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Author |
Mariella Dimiccoli; Benoît Girard; Alain Berthoz; Daniel Bennequin |
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Title |
Striola Magica: a functional explanation of otolith organs |
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Journal Article |
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Year |
2013 |
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Journal of Computational Neuroscience |
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JCN |
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35 |
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2 |
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125-154 |
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Otolith organs ;Striola; Vestibular pathway |
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Abstract |
Otolith end organs of vertebrates sense linear accelerations of the head and gravitation. The hair cells on their epithelia are responsible for transduction. In mammals, the striola, parallel to the line where hair cells reverse their polarization, is a narrow region centered on a curve with curvature and torsion. It has been shown that the striolar region is functionally different from the rest, being involved in a phasic vestibular pathway. We propose a mathematical and computational model that explains the necessity of this amazing geometry for the striola to be able to carry out its function. Our hypothesis, related to the biophysics of the hair cells and to the physiology of their afferent neurons, is that striolar afferents collect information from several type I hair cells to detect the jerk in a large domain of acceleration directions. This predicts a mean number of two calyces for afferent neurons, as measured in rodents. The domain of acceleration directions sensed by our striolar model is compatible with the experimental results obtained on monkeys considering all afferents. Therefore, the main result of our study is that phasic and tonic vestibular afferents cover the same geometrical fields, but at different dynamical and frequency domains. |
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Springer US |
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1573-6873. 2013 |
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MILAB |
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Admin @ si @DBG2013 |
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2787 |
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Author |
R. Bertrand; P. Gomez-Krämer; Oriol Ramos Terrades; P. Franco; Jean-Marc Ogier |
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Title |
A System Based On Intrinsic Features for Fraudulent Document Detection |
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Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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106-110 |
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paper document; document analysis; fraudulent document; forgery; fake |
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Abstract |
Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer froma lack of security.
However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one.
In this paper, we present an automatic forgery detection method based on document’s intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set iscomputed for all characters. Then, based on a distance between characters of the same class. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; 600.061 |
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Admin @ si @ BGR2013a |
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2332 |
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Author |
Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan |
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Title |
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach |
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Journal Article |
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Year |
2016 |
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Journal of Machine Vision and Applications |
Abbreviated Journal |
MVAP |
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27 |
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511-527 |
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particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging |
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In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor
quality data, particles and trajectories can be characterized by an a-contrario
model, that estimates the probability of observing the structures of interest
in random data. This approach, first introduced in the modeling of human visual
perception and then successfully applied in many image processing tasks, leads
to algorithms that do not require a previous learning stage, nor a tedious
parameter tuning and are very robust to noise. Comparative evaluations against
a well established baseline show that the proposed approach outperforms the
state of the art. |
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MILAB; |
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Admin @ si @ DJM2016 |
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2735 |
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Author |
V. Kober; Mikhail Mozerov; J. Alvarez-Borrego; I.A. Ovseyevich |
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Title |
Adaptive Correlation Filters for Pattern Recognition |
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Journal |
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Year |
2006 |
Publication |
Pattern Recognition and Image Analysis |
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16 |
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3 |
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425-431 |
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Pattern recognition, Correlation filters, A adaptive filters |
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Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. |
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ISE |
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ISE @ ise @ KMA2006a |
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673 |
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Author |
Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados; Thierry Brouard |
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Title |
Fuzzy Multilevel Graph Embedding |
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Journal Article |
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Year |
2013 |
Publication |
Pattern Recognition |
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PR |
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46 |
Issue |
2 |
Pages |
551-565 |
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Pattern recognition; Graphics recognition; Graph clustering; Graph classification; Explicit graph embedding; Fuzzy logic |
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Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ LRL2013a |
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2270 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
An Overview of Symbol Recognition |
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Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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D |
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523-551 |
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Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting |
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According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ TaT2014 |
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2489 |
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Author |
David Vazquez; David Geronimo; Antonio Lopez |
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Title |
The effect of the distance in pedestrian detection |
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Report |
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2009 |
