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| Author | Francisco Javier Orozco | ||||
| Title | Human Emotion Evaluation on Facial Image Sequences | Type | Book Whole | ||
| Year | 2010 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | Psychological evidence has emphasized the importance of affective behaviour understanding due to its high impact in nowadays interaction humans and computers. All
type of affective and behavioural patterns such as gestures, emotions and mental states are highly displayed through the face, head and body. Therefore, this thesis is focused to analyse affective behaviours on head and face. To this end, head and facial movements are encoded by using appearance based tracking methods. Specifically, a wise combination of deformable models captures rigid and non-rigid movements of different kinematics; 3D head pose, eyebrows, mouth, eyelids and irises are taken into account as basis for extracting features from databases of video sequences. This approach combines the strengths of adaptive appearance models, optimization methods and backtracking techniques. For about thirty years, computer sciences have addressed the investigation on human emotions to the automatic recognition of six prototypic emotions suggested by Darwin and systematized by Paul Ekman in the seventies. The Facial Action Coding System (FACS) which uses discrete movements of the face (called Action units or AUs) to code the six facial emotions named anger, disgust, fear, happy-Joy, sadness and surprise. However, human emotions are much complex patterns that have not received the same attention from computer scientists. Simon Baron-Cohen proposed a new taxonomy of emotions and mental states without a system coding of the facial actions. These 426 affective behaviours are more challenging for the understanding of human emotions. Beyond of classically classifying the six basic facial expressions, more subtle gestures, facial actions and spontaneous emotions are considered here. By assessing confidence on the recognition results, exploring spatial and temporal relationships of the features, some methods are combined and enhanced for developing new taxonomy of expressions and emotions. The objective of this dissertation is to develop a computer vision system, including both facial feature extraction, expression recognition and emotion understanding by building a bottom-up reasoning process. Building a detailed taxonomy of human affective behaviours is an interesting challenge for head-face-based image analysis methods. In this paper, we exploit the strengths of Canonical Correlation Analysis (CCA) to enhance an on-line head-face tracker. A relationship between head pose and local facial movements is studied according to their cognitive interpretation on affective expressions and emotions. Active Shape Models are synthesized for AAMs based on CCA-regression. Head pose and facial actions are fused into a maximally correlated space in order to assess expressiveness, confidence and classification in a CBR system. The CBR solutions are also correlated to the cognitive features, which allow avoiding exhaustive search when recognizing new head-face features. Subsequently, Support Vector Machines (SVMs) and Bayesian Networks are applied for learning the spatial relationships of facial expressions. Similarly, the temporal evolution of facial expressions, emotion and mental states are analysed based on Factorized Dynamic Bayesian Networks (FaDBN). As results, the bottom-up system recognizes six facial expressions, six basic emotions and six mental states, plus enhancing this categorization with confidence assessment at each level, intensity of expressions and a complete taxonomy |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| ISSN | ISBN | 978-84-936529-3-7 | Medium | ||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Oro2010 | Serial | 1335 | ||
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| Author | Marc Serra | ||||
| Title | Estimating Intrinsic Images from Physical and Categorical Color Cues | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 151 | Issue | Pages |
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Ser2010 | Serial | 1345 | ||
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| Author | Ahmed Mounir Gad | ||||
| Title | Object Localization Enhancement by Multiple Segmentation Fusion | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 152 | Issue | Pages |
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Mou2010 | Serial | 1346 | ||
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| Author | Antonio Hernandez | ||||
| Title | Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 153 | Issue | Pages |
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | HUPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ Her2010 | Serial | 1347 | ||
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| Author | Jorge Bernal; Fernando Vilariño; F. Javier Sanchez | ||||
| Title | Feature Detectors and Feature Descriptors: Where We Are Now | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 154 | Issue | Pages |
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| Abstract | Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours. | ||||
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| Area | 800 | Expedition | Conference | ||
| Notes | MV;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BVS2010; IAM @ iam @ BVS2010 | Serial | 1348 | ||
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| Author | Lluis Pere de las Heras | ||||
| Title | Syntactic Model for Semantic Document Analysis | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 158 | Issue | Pages |
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Per2010 | Serial | 1350 | ||
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| Author | Anjan Dutta | ||||
| Title | Symbol Spotting in Graphical Documents by Serialized Subgraph Matching | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 159 | Issue | Pages |
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Dut2010 | Serial | 1351 | ||
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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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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Art2010 | Serial | 1352 | ||
