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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 | Joan Mas; Josep Llados; Gemma Sanchez; J.A. Jorge | ||||
| Title | A syntactic approach based on distortion-tolerant Adjacency Grammars and a spatial-directed parser to interpret sketched diagrams | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 43 | Issue | 12 | Pages | 4148–4164 |
| Keywords | Syntactic Pattern Recognition; Symbol recognition; Diagram understanding; Sketched diagrams; Adjacency Grammars; Incremental parsing; Spatial directed parsing | ||||
| Abstract | This paper presents a syntactic approach based on Adjacency Grammars (AG) for sketch diagram modeling and understanding. Diagrams are a combination of graphical symbols arranged according to a set of spatial rules defined by a visual language. AG describe visual shapes by productions defined in terms of terminal and non-terminal symbols (graphical primitives and subshapes), and a set functions describing the spatial arrangements between symbols. Our approach to sketch diagram understanding provides three main contributions. First, since AG are linear grammars, there is a need to define shapes and relations inherently bidimensional using a sequential formalism. Second, our parsing approach uses an indexing structure based on a spatial tessellation. This serves to reduce the search space when finding candidates to produce a valid reduction. This allows order-free parsing of 2D visual sentences while keeping combinatorial explosion in check. Third, working with sketches requires a distortion model to cope with the natural variations of hand drawn strokes. To this end we extended the basic grammar with a distortion measure modeled on the allowable variation on spatial constraints associated with grammar productions. Finally, the paper reports on an experimental framework an interactive system for sketch analysis. User tests performed on two real scenarios show that our approach is usable in interactive settings. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ MLS2010 | Serial | 1336 | ||
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| Author | Umapada Pal; Partha Pratim Roy; N. Tripathya; Josep Llados | ||||
| Title | Multi-oriented Bangla and Devnagari text recognition | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 43 | Issue | 12 | Pages | 4124–4136 |
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| Abstract | There are printed complex documents where text lines of a single page may have different orientations or the text lines may be curved in shape. As a result, it is difficult to detect the skew of such documents and hence character segmentation and recognition of such documents are a complex task. In this paper, using background and foreground information we propose a novel scheme towards the recognition of Indian complex documents of Bangla and Devnagari script. In Bangla and Devnagari documents usually characters in a word touch and they form cavity regions. To take care of these cavity regions, background information of such documents is used. Convex hull and water reservoir principle have been applied for this purpose. Here, at first, the characters are segmented from the documents using the background information of the text. Next, individual characters are recognized using rotation invariant features obtained from the foreground part of the characters.
For character segmentation, at first, writing mode of a touching component (word) is detected using water reservoir principle based features. Next, depending on writing mode and the reservoir base-region of the touching component, a set of candidate envelope points is then selected from the contour points of the component. Based on these candidate points, the touching component is finally segmented into individual characters. For recognition of multi-sized/multi-oriented characters the features are computed from different angular information obtained from the external and internal contour pixels of the characters. These angular information are computed in such a way that they do not depend on the size and rotation of the characters. Circular and convex hull rings have been used to divide a character into smaller zones to get zone-wise features for higher recognition results. We combine circular and convex hull features to improve the results and these features are fed to support vector machines (SVM) for recognition. From our experiment we obtained recognition results of 99.18% (98.86%) accuracy when tested on 7515 (7874) Devnagari (Bangla) characters. |
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | DAG | Approved | no | ||
| Call Number | DAG @ dag @ PRT2010 | Serial | 1337 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| Title | Re-coding ECOCs without retraining | Type | Journal Article | ||
| Year | 2010 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 31 | Issue | 7 | Pages | 555–562 |
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| Abstract | A standard way to deal with multi-class categorization problems is by the combination of binary classifiers in a pairwise voting procedure. Recently, this classical approach has been formalized in the Error-Correcting Output Codes (ECOC) framework. In the ECOC framework, the one-versus-one coding demonstrates to achieve higher performance than the rest of coding designs. The binary problems that we train in the one-versus-one strategy are significantly smaller than in the rest of designs, and usually easier to be learnt, taking into account the smaller overlapping between classes. However, a high percentage of the positions coded by zero of the coding matrix, which implies a high sparseness degree, does not codify meta-class membership information. In this paper, we show that using the training data we can redefine without re-training, in a problem-dependent way, the one-versus-one coding matrix so that the new coded information helps the system to increase its generalization capability. Moreover, the new re-coding strategy is generalized to be applied over any binary code. The results over several UCI Machine Learning repository data sets and two real multi-class problems show that performance improvements can be obtained re-coding the classical one-versus-one and Sparse random designs compared to different state-of-the-art ECOC configurations. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | MILAB;HUPBA | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ EPR2010e | Serial | 1338 | ||
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| Author | David Geronimo; Antonio Lopez; Angel Sappa; Thorsten Graf | ||||
| Title | Survey on Pedestrian Detection for Advanced Driver Assistance Systems | Type | Journal Article | ||
| Year | 2010 | Publication | IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 32 | Issue | 7 | Pages | 1239–1258 |
| Keywords | ADAS, pedestrian detection, on-board vision, survey | ||||
| Abstract | Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one-after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges. | ||||
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| ISSN | 0162-8828 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GLS2010 | Serial | 1340 | ||
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| Author | David Geronimo; Angel Sappa; Daniel Ponsa; Antonio Lopez | ||||
| Title | 2D-3D based on-board pedestrian detection system | Type | Journal Article | ||
| Year | 2010 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 114 | Issue | 5 | Pages | 583–595 |
| Keywords | Pedestrian detection; Advanced Driver Assistance Systems; Horizon line; Haar wavelets; Edge orientation histograms | ||||
| Abstract | During the next decade, on-board pedestrian detection systems will play a key role in the challenge of increasing traffic safety. The main target of these systems, to detect pedestrians in urban scenarios, implies overcoming difficulties like processing outdoor scenes from a mobile platform and searching for aspect-changing objects in cluttered environments. This makes such systems combine techniques in the state-of-the-art Computer Vision. In this paper we present a three module system based on both 2D and 3D cues. The first module uses 3D information to estimate the road plane parameters and thus select a coherent set of regions of interest (ROIs) to be further analyzed. The second module uses Real AdaBoost and a combined set of Haar wavelets and edge orientation histograms to classify the incoming ROIs as pedestrian or non-pedestrian. The final module loops again with the 3D cue in order to verify the classified ROIs and with the 2D in order to refine the final results. According to the results, the integration of the proposed techniques gives rise to a promising system. | ||||
| Address | Computer Vision and Image Understanding (Special Issue on Intelligent Vision Systems), Vol. 114(5):583-595 | ||||
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| ISSN | 1077-3142 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ GSP2010 | Serial | 1341 | ||
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| Author | Naveen Onkarappa; Angel Sappa | ||||
| Title | On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow | Type | Conference Article | ||
| Year | 2010 | Publication | 7th International Conference on Image Analysis and Recognition | Abbreviated Journal | |
| Volume | 6111 | Issue | Pages | 230-239 | |
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| Abstract | This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. | ||||
| Address | Povoa de Varzim (Portugal) | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-13771-6 | Medium | |
| Area | Expedition | Conference | ICIAR | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ OnS2010 | Serial | 1342 | ||
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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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