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CVC Technical Report |
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149 |
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Pedestrian Detection |
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Pedestrian accidents are one of the leading preventable causes of death. In order to reduce the number of accidents, in the last decade the pedestrian protection systems have been introduced, a special type of advanced driver assistance systems, in witch an on-board camera explores the road ahead for possible collisions with pedestrians in order to warn the driver or perform braking actions. As a result of the variability of the appearance, pose and size, pedestrian detection is a very challenging task. So many techniques, models and features have been proposed to solve the problem. As the appearance of pedestrians varies signicantly as a function of distance, a system based on multiple classiers specialized on diferent depths is likely to improve the overall performance with respect to a typical system based on a general detector. Accordingly, the main aim of this work is to explore the eect of the distance in pedestrian detection. We have evaluated three pedestrian detectors (HOG, HAAR and EOH) in two dierent databases (INRIA and Daimler09) for two dierent sizes (small and big). By a extensive set of experiments we answer to questions like which datasets and evaluation methods are the most adequate, which is the best method for each size of the pedestrians and why or how do the method optimum parameters vary with respect to the distance |
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Master's thesis |
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M.Sc. |
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ADAS |
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ADAS @ adas @ VGL2009 |
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1669 |
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Author |
David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa |
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Title |
Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection |
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Conference Article |
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Year |
2007 |
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Proceedings of the 5th International Conference on Computer Vision Systems |
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ICVS |
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Pedestrian Detection |
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On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera
pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one. |
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Bielefeld (Germany) |
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ADAS @ adas @ gsl2007a |
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786 |
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Author |
David Geronimo; Antonio Lopez; Angel Sappa |
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Title |
Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey |
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Conference Article |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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1 |
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547–554 |
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Pedestrian detection |
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Pedestrian detection from images of the visible spectrum is a high relevant area of research given its potential impact in the design of pedestrian protection systems. There are many proposals in the literature but they lack a comparative viewpoint. According to this, in this paper we first propose a common framework where we fit the different approaches, and second we use this framework to provide a comparative point of view of the details of such different approaches, pointing out also the main challenges to be solved in the future. In summary, we expect
this survey to be useful for both novel and experienced researchers in the field. In the first case, as a clarifying snapshot of the state of the art; in the second, as a way to unveil trends and to take conclusions from the comparative study. |
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Girona (Spain) |
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J. Marti et al. |
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ADAS |
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ADAS @ adas @ GLS2007 |
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804 |
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Author |
David Geronimo; Antonio Lopez; Daniel Ponsa; Angel Sappa |
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Title |
Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection |
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Conference Article |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477 |
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1 |
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418–425 |
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Pedestrian detection |
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Girona (Spain) |
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J. Marti et al. |
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ADAS @ adas @ GLP2007a |
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805 |
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David Geronimo; Angel Sappa; Antonio Lopez |
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Title |
Stereo-based Candidate Generation for Pedestrian Protection Systems |
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Book Chapter |
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2010 |
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Binocular Vision: Development, Depth Perception and Disorders |
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9 |
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189–208 |
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Pedestrian Detection |
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This chapter describes a stereo-based algorithm that provides candidate image windows to a latter 2D classification stage in an on-board pedestrian detection system. The proposed algorithm, which consists of three stages, is based on the use of both stereo imaging and scene prior knowledge (i.e., pedestrians are on the ground) to reduce the candidate searching space. First, a successful road surface fitting algorithm provides estimates on the relative ground-camera pose. This stage directs the search toward the road area thus avoiding irrelevant regions like the sky. Then, three different schemes are used to scan the estimated road surface with pedestrian-sized windows: (a) uniformly distributed through the road surface (3D); (b) uniformly distributed through the image (2D); (c) not uniformly distributed but according to a quadratic function (combined 2D-3D). Finally, the set of candidate windows is reduced by analyzing their 3D content. Experimental results of the proposed algorithm, together with statistics of searching space reduction are provided. |
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NOVA Publishers |
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ADAS @ adas @ GSL2010 |
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1301 |
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Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez |
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Title |
Color Attributes for Object Detection |
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Conference Article |
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2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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3306-3313 |
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pedestrian detection |
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Abstract |
State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape.