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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 | |||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Fer2010b | Serial | 1353 | ||
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| 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 | |||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Alm2010 | Serial | 1354 | ||
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| Author | Nataliya Shapovalova | ||||
| Title | On Importance of Interaction and Context | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 155 | Issue | Pages |
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Sha2010 | Serial | 1355 | ||
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| Author | Zhanwu Xiong | ||||
| Title | A Pompd Model for Active Camera Control | Type | Report | ||
| Year | 2010 | Publication | CVC Technical Report | Abbreviated Journal | |
| Volume | 156 | Issue | Pages |
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| Corporate Author | Thesis | Master's thesis | |||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ Xio2010 | Serial | 1356 | ||
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| Author | David Geronimo; Antonio Lopez | ||||
| Title | Deteccion de Peatones para Sistemas Avanzados de Asistencia al Conductor | Type | Miscellaneous | ||
| Year | 2010 | Publication | UAB Divulga | Abbreviated Journal | |
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| Abstract | Los sistemas de asistencia al conductor, y particularmente los sistemas de protección de peatones, representan uno de los campos de investigación más activos dedicados a la mejora de la seguridad vial. El mayor desafío es el desarrollo de sistemas a bordo fiables de detección de peatones. En esta revisión del estado de la técnica de la detección de peatones, se divide el problema en diferentes etapas, cada una con responsabilidades propias dentro del sistema. Esta división facilita el posterior análisis y discusión de cada uno de los métodos en la literatura, favoreciendo la comparación entre ellos. Finalmente se discuten los temas más importantes de este campo poniendo especial énfasis en las necesidades actuales y los desafíos futuros. | ||||
| Address | Bellaterra (Catalonia), Spain | ||||
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| Notes | spreading;ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GeL2010a | Serial | 1414 | ||
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| Author | Sergio Escalera; Petia Radeva; Jordi Vitria; Xavier Baro; Bogdan Raducanu | ||||
| Title | Modelling and Analyzing Multimodal Dyadic Interactions Using Social Networks | Type | Conference Article | ||
| Year | 2010 | Publication | 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction. | Abbreviated Journal | |
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| Keywords | Social interaction; Multimodal fusion, Influence model; Social network analysis | ||||
| Abstract | Social network analysis became a common technique used to model and quantify the properties of social interactions. In this paper, we propose an integrated framework to explore the characteristics of a social network extracted from
multimodal dyadic interactions. First, speech detection is performed through an audio/visual fusion scheme based on stacked sequential learning. In the audio domain, speech is detected through clusterization of audio features. Clusters are modelled by means of an One-state Hidden Markov Model containing a diagonal covariance Gaussian Mixture Model. In the visual domain, speech detection is performed through differential-based feature extraction from the segmented mouth region, and a dynamic programming matching procedure. Second, in order to model the dyadic interactions, we employed the Influence Model whose states encode the previous integrated audio/visual data. Third, the social network is extracted based on the estimated influences. For our study, we used a set of videos belonging to New York Times’ Blogging Heads opinion blog. The results are reported both in terms of accuracy of the audio/visual data fusion and centrality measures used to characterize the social network. |
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| Address | Beijing (China) | ||||
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| Area | Expedition | Conference | ICMI-MLI | ||
| Notes | OR;MILAB;HUPBA;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ ERV2010 | Serial | 1427 | ||
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| Author | Jaume Amores; David Geronimo; Antonio Lopez | ||||
| Title | Multiple instance and active learning for weakly-supervised object-class segmentation | Type | Conference Article | ||
| Year | 2010 | Publication | 3rd IEEE International Conference on Machine Vision | Abbreviated Journal | |
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| Keywords | Multiple Instance Learning; Active Learning; Object-class segmentation. | ||||
| Abstract | In object-class segmentation, one of the most tedious tasks is to manually segment many object examples in order to learn a model of the object category. Yet, there has been little research on reducing the degree of manual annotation for
object-class segmentation. In this work we explore alternative strategies which do not require full manual segmentation of the object in the training set. In particular, we study the use of bounding boxes as a coarser and much cheaper form of segmentation and we perform a comparative study of several Multiple-Instance Learning techniques that allow to obtain a model with this type of weak annotation. We show that some of these methods can be competitive, when used with coarse segmentations, with methods that require full manual segmentation of the objects. Furthermore, we show how to use active learning combined with this weakly supervised strategy. As we see, this strategy permits to reduce the amount of annotation and optimize the number of examples that require full manual segmentation in the training set. |
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| Address | Hong-Kong | ||||
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| Area | Expedition | Conference | ICMV | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ AGL2010b | Serial | 1429 | ||
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