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Author ![]() |
Antonio Hernandez; Miguel Reyes; Victor Ponce; Sergio Escalera | ||||
Title | GrabCut-Based Human Segmentation in Video Sequences | Type | Journal Article | ||
Year | 2012 | Publication | Sensors | Abbreviated Journal | SENS |
Volume | 12 | Issue | 11 | Pages | 15376-15393 |
Keywords | segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field | ||||
Abstract | In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology. | ||||
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Language | Summary Language | Original Title | |||
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ISSN | ISBN | Medium | |||
Area | Expedition | Conference | |||
Notes | HuPBA;MILAB | Approved | no | ||
Call Number | Admin @ si @ HRP2012 | Serial | 2147 | ||
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Author ![]() |
Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera | ||||
Title | Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps | Type | Conference Article | ||
Year | 2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 726-732 | ||
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Abstract | We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. 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 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 | Portland; Oregon; June 2013 | ||||
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 | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
Area | Expedition | Conference | CVPR | ||
Notes | MILAB;HuPBA | Approved | no | ||
Call Number | Admin @ si @ HZM2012b | Serial | 2046 | ||
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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 | 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. | ||||
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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 | ||
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Author ![]() |
Ariel Amato | ||||
Title | Environment-Independent Moving Cast Shadow Suppression in Video Surveillance | Type | Book Whole | ||
Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | This thesis is devoted to moving shadows detection and suppression. Shadows could be defined as the parts of the scene that are not directly illuminated by a light source due to obstructing object or objects. Often, moving shadows in images sequences are undesirable since they could cause degradation of the expected results during processing of images for object detection, segmentation, scene surveillance or similar purposes. In this thesis first moving shadow detection methods are exhaustively overviewed. Beside the mentioned methods from literature and to compensate their limitations a new moving shadow detection method is proposed. It requires no prior knowledge about the scene, nor is it restricted to assumptions about specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene the values of the background image are divided by values of the current frame in the RGB color space. In the thesis how this luminance ratio can be used to identify segments with low gradient constancy is shown, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of the proposed method compared with the most sophisticated state-of-the-art shadow detection algorithms. These results show that the proposed approach is robust and accurate over a broad range of shadow types and challenging video conditions. | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Mikhail Mozerov;Jordi Gonzalez | |
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 @ Ama2012 | Serial | 2201 | ||
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Author ![]() |
Arjan Gijsenij; R. Lu; Theo Gevers; De Xu | ||||
Title | Color Constancy for Multiple Light Source | Type | Journal Article | ||
Year | 2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 21 | Issue | 2 | Pages | 697-707 |
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Abstract | Impact factor 2010: 2.92
Impact factor 2011/2012?: 3.32 Color constancy algorithms are generally based on the simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated due to the presence of multiple light sources. In this paper, we will address more realistic scenarios where the uniform light-source assumption is too restrictive. First, a methodology is proposed to extend existing algorithms by applying color constancy locally to image patches, rather than globally to the entire image. After local (patch-based) illuminant estimation, these estimates are combined into more robust estimations, and a local correction is applied based on a modified diagonal model. Quantitative and qualitative experiments on spectral and real images show that the proposed methodology reduces the influence of two light sources simultaneously present in one scene. If the chromatic difference between these two illuminants is more than 1° , the proposed framework outperforms algorithms based on the uniform light-source assumption (with error-reduction up to approximately 30%). Otherwise, when the chromatic difference is less than 1° and the scene can be considered to contain one (approximately) uniform light source, the performance of the proposed method framework is similar to global color constancy methods. |
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Publisher | Place of Publication | Editor | |||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1057-7149 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ALTRES;ISE | Approved | no | ||
Call Number | Admin @ si @ GLG2012a | Serial | 1852 | ||
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Author ![]() |
Arjan Gijsenij; Theo Gevers; Joost Van de Weijer | ||||
Title | Improving Color Constancy by Photometric Edge Weighting | Type | Journal Article | ||
Year | 2012 | Publication | IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 34 | Issue | 5 | Pages | 918-929 |
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Abstract | : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy. | ||||
Address | Los Alamitos; CA; USA; | ||||
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 | 0162-8828 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | CIC;ISE | Approved | no | ||
Call Number | Admin @ si @ GGW2012 | Serial | 1850 | ||
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Author ![]() |
Arnau Ramisa; David Aldavert; Shrihari Vasudevan; Ricardo Toledo; Ramon Lopez de Mantaras | ||||
Title | Evaluation of Three Vision Based Object Perception Methods for a Mobile Robot | Type | Journal Article | ||
