopez de Mantaras, & Ricardo Toledo. (2008). Mobile Robot Localization using Panoramic Vision and Combination of Feature Region Detectors. In IEEE International Conference on Robotics and Automation, (538–543).
|
Francesco Ciompi. (2008). ECOC-based Plaque Classification using In-vivo and Exvivo Intravascular Ultrasound Data.
|
Carles Fernandez, & Jordi Gonzalez. (2008). A Multilingually-Extensible Module for Natural Language Generation.
|
Ognjen Rudovic, & Jordi Gonzalez. (2008). Building Temporal Templates for Human Behaviour Classification.
|
Marco Pedersoli. (2008). A Multiresolution Cascade for Human Detection.
|
Bhaskar Chakraborty. (2008). View-Invariant Human-Body Detection with Extension to Human Action Recognition using Component Wise HMM of Body Parts.
|
Pierluigi Casale. (2008). Social Environment Description from Data Collected with a Wearable Device.
|
Bogdan Raducanu, Jordi Vitria, & D. Gatica-Perez. (2009). You are Fired! Nonverbal Role Analysis in Competitive Meetings. In IEEE International Conference on Audio, Speech and Signal Processing (1949–1952).
Abstract: This paper addresses the problem of social interaction analysis in competitive meetings, using nonverbal cues. For our study, we made use of ldquoThe Apprenticerdquo reality TV show, which features a competition for a real, highly paid corporate job. Our analysis is centered around two tasks regarding a person's role in a meeting: predicting the person with the highest status and predicting the fired candidates. The current study was carried out using nonverbal audio cues. Results obtained from the analysis of a full season of the show, representing around 90 minutes of audio data, are very promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words.
|
Jose Manuel Alvarez, Theo Gevers, & Antonio Lopez. (2009). Learning Photometric Invariance from Diversified Color Model Ensembles. In 22nd IEEE Conference on Computer Vision and Pattern Recognition (565–572).
Abstract: Color is a powerful visual cue for many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions affecting negatively the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, those reflection models might be too restricted to model real-world scenes in which different reflectance mechanisms may hold simultaneously. Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is taken on input composed of both color variants and invariants. Then, the proposed method combines and weights these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, the fusion method uses a multi-view approach to minimize the estimation error. In this way, the method is robust to data uncertainty and produces properly diversified color invariant ensembles. Experiments are conducted on three different image datasets to validate the method. From the theoretical and experimental results, it is concluded that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning. Further, the method outperforms state-of- the-art detection techniques in the field of object, skin and road recognition.
Keywords: road detection
|