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Author David Geronimo; Frederic Lerasle; Antonio Lopez
Title State-driven particle filter for multi-person tracking Type Conference Article
Year 2012 Publication 11th International Conference on Advanced Concepts for Intelligent Vision Systems Abbreviated Journal
Volume 7517 Issue Pages (up) 467-478
Keywords human tracking
Abstract Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences.
Address Brno, Chzech Republic
Corporate Author Thesis
Publisher Springer Place of Publication Heidelberg Editor J. Blanc-Talon et al.
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ACIVS
Notes ADAS Approved yes
Call Number GLL2012; ADAS @ adas @ gll2012a Serial 1990
Permanent link to this record
 
 
Author Monica Piñol; Angel Sappa; Ricardo Toledo
Title MultiTable Reinforcement for Visual Object Recognition Type Conference Article
Year 2012 Publication 4th International Conference on Signal and Image Processing Abbreviated Journal
Volume 221 Issue Pages (up) 469-480
Keywords
Abstract This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach.
Address Coimbatore, India
Corporate Author Thesis
Publisher Springer India Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 1876-1100 ISBN 978-81-322-0996-6 Medium
Area Expedition Conference ICSIP
Notes ADAS Approved no
Call Number Admin @ si @ PST2012 Serial 2157
Permanent link to this record
 
 
Author Pedro Martins; Carlo Gatta; Paulo Carvalho
Title Feature-driven Maximally Stable Extremal Regions Type Conference Article
Year 2012 Publication 7th International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages (up) 490-497
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference VISAPP
Notes MILAB Approved no
Call Number Admin @ si @ MGC2012 Serial 2139
Permanent link to this record
 
 
Author Noha Elfiky; Jordi Gonzalez; Xavier Roca
Title Compact and Adaptive Spatial Pyramids for Scene Recognition Type Journal Article
Year 2012 Publication Image and Vision Computing Abbreviated Journal IMAVIS
Volume 30 Issue 8 Pages (up) 492–500
Keywords
Abstract Most successful approaches on scenerecognition tend to efficiently combine global image features with spatial local appearance and shape cues. On the other hand, less attention has been devoted for studying spatial texture features within scenes. Our method is based on the insight that scenes can be seen as a composition of micro-texture patterns. This paper analyzes the role of texture along with its spatial layout for scenerecognition. However, one main drawback of the resulting spatial representation is its huge dimensionality. Hence, we propose a technique that addresses this problem by presenting a compactSpatialPyramid (SP) representation. The basis of our compact representation, namely, CompactAdaptiveSpatialPyramid (CASP) consists of a two-stages compression strategy. This strategy is based on the Agglomerative Information Bottleneck (AIB) theory for (i) compressing the least informative SP features, and, (ii) automatically learning the most appropriate shape for each category. Our method exceeds the state-of-the-art results on several challenging scenerecognition data sets.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ EGR2012 Serial 2004
Permanent link to this record
 
 
Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Error Correcting Output Codes for multiclass classification: Application to two image vision problems Type Conference Article
Year 2012 Publication 16th symposium on Artificial Intelligence & Signal Processing Abbreviated Journal
Volume Issue Pages (up) 508-513
Keywords
Abstract Error-correcting output codes (ECOC) represents a powerful framework to deal with multiclass classification problems based on combining binary classifiers. The key factor affecting the performance of ECOC methods is the independence of binary classifiers, without which the ECOC method would be ineffective. In spite of its ability on classification of problems with relatively large number of classes, it has been applied in few real world problems. In this paper, we investigate the behavior of the ECOC approach on two image vision problems: logo recognition and shape classification using Decision Tree and AdaBoost as the base learners. The results show that the ECOC method can be used to improve the classification performance in comparison with the classical multiclass approaches.
Address Shiraz, Iran
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4673-1478-7 Medium
Area Expedition Conference AISP
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2012b Serial 2042
Permanent link to this record
 
 
Author Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados
Title Multipage Document Retrieval by Textual and Visual Representations Type Conference Article
Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages (up) 521-524
Keywords
Abstract In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow.
Address Tsukuba Science City, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium
Area Expedition Conference ICPR
Notes DAG Approved no
Call Number Admin @ si @ RKB2012 Serial 2053
Permanent link to this record
 