Year | 2012 | Publication | Journal of Intelligent and Robotic Systems | Abbreviated Journal | JIRC |
Volume | 68 | Issue | 2 | Pages | 185-208 |
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Abstract | This paper addresses visual object perception applied to mobile robotics. Being able to perceive household objects in unstructured environments is a key capability in order to make robots suitable to perform complex tasks in home environments. However, finding a solution for this task is daunting: it requires the ability to handle the variability in image formation in a moving camera with tight time constraints. The paper brings to attention some of the issues with applying three state of the art object recognition and detection methods in a mobile robotics scenario, and proposes methods to deal with windowing/segmentation. Thus, this work aims at evaluating the state-of-the-art in object perception in an attempt to develop a lightweight solution for mobile robotics use/research in typical indoor settings. | ||||
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Publisher | Springer Netherlands | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0921-0296 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS | Approved | no | ||
Call Number | Admin @ si @ RAV2012 | Serial | 2150 | ||
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Author ![]() |
Aura Hernandez-Sabate; Debora Gil | ||||
Title | The Benefits of IVUS Dynamics for Retrieving Stable Models of Arteries | Type | Book Chapter | ||
Year | 2012 | Publication | Intravascular Ultrasound | Abbreviated Journal | |
Volume | Issue | Pages | 185-206 | ||
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Corporate Author | Thesis | ||||
Publisher | Intech | Place of Publication | Editor | Yasuhiro Honda | |
Language | English | Summary Language | english | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-953-307-900-4 | Medium | ||
Area | Expedition | Conference | |||
Notes | IAM; ADAS | Approved | no | ||
Call Number | IAM @ iam @ HeG2012 | Serial | 1684 | ||
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Author ![]() |
Bhaskar Chakraborty | ||||
Title | Model free approach to human action recognition | Type | Book Whole | ||
Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
Volume | Issue | Pages | |||
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Abstract | Automatic understanding of human activity and action is very important and challenging research area of Computer Vision with wide applications in video surveillance, motion analysis, virtual reality interfaces, video indexing, content based video retrieval, HCI and health care. This thesis presents a series of techniques to solve the problem of human action recognition in video. First approach towards this goal is based on a probabilistic optimization model of body parts using Hidden Markov Model. This strong model based approach is able to distinguish between similar actions by only considering the body parts having major contributions to the actions. In next approach, we apply a weak model based human detector and actions are represented by Bag-of-key poses model to capture the human pose changes during the actions. To tackle the problem of human action recognition in complex scenes, a selective spatio-temporal interest point (STIP) detector is proposed by using a mechanism similar to that of the non-classical receptive field inhibition that is exhibited by most oriented selective neuron in the primary visual cortex. An extension of the selective STIP detector is applied to multi-view action recognition system by introducing a novel 4D STIPs (3D space + time). Finally, we use our STIP detector on large scale continuous visual event recognition problem and propose a novel generalized max-margin Hough transformation framework for activity detection | ||||
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Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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 @ Cha2012 | Serial | 2207 | ||
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Author ![]() |
Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez | ||||
Title | Selective Spatio-Temporal Interest Points | Type | Journal Article | ||
Year | 2012 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
Volume | 116 | Issue | 3 | Pages | 396-410 |
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Abstract | Recent progress in the field of human action recognition points towards the use of Spatio-TemporalInterestPoints (STIPs) for local descriptor-based recognition strategies. In this paper, we present a novel approach for robust and selective STIP detection, by applying surround suppression combined with local and temporal constraints. This new method is significantly different from existing STIP detection techniques and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-video words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on popular benchmark datasets (KTH and Weizmann), more challenging datasets of complex scenes with background clutter and camera motion (CVC and CMU), movie and YouTube video clips (Hollywood 2 and YouTube), and complex scenes with multiple actors (MSR I and Multi-KTH), validates our approach and show state-of-the-art performance. Due to the unavailability of ground truth action annotation data for the Multi-KTH dataset, we introduce an actor specific spatio-temporal clustering of STIPs to address the problem of automatic action annotation of multiple simultaneous actors. Additionally, we perform cross-data action recognition by training on source datasets (KTH and Weizmann) and testing on completely different and more challenging target datasets (CVC, CMU, MSR I and Multi-KTH). This documents the robustness of our proposed approach in the realistic scenario, using separate training and test datasets, which in general has been a shortcoming in the performance evaluation of human action recognition techniques. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1077-3142 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ CHM2012 | Serial | 1806 | ||
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Author ![]() |
Bogdan Raducanu; D. Gatica-Perez | ||||
Title | Inferring competitive role patterns in reality TV show through nonverbal analysis | Type | Journal Article | ||
Year | 2012 | Publication | Multimedia Tools and Applications | Abbreviated Journal | MTAP |
Volume | 56 | Issue | 1 | Pages | 207-226 |
