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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg | ||||
| Title | Coloring Action Recognition in Still Images | Type  |
Journal Article | ||
| Year | 2013 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 105 | Issue | 3 | Pages | 205-221 |
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| Abstract | In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0920-5691 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC; ADAS; 600.057; 600.048 | Approved | no | ||
| Call Number | Admin @ si @ KRW2013 | Serial | 2285 | ||
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| Author | Jordi Roca; C. Alejandro Parraga; Maria Vanrell | ||||
| Title | Chromatic settings and the structural color constancy index | Type |
Journal Article | ||
| Year | 2013 | Publication | Journal of Vision | Abbreviated Journal | JV |
| Volume | 13 | Issue | 4-3 | Pages | 1-26 |
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| Abstract | Color constancy is usually measured by achromatic setting, asymmetric matching, or color naming paradigms, whose results are interpreted in terms of indexes and models that arguably do not capture the full complexity of the phenomenon. Here we propose a new paradigm, chromatic setting, which allows a more comprehensive characterization of color constancy through the measurement of multiple points in color space under immersive adaptation. We demonstrated its feasibility by assessing the consistency of subjects' responses over time. The paradigm was applied to two-dimensional (2-D) Mondrian stimuli under three different illuminants, and the results were used to fit a set of linear color constancy models. The use of multiple colors improved the precision of more complex linear models compared to the popular diagonal model computed from gray. Our results show that a diagonal plus translation matrix that models mechanisms other than cone gain might be best suited to explain the phenomenon. Additionally, we calculated a number of color constancy indices for several points in color space, and our results suggest that interrelations among colors are not as uniform as previously believed. To account for this variability, we developed a new structural color constancy index that takes into account the magnitude and orientation of the chromatic shift in addition to the interrelations among colors and memory effects. | ||||
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| Notes | CIC; 600.052; 600.051; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ RPV2013 | Serial | 2288 | ||
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| Author | Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga | ||||
| Title | Low-level SpatioChromatic Grouping for Saliency Estimation | Type |
Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 35 | Issue | 11 | Pages | 2810-2816 |
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| Abstract | We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC; 600.051; 600.052; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ MVO2013 | Serial | 2289 | ||
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| Author | Jaume Gibert; Ernest Valveny; Horst Bunke | ||||
| Title | Embedding of Graphs with Discrete Attributes Via Label Frequencies | Type |
Journal Article | ||
| Year | 2013 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal | IJPRAI |
| Volume | 27 | Issue | 3 | Pages | 1360002-1360029 |
| Keywords | Discrete attributed graphs; graph embedding; graph classification | ||||
| Abstract | Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient. | ||||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GVB2013 | Serial | 2305 | ||
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| Author | Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez | ||||
| Title | Road Geometry Classification by Adaptative Shape Models | Type |
Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
| Volume | 14 | Issue | 1 | Pages | 459-468 |
| Keywords | road detection | ||||
| Abstract | Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1524-9050 | ISBN | Medium | ||
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| Notes | ADAS;ISE | Approved | no | ||
| Call Number | Admin @ si @ AGD2013;; ADAS @ adas @ | Serial | 2269 | ||
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| Author | Santiago Segui; Laura Igual; Jordi Vitria | ||||
| Title | Bagged One Class Classifiers in the Presence of Outliers | Type |
Journal Article | ||
| Year | 2013 | Publication | International Journal of Pattern Recognition and Artificial Intelligence | Abbreviated Journal | IJPRAI |
| Volume | 27 | Issue | 5 | Pages | 1350014-1350035 |
| Keywords | One-class Classifier; Ensemble Methods; Bagging and Outliers | ||||
| Abstract | The problem of training classifiers only with target data arises in many applications where non-target data are too costly, difficult to obtain, or not available at all. Several one-class classification methods have been presented to solve this problem, but most of the methods are highly sensitive to the presence of outliers in the target class. Ensemble methods have therefore been proposed as a powerful way to improve the classification performance of binary/multi-class learning algorithms by introducing diversity into classifiers.
