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Author  |
Victor Ponce; Sergio Escalera; Xavier Baro | ||||
| Title | Multi-modal Social Signal Analysis for Predicting Agreement in Conversation Settings | Type | Conference Article | ||
| Year | 2013 | Publication | 15th ACM International Conference on Multimodal Interaction | Abbreviated Journal | |
| Volume | Issue | Pages | 495-502 | ||
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| Abstract | In this paper we present a non-invasive ambient intelligence framework for the analysis of non-verbal communication applied to conversational settings. In particular, we apply feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues coming from the fields of psychology and observational methodology. We test our methodology over data captured in victim-offender mediation scenarios. Using different state-of-the-art classification approaches, our system achieve upon 75% of recognition predicting agreement among the parts involved in the conversations, using as ground truth the experts opinions. | ||||
| Address | Sidney; Australia; December 2013 | ||||
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| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-2129-7 | Medium | ||
| Area | Expedition | Conference | ICMI | ||
| Notes | HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ PEB2013 | Serial | 2488 | ||
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| Author |
Vitaliy Konovalov; Albert Clapes; Sergio Escalera | ||||
| Title | Automatic Hand Detection in RGB-Depth Data Sequences | Type | Conference Article | ||
| Year | 2013 | Publication | 16th Catalan Conference on Artificial Intelligence | Abbreviated Journal | |
| Volume | Issue | Pages | 91-100 | ||
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| Abstract | Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, viewpoint variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point.
In a given frame, the human body is segmented first in the depth image. Then, a graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths’ connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures. |
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| Address | Vic; October 2013 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | CCIA | ||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ KCE2013 | Serial | 2323 | ||
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| Author |
Volkmar Frinken; Andreas Fischer; Carlos David Martinez Hinarejos | ||||
| Title | Handwriting Recognition in Historical Documents using Very Large Vocabularies | Type | Conference Article | ||
| Year | 2013 | Publication | 2nd International Workshop on Historical Document Imaging and Processing | Abbreviated Journal | |
| Volume | Issue | Pages | 67-72 | ||
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| Abstract | Language models are used in automatic transcription system to resolve ambiguities. This is done by limiting the vocabulary of words that can be recognized as well as estimating the n-gram probability of the words in the given text. In the context of historical documents, a non-unified spelling and the limited amount of written text pose a substantial problem for the selection of the recognizable vocabulary as well as the computation of the word probabilities. In this paper we propose for the transcription of historical Spanish text to keep the corpus for the n-gram limited to a sample of the target text, but expand the vocabulary with words gathered from external resources. We analyze the performance of such a transcription system with different sizes of external vocabularies and demonstrate the applicability and the significant increase in recognition accuracy of using up to 300 thousand external words. | ||||
| Address | Washington; USA; August 2013 | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-1-4503-2115-0 | Medium | ||
| Area | Expedition | Conference | HIP | ||
| Notes | DAG; 600.056; 600.045; 600.061; 602.006; 602.101 | Approved | no | ||
| Call Number | Admin @ si @ FFM2013 | Serial | 2296 | ||
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| Author |
Wenjuan Gong | ||||
| Title | 3D Motion Data aided Human Action Recognition and Pose Estimation | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | In this work, we explore human action recognition and pose estimation prob-
lems. Different from traditional works of learning from 2D images or video sequences and their annotated output, we seek to solve the problems with ad- ditional 3D motion capture information, which helps to fill the gap between 2D image features and human interpretations. We first compare two different schools of approaches commonly used for 3D pose estimation from 2D pose configuration: modeling and learning methods. By looking into experiments results and considering our problems, we fixed a learning method as the following approaches to do pose estimation. We then establish a framework by adding a module of detecting 2D pose configuration from images with varied background, which widely extend the application of the approach. We also seek to directly estimate 3D poses from image features, instead of estimating 2D poses as a intermediate module. We explore a robust input feature, which combined with the proposed distance measure, provides a solution for noisy or corrupted inputs. We further utilize the above method to estimate weak poses,which is a concise representation of the original poses by using dimension deduction technologies, from image features. Weak pose space is where we calculate vocabulary and label action types using a bog of words pipeline. Temporal information of an action is taken into consideration by considering several consecutive frames as a single unit for computing vocabulary and histogram assignments. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Gon2013 | Serial | 2279 | ||
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| Author |
Xavier Baro; David Masip; Elena Planas; Julia Minguillon | ||||
| Title | PeLP: Plataforma para el Aprendizaje de Lenguajes de Programación | Type | Miscellaneous | ||
| Year | 2013 | Publication | XV Jornadas de Enseñanza Universitaria de la Informatica | Abbreviated Journal | |
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| Area | Expedition | Conference | JENUI | ||
| Notes | OR;HuPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ BMP2013 | Serial | 2237 | ||
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| Author |
Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers | ||||
| Title | Adapting Pedestrian Detection from Synthetic to Far Infrared Images | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Visual Domain Adaptation and Dataset Bias | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Domain Adaptation; Far Infrared; Pedestrian Detection | ||||
| Abstract | We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Publisher | Place of Publication | Sydney, Australy | Editor | ||
| Language | English | Summary Language | Original Title | ||
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| Area | Expedition | Conference | ICCVW-VisDA | ||
| Notes | ADAS; 600.054; 600.055; 600.057; 601.217;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ SRV2013 | Serial | 2334 | ||
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| Author |
Zeynep Yucel; Albert Ali Salah; Çetin Meriçli; Tekin Meriçli; Roberto Valenti; Theo Gevers | ||||
| Title | Joint Attention by Gaze Interpolation and Saliency | Type | Journal | ||
| Year | 2013 | Publication | IEEE Transactions on cybernetics | Abbreviated Journal | T-CIBER |
| Volume | 43 | Issue | 3 | Pages | 829-842 |
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| Abstract | Joint attention, which is the ability of coordination of a common point of reference with the communicating party, emerges as a key factor in various interaction scenarios. This paper presents an image-based method for establishing joint attention between an experimenter and a robot. The precise analysis of the experimenter's eye region requires stability and high-resolution image acquisition, which is not always available. We investigate regression-based interpolation of the gaze direction from the head pose of the experimenter, which is easier to track. Gaussian process regression and neural networks are contrasted to interpolate the gaze direction. Then, we combine gaze interpolation with image-based saliency to improve the target point estimates and test three different saliency schemes. We demonstrate the proposed method on a human-robot interaction scenario. Cross-subject evaluations, as well as experiments under adverse conditions (such as dimmed or artificial illumination or motion blur), show that our method generalizes well and achieves rapid gaze estimation for establishing joint attention. | ||||
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| ISSN | 2168-2267 | ISBN | Medium | ||
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| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ YSM2013 | Serial | 2363 | ||
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