 
Author Hamdi Dibeklioglu; Theo Gevers; Albert Ali Salah
Title Are You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles Type Conference Article
Year 2012 Publication 12th European Conference on Computer Vision Abbreviated Journal
Volume 7574 Issue III Pages (up) 525-538
Keywords
Abstract Smiling is an indispensable element of nonverbal social interaction. Besides, automatic distinction between spontaneous and posed expressions is important for visual analysis of social signals. Therefore, in this paper, we propose a method to distinguish between spontaneous and posed enjoyment smiles by using the dynamics of eyelid, cheek, and lip corner movements. The discriminative power of these movements, and the effect of different fusion levels are investigated on multiple databases. Our results improve the state-of-the-art. We also introduce the largest spontaneous/posed enjoyment smile database collected to date, and report new empirical and conceptual findings on smile dynamics. The collected database consists of 1240 samples of 400 subjects. Moreover, it has the unique property of having an age range from 8 to 76 years. Large scale experiments on the new database indicate that eyelid dynamics are highly relevant for smile classification, and there are age-related differences in smile dynamics.
Address Florence, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33711-6 Medium
Area Expedition Conference ECCV
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ DGS2012 Serial 2024
Permanent link to this record
 
 
Author Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados
Title Hierarchical graph representation for symbol spotting in graphical document images Type Conference Article
Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal
Volume 7626 Issue Pages (up) 529-538
Keywords
Abstract Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset.
Address Miyajima-Itsukushima, Hiroshima
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-34165-6 Medium
Area Expedition Conference SSPR&SPR
Notes DAG Approved no
Call Number Admin @ si @ BDJ2012 Serial 2126
Permanent link to this record
 
 
Author Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera
Title Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization Type Journal Article
Year 2012 Publication Journal of Ambient Intelligence and Smart Environments Abbreviated Journal JAISE
Volume 4 Issue 6 Pages (up) 535-546
Keywords Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation
Abstract We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1876-1364 ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ HZM2012a Serial 2006
Permanent link to this record
 
 
Author Michael Holte; Bhaskar Chakraborty; Jordi Gonzalez; Thomas B. Moeslund
Title A Local 3D Motion Descriptor for Multi-View Human Action Recognition from 4D Spatio-Temporal Interest Points Type Journal Article
Year 2012 Publication IEEE Journal of Selected Topics in Signal Processing Abbreviated Journal J-STSP
Volume 6 Issue 5 Pages (up) 553-565
Keywords
Abstract In this paper, we address the problem of human action recognition in reconstructed 3-D data acquired by multi-camera systems. We contribute to this field by introducing a novel 3-D action recognition approach based on detection of 4-D (3-D space $+$ time) spatio-temporal interest points (STIPs) and local description of 3-D motion features. STIPs are detected in multi-view images and extended to 4-D using 3-D reconstructions of the actors and pixel-to-vertex correspondences of the multi-camera setup. Local 3-D motion descriptors, histogram of optical 3-D flow (HOF3D), are extracted from estimated 3-D optical flow in the neighborhood of each 4-D STIP and made view-invariant. The local HOF3D descriptors are divided using 3-D spatial pyramids to capture and improve the discrimination between arm- and leg-based actions. Based on these pyramids of HOF3D descriptors we build a bag-of-words (BoW) vocabulary of human actions, which is compressed and classified using agglomerative information bottleneck (AIB) and support vector machines (SVMs), respectively. Experiments on the publicly available i3DPost and IXMAS datasets show promising state-of-the-art results and validate the performance and view-invariance of the approach.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1932-4553 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ HCG2012 Serial 1994
Permanent link to this record
 