However, their application to one-class classification has been rather limited. In this paper, we present a new ensemble method based on a non-parametric weighted bagging strategy for one-class classification, to improve accuracy in the presence of outliers. While the standard bagging strategy assumes a uniform data distribution, the method we propose here estimates a probability density based on a forest structure of the data. This assumption allows the estimation of data distribution from the computation of simple univariate and bivariate kernel densities. Experiments using original and noisy versions of 20 different datasets show that bagging ensemble methods applied to different one-class classifiers outperform base one-class classification methods. Moreover, we show that, in noisy versions of the datasets, the non-parametric weighted bagging strategy we propose outperforms the classical bagging strategy in a statistically significant way. |
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| Notes | OR; 600.046;MV | Approved | no | ||
| Call Number | Admin @ si @ SIV2013 | Serial | 2256 | ||
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| Author | Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera | ||||
| Title | Automatic Digital Biometry Analysis based on Depth Maps | Type |
Journal Article | ||
| Year | 2013 | Publication | Computers in Industry | Abbreviated Journal | COMPUTIND |
| Volume | 64 | Issue | 9 | Pages | 1316-1325 |
| Keywords | Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis | ||||
| Abstract | World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ RCR2013 | Serial | 2252 | ||
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| Author | Albert Clapes; Miguel Reyes; Sergio Escalera | ||||
| Title | Multi-modal User Identification and Object Recognition Surveillance System | Type |
Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 34 | Issue | 7 | Pages | 799-808 |
| Keywords | Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning | ||||
| Abstract | We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | HUPBA; 600.046; 605.203;MILAB | Approved | no | ||
| Call Number | Admin @ si @ CRE2013 | Serial | 2248 | ||
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| Author | Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera | ||||
| Title | A Genetic-based Subspace Analysis Method for Improving Error-Correcting Output Coding | Type |
Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 46 | Issue | 10 | Pages | 2830-2839 |
| Keywords | Error Correcting Output Codes; Evolutionary computation; Multiclass classification; Feature subspace; Ensemble classification | ||||
| Abstract | Two key factors affecting the performance of Error Correcting Output Codes (ECOC) in multiclass classification problems are the independence of binary classifiers and the problem-dependent coding design. In this paper, we propose an evolutionary algorithm-based approach to the design of an application-dependent codematrix in the ECOC framework. The central idea of this work is to design a three-dimensional codematrix, where the third dimension is the feature space of the problem domain. In order to do that, we consider the feature space in the design process of the codematrix with the aim of improving the independence and accuracy of binary classifiers. The proposed method takes advantage of some basic concepts of ensemble classification, such as diversity of classifiers, and also benefits from the evolutionary approach for optimizing the three-dimensional codematrix, taking into account the problem domain. We provide a set of experimental results using a set of benchmark datasets from the UCI Machine Learning Repository, as well as two real multiclass Computer Vision problems. Both sets of experiments are conducted using two different base learners: Neural Networks and Decision Trees. The results show that the proposed method increases the classification accuracy in comparison with the state-of-the-art ECOC coding techniques. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | HuPBA;MILAB | Approved | no | ||
| Call Number | Admin @ si @ BGE2013a | Serial | 2247 | ||
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| Author | Fernando Barrera; Felipe Lumbreras; Angel Sappa | ||||
| Title | Multispectral Piecewise Planar Stereo using Manhattan-World Assumption | Type |
Journal Article | ||
| Year | 2013 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 34 | Issue | 1 | Pages | 52-61 |
| Keywords | Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images | ||||
| Abstract | This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework. | ||||
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| Notes | ADAS; 600.054; 600.055; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ BLS2013 | Serial | 2245 | ||
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| Author | Naveen Onkarappa; Angel Sappa | ||||
| Title | A Novel Space Variant Image Representation | Type |
Journal Article | ||
| Year | 2013 | Publication | Journal of Mathematical Imaging and Vision | Abbreviated Journal | JMIV |
| Volume | 47 | Issue | 1-2 | Pages | 48-59 |
| Keywords | Space-variant representation; Log-polar mapping; Onboard vision applications | ||||
| Abstract | Traditionally, in machine vision images are represented using cartesian coordinates with uniform sampling along the axes. On the contrary, biological vision systems represent images using polar coordinates with non-uniform sampling. For various advantages provided by space-variant representations many researchers are interested in space-variant computer vision. In this direction the current work proposes a novel and simple space variant representation of images. The proposed representation is compared with the classical log-polar mapping. The log-polar representation is motivated by biological vision having the characteristic of higher resolution at the fovea and reduced resolution at the periphery. On the contrary to the log-polar, the proposed new representation has higher resolution at the periphery and lower resolution at the fovea. Our proposal is proved to be a better representation in navigational scenarios such as driver assistance systems and robotics. The experimental results involve analysis of optical flow fields computed on both proposed and log-polar representations. Additionally, an egomotion estimation application is also shown as an illustrative example. The experimental analysis comprises results from synthetic as well as real sequences. | ||||