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Abstract | This paper introduces a new facet of social media, namely that depicting social interaction. More concretely, we address this problem from the perspective of nonverbal behavior-based analysis of competitive meetings. For our study, we made use of “The Apprentice” 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. We address this problem by adopting both supervised and unsupervised strategies. The current study was carried out using nonverbal audio cues. Our approach is based only on the nonverbal interaction dynamics during the meeting without relying on the spoken words. The analysis is based on two types of data: individual and relational measures. Results obtained from the analysis of a full season of the show are promising (up to 85.7% of accuracy in the first case and up to 92.8% in the second case). Our approach has been conveniently compared with the Influence Model, demonstrating its superiority. | ||||
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1380-7501 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | OR;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ RaG2012 | Serial | 1360 | ||
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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 | 566.575 | |
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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. | ||||
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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 | ||
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Author ![]() |
Bogdan Raducanu; Fadi Dornaika | ||||
Title | Appearance-based Face Recognition Using A Supervised Manifold Learning Framework | Type | Conference Article | ||
Year | 2012 | Publication | IEEE Workshop on the Applications of Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 465-470 | ||
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Abstract | Many natural image sets, depicting objects whose appearance is changing due to motion, pose or light variations, can be considered samples of a low-dimension nonlinear manifold embedded in the high-dimensional observation space (the space of all possible images). The main contribution of our work is represented by a Supervised Laplacian Eigemaps (S-LE) algorithm, which exploits the class label information for mapping the original data in the embedded space. Our proposed approach benefits from two important properties: i) it is discriminative, and ii) it adaptively selects the neighbors of a sample without using any predefined neighborhood size. Experiments were conducted on four face databases and the results demonstrate that the proposed algorithm significantly outperforms many linear and non-linear embedding techniques. Although we've focused on the face recognition problem, the proposed approach could also be extended to other category of objects characterized by large variance in their appearance. | ||||
Address | Breckenridge; CO; USA | ||||
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 | 1550-5790 | ISBN | 978-1-4673-0233-3 | Medium | |
Area | Expedition | Conference | WACV | ||
Notes | OR;MV | Approved | no | ||
Call Number | Admin @ si @ RaD2012d | Serial | 1890 | ||
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Author ![]() |
Bogdan Raducanu; Fadi Dornaika | ||||
Title | A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning | Type | Journal Article | ||
Year | 2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
Volume | 45 | Issue | 6 | Pages | 2432-2444 |
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Abstract | IF= 2.61
IF=2.61 (2010) In this paper we introduce a novel supervised manifold learning technique called Supervised Laplacian Eigenmaps (S-LE), which makes use of class label information to guide the procedure of non-linear dimensionality reduction by adopting the large margin concept. The graph Laplacian is split into two components: within-class graph and between-class graph to better characterize the discriminant property of the data. Our approach has two important characteristics: (i) it adaptively estimates the local neighborhood surrounding each sample based on data density and similarity and (ii) the objective function simultaneously maximizes the local margin between heterogeneous samples and pushes the homogeneous samples closer to each other. Our approach has been tested on several challenging face databases and it has been conveniently compared with other linear and non-linear techniques, demonstrating its superiority. Although we have concentrated in this paper on the face recognition problem, the proposed approach could also be applied to other category of objects characterized by large variations in their appearance (such as hand or body pose, for instance. |
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Publisher | Elsevier | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 0031-3203 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | OR; MV | Approved | no | ||
Call Number | Admin @ si @ RaD2012a | Serial | 1884 | ||
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Author ![]() |
Bogdan Raducanu; Fadi Dornaika | ||||
Title | Out-of-Sample Embedding by Sparse Representation | Type | Conference Article | ||
Year | 2012 | Publication | Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop | Abbreviated Journal | |
Volume | 7626 | Issue | Pages | 336-344 | |
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Abstract | A critical aspect of non-linear dimensionality reduction techniques is represented by the construction of the adjacency graph. The difficulty resides in finding the optimal parameters, a process which, in general, is heuristically driven. Recently, sparse representation has been proposed as a non-parametric solution to overcome this problem. In this paper, we demonstrate that this approach not only serves for the graph construction, but also represents an efficient and accurate alternative for out-of-sample embedding. Considering for a case study the Laplacian Eigenmaps, we applied our method to the face recognition problem. Experimental results conducted on some challenging datasets confirmed the robustness of our approach and its superiority when compared to existing techniques. | ||||
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Corporate Author | Thesis | ||||
Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
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 @ RaD2012c | Serial | 2175 | ||
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