 
Author Pierluigi Casale; Oriol Pujol; Petia Radeva
Title Personalization and User Verification in Wearable Systems using Biometric Walking Patterns Type Journal Article
Year 2012 Publication Personal and Ubiquitous Computing Abbreviated Journal PUC
Volume 16 Issue 5 Pages (up) 563-580
Keywords
Abstract In this article, a novel technique for user’s authentication and verification using gait as a biometric unobtrusive pattern is proposed. The method is based on a two stages pipeline. First, a general activity recognition classifier is personalized for an specific user using a small sample of her/his walking pattern. As a result, the system is much more selective with respect to the new walking pattern. A second stage verifies whether the user is an authorized one or not. This stage is defined as a one-class classification problem. In order to solve this problem, a four-layer architecture is built around the geometric concept of convex hull. This architecture allows to improve robustness to outliers, modeling non-convex shapes, and to take into account temporal coherence information. Two different scenarios are proposed as validation with two different wearable systems. First, a custom high-performance wearable system is built and used in a free environment. A second dataset is acquired from an Android-based commercial device in a ‘wild’ scenario with rough terrains, adversarial conditions, crowded places and obstacles. Results on both systems and datasets are very promising, reducing the verification error rates by an order of magnitude with respect to the state-of-the-art technologies.
Address
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1617-4909 ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ CPR2012 Serial 1706
Permanent link to this record
 
 
Author Bogdan Raducanu; Fadi Dornaika
Title Pose-Invariant Face Recognition in Videos for Human-Machine Interaction Type Conference Article
Year 2012 Publication 12th European Conference on Computer Vision Abbreviated Journal
Volume 7584 Issue Pages (up) 566.575
Keywords
Abstract Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33867-0 Medium
Area Expedition Conference ECCVW
Notes OR;MV Approved no
Call Number Admin @ si @ RaD2012e Serial 2182
Permanent link to this record
 
 
Author Fadi Dornaika; A.Assoum; Bogdan Raducanu
Title Automatic Dimensionality Estimation for Manifold Learning through Optimal Feature Selection Type Conference Article
Year 2012 Publication Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop Abbreviated Journal
Volume 7626 Issue Pages (up) 575-583
Keywords
Abstract A very important aspect in manifold learning is represented by automatic estimation of the intrinsic dimensionality. Unfortunately, this problem has received few attention in the literature of manifold learning. In this paper, we argue that feature selection paradigm can be used to the problem of automatic dimensionality estimation. Besides this, it also leads to improved recognition rates. Our approach for optimal feature selection is based on a Genetic Algorithm. As a case study for manifold learning, we have considered Laplacian Eigenmaps (LE) and Locally Linear Embedding (LLE). The effectiveness of the proposed framework was tested on the face recognition problem. Extensive experiments carried out on ORL, UMIST, Yale, and Extended Yale face data sets confirmed our hypothesis.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-34165-6 Medium
Area Expedition Conference SSPR&SPR
Notes OR;MV Approved no
Call Number Admin @ si @ DAR2012 Serial 2174
Permanent link to this record
 
 
Author Jose Manuel Alvarez; Y. LeCun; Theo Gevers; Antonio Lopez
Title Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features Type Conference Article
Year 2012 Publication 12th European Conference on Computer Vision – Workshops and Demonstrations Abbreviated Journal
Volume 7584 Issue Pages (up) 586-595
Keywords road detection
Abstract Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process.
Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33867-0 Medium
Area Expedition Conference ECCVW
Notes ADAS;ISE Approved no
Call Number Admin @ si @ ALG2012; ADAS @ adas Serial 2187
Permanent link to this record
 
 
Author Laura Igual; Joan Carles Soliva; Sergio Escalera; Roger Gimeno; Oscar Vilarroya; Petia Radeva
Title Automatic Brain Caudate Nuclei Segmentation and Classification in Diagnostic of Attention-Deficit/Hyperactivity Disorder Type Journal Article
Year 2012 Publication Computerized Medical Imaging and Graphics Abbreviated Journal CMIG
Volume 36 Issue 8 Pages (up) 591-600
Keywords Automatic caudate segmentation; Attention-Deficit/Hyperactivity Disorder; Diagnostic test; Machine learning; Decision stumps; Dissociated dipoles
Abstract We present a fully automatic diagnostic imaging test for Attention-Deficit/Hyperactivity Disorder diagnosis assistance based on previously found evidences of caudate nucleus volumetric abnormalities. The proposed method consists of different steps: a new automatic method for external and internal segmentation of caudate based on Machine Learning methodologies; the definition of a set of new volume relation features, 3D Dissociated Dipoles, used for caudate representation and classification. We separately validate the contributions using real data from a pediatric population and show precise internal caudate segmentation and discrimination power of the diagnostic test, showing significant performance improvements in comparison to other state-of-the-art methods.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes OR; HuPBA; MILAB Approved no
Call Number Admin @ si @ ISE2012 Serial 2143
Permanent link to this record