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| Publisher | Springer US | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0924-9907 | ISBN | Medium | ||
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| Notes | ADAS; 600.055; 605.203; 601.215 | Approved | no | ||
| Call Number | Admin @ si @ OnS2013a | Serial | 2243 | ||
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| Author | Olivier Penacchio; Xavier Otazu; Laura Dempere-Marco | ||||
| Title | A Neurodynamical Model of Brightness Induction in V1 | Type |
Journal Article | ||
| Year | 2013 | Publication | PloS ONE | Abbreviated Journal | Plos |
| Volume | 8 | Issue | 5 | Pages | e64086 |
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| Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. Recent neurophysiological evidence suggests that brightness information might be explicitly represented in V1, in contrast to the more common assumption that the striate cortex is an area mostly responsive to sensory information. Here we investigate possible neural mechanisms that offer a plausible explanation for such phenomenon. To this end, a neurodynamical model which is based on neurophysiological evidence and focuses on the part of V1 responsible for contextual influences is presented. The proposed computational model successfully accounts for well known psychophysical effects for static contexts and also for brightness induction in dynamic contexts defined by modulating the luminance of surrounding areas. This work suggests that intra-cortical interactions in V1 could, at least partially, explain brightness induction effects and reveals how a common general architecture may account for several different fundamental processes, such as visual saliency and brightness induction, which emerge early in the visual processing pathway. | ||||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ POD2013 | Serial | 2242 | ||
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| Author | Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| Title | Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation | Type |
Journal Article | ||
| Year | 2013 | Publication | Ultrasound in Medicine and Biology | Abbreviated Journal | UMB |
| Volume | 39 | Issue | 9 | Pages | 1698-712 |
| Keywords | Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy | ||||
| Abstract | Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages. | ||||
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| Notes | MILAB | Approved | no | ||
| Call Number | Admin @ si @ ABC2013 | Serial | 2313 | ||
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| Author | Bogdan Raducanu; Fadi Dornaika | ||||
| Title | Texture-independent recognition of facial expressions in image snapshots and videos | Type |
Journal Article | ||
| Year | 2013 | Publication | Machine Vision and Applications | Abbreviated Journal | MVA |
| Volume | 24 | Issue | 4 | Pages | 811-820 |
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| Abstract | This paper addresses the static and dynamic recognition of basic facial expressions. It has two main contributions. First, we introduce a view- and texture-independent scheme that exploits facial action parameters estimated by an appearance-based 3D face tracker. We represent the learned facial actions associated with different facial expressions by time series. Second, we compare this dynamic scheme with a static one based on analyzing individual snapshots and show that the former performs better than the latter. We provide evaluations of performance using three subspace learning techniques: linear discriminant analysis, non-parametric discriminant analysis and support vector machines. | ||||
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| Publisher | Springer-Verlag | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0932-8092 | ISBN | Medium | ||
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| Notes | OR; 600.046; 605.203;MV | Approved | no | ||
| Call Number | Admin @ si @ RaD2013 | Serial | 2230 | ||
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| Author | Ferran Diego; Joan Serrat; Antonio Lopez | ||||
| Title | Joint spatio-temporal alignment of sequences | Type |
Journal Article | ||
| Year | 2013 | Publication | IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
| Volume | 15 | Issue | 6 | Pages | 1377-1387 |
| Keywords | video alignment | ||||
| Abstract | Video alignment is important in different areas of computer vision such as wide baseline matching, action recognition, change detection, video copy detection and frame dropping prevention. Current video alignment methods usually deal with a relatively simple case of fixed or rigidly attached cameras or simultaneous acquisition. Therefore, in this paper we propose a joint video alignment for bringing two video sequences into a spatio-temporal alignment. Specifically, the novelty of the paper is to formulate the video alignment to fold the spatial and temporal alignment into a single alignment framework. This simultaneously satisfies a frame-correspondence and frame-alignment similarity; exploiting the knowledge among neighbor frames by a standard pairwise Markov random field (MRF). This new formulation is able to handle the alignment of sequences recorded at different times by independent moving cameras that follows a similar trajectory, and also generalizes the particular cases that of fixed geometric transformation and/or linear temporal mapping. We conduct experiments on different scenarios such as sequences recorded simultaneously or by moving cameras to validate the robustness of the proposed approach. The proposed method provides the highest video alignment accuracy compared to the state-of-the-art methods on sequences recorded from vehicles driving along the same track at different times. | ||||
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| ISSN | 1520-9210 | ISBN | Medium | ||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ DSL2013; ADAS @ adas @ | Serial | 2228 | ||
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