In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe-
art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
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Providence; Rhode Island; USA; |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CVPR |
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Notes |
ADAS; CIC; |
Approved |
no |
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Call Number |
Admin @ si @ KRW2012 |
Serial |
1935 |
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Permanent link to this record |
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Author |
Diego Cheda; Daniel Ponsa; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Pedestrian Candidates Generation using Monocular Cues |
Type |
Conference Article |
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Year |
2012 |
Publication |
IEEE Intelligent Vehicles Symposium |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
7-12 |
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Keywords ![sorted by Keywords field, ascending order (up)](img/sort_asc.gif) |
pedestrian detection |
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Abstract |
Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
IEEE Xplore |
Place of Publication |
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Editor |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1931-0587 |
ISBN |
978-1-4673-2119-8 |
Medium |
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Area |
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Expedition |
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Conference |
IV |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d |
Serial |
2013 |
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Permanent link to this record |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Mohammad Rouhani; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Moving object detection from mobile platforms using stereo data registration |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Computational Intelligence paradigms in advanced pattern classification |
Abbreviated Journal |
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Volume |
386 |
Issue |
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Pages |
25-37 |
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Keywords ![sorted by Keywords field, ascending order (up)](img/sort_asc.gif) |
pedestrian detection |
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Abstract |
This chapter describes a robust approach for detecting moving objects from on-board stereo vision systems. It relies on a feature point quaternion-based registration, which avoids common problems that appear when computationally expensive iterative-based algorithms are used on dynamic environments. The proposed approach consists of three main stages. Initially, feature points are extracted and tracked through consecutive 2D frames. Then, a RANSAC based approach is used for registering two point sets, with known correspondences in the 3D space. The computed 3D rigid displacement is used to map two consecutive 3D point clouds into the same coordinate system by means of the quaternion method. Finally, moving objects correspond to those areas with large 3D registration errors. Experimental results show the viability of the proposed approach to detect moving objects like vehicles or pedestrians in different urban scenarios. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Marek R. Ogiela; Lakhmi C. Jain |
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Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1860-949X |
ISBN |
978-3-642-24048-5 |
Medium |
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Area |
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Expedition |
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Conference |
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Notes |
ADAS |
Approved |
no |
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Call Number |
Admin @ si @ SGD2012 |
Serial |
2061 |
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Permanent link to this record |
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Author |
Alejandro Gonzalez Alzate; Gabriel Villalonga; Jiaolong Xu; David Vazquez; Jaume Amores; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection |
Type |
Conference Article |
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Year |
2015 |
Publication |
IEEE Intelligent Vehicles Symposium IV2015 |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
356-361 |
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Keywords ![sorted by Keywords field, ascending order (up)](img/sort_asc.gif) |
Pedestrian Detection |
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Abstract |
Despite recent significant advances, pedestrian detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities and a strong multi-view classifier that accounts for different pedestrian views and poses. In this paper we provide an extensive evaluation that gives insight into how each of these aspects (multi-cue, multimodality and strong multi-view classifier) affect performance both individually and when integrated together. In the multimodality component we explore the fusion of RGB and depth maps obtained by high-definition LIDAR, a type of modality that is only recently starting to receive attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the performance, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient. These simple blocks can be easily replaced with more sophisticated ones recently proposed, such as the use of convolutional neural networks for feature representation, to further improve the accuracy. |
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Address |
Seoul; Corea; June 2015 |
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Series Editor |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
ACDC |
Expedition |
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Conference |
IV |
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Notes |
ADAS; 600.076; 600.057; 600.054 |
Approved |
no |
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Call Number |
ADAS @ adas @ GVX2015 |
Serial |
2625 |
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Permanent link to this record |