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Author  |
Ivo Everts; Jan van Gemert; Theo Gevers | ||||
| Title | Evaluation of Color STIPs for Human Action Recognition | Type | Conference Article | ||
| Year | 2013 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2850-2857 | ||
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| Abstract | This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF~sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition. | ||||
| Address | Portland; oregon; June 2013 | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | Medium | ||
| Area | Expedition | Conference | CVPR | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ EGG2013 | Serial | 2364 | ||
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| Author |
J.S. Cope; P.Remagnino; S.Mannan; Katerine Diaz; Francesc J. Ferri; P.Wilkin | ||||
| Title | Reverse Engineering Expert Visual Observations: From Fixations To The Learning Of Spatial Filters With A Neural-Gas Algorithm | Type | Journal Article | ||
| Year | 2013 | Publication | Expert Systems with Applications | Abbreviated Journal | EXWA |
| Volume | 40 | Issue | 17 | Pages | 6707-6712 |
| Keywords | Neural gas; Expert vision; Eye-tracking; Fixations | ||||
| Abstract | Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give high responses to image regions which a human expert fixated on. These filters can then be used to identify regions in other images which are likely to be useful for a given vision task. The algorithm is evaluated by learning filters for locating specific areas of plant leaves. | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0957-4174 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ CRM2013 | Serial | 2438 | ||
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| Author |
Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders | ||||
| Title | Selective Search for Object Recognition | Type | Journal Article | ||
| Year | 2013 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 104 | Issue | 2 | Pages | 154-171 |
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| Abstract | This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html). | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0920-5691 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ USG2013 | Serial | 2362 | ||
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| Author |
Jaume Amores | ||||
| Title | Multiple Instance Classification: review, taxonomy and comparative study | Type | Journal Article | ||
| Year | 2013 | Publication | Artificial Intelligence | Abbreviated Journal | AI |
| Volume | 201 | Issue | Pages | 81-105 | |
| Keywords | Multi-instance learning; Codebook; Bag-of-Words | ||||
| Abstract | Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL methods. |
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| Publisher | Elsevier Science Publishers Ltd. Essex, UK | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0004-3702 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ADAS; 601.042; 600.057 | Approved | no | ||
| Call Number | Admin @ si @ Amo2013 | Serial | 2273 | ||
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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 |
Javier Marin | ||||
| Title | Pedestrian Detection Based on Local Experts | Type | Book Whole | ||
| Year | 2013 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
| Volume | Issue | Pages | |||
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| Abstract | During the last decade vision-based human detection systems have started to play a key rolein multiple applications linked to driver assistance, surveillance, robot sensing and home automation.
Detecting humans is by far one of the most challenging tasks in Computer Vision. This is mainly due to the high degree of variability in the human appearanceassociated to the clothing, pose, shape and size. Besides, other factors such as cluttered scenarios, partial occlusions, or environmental conditions can make the detection task even harder. Most promising methods of the state-of-the-art rely on discriminative learning paradigms which are fed with positive and negative examples. The training data is one of the most relevant elements in order to build a robust detector as it has to cope the large variability of the target. In order to create this dataset human supervision is required. The drawback at this point is the arduous effort of annotating as well as looking for such claimed variability. In this PhD thesis we address two recurrent problems in the literature. In the first stage,we aim to reduce the consuming task of annotating, namely, by using computer graphics. More concretely, we develop a virtual urban scenario for later generating a pedestrian dataset. Then, we train a detector using this dataset, and finally we assess if this detector can be successfully applied in a real scenario. In the second stage, we focus on increasing the robustness of our pedestrian detectors under partial occlusions. In particular, we present a novel occlusion handling approach to increase the performance of block-based holistic methods under partial occlusions. For this purpose, we make use of local experts via a RandomSubspaceMethod (RSM) to handle these cases. If the method infers a possible partial occlusion, then the RSM, based on performance statistics obtained from partially occluded data, is applied. The last objective of this thesis is to propose a robust pedestrian detector based on an ensemble of local experts. To achieve this goal, we use the random forest paradigm, where the trees act as ensembles an their nodesare the local experts. In particular, each expert focus on performing a robust classification ofa pedestrian body patch. This approach offers computational efficiency and far less design complexity when compared to other state-of-the-artmethods, while reaching better accuracy |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Antonio Lopez;Jaume Amores | |
| Language | Summary Language | Original Title | |||
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| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ Mar2013 | Serial | 2280 | ||
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| Author |
Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe | ||||
| Title | Random Forests of Local Experts for Pedestrian Detection | Type | Conference Article | ||
| Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 2592 - 2599 | ||
| Keywords | ADAS; Random Forest; Pedestrian Detection | ||||
| Abstract | Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1550-5499 | ISBN | Medium | ||
| Area | Expedition | Conference | ICCV | ||
| Notes | ADAS; 600.057; 600.054 | Approved | no | ||
| Call Number | ADAS @ adas @ MVL2013 | Serial | 2333 | ||
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| Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa | ||||
| Title | Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection | Type | Conference Article | ||
| Year | 2013 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
| Volume | Issue | Pages | 467 - 472 | ||
| Keywords | Pedestrian Detection; Virtual World; Part based | ||||
| Abstract | State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster). | ||||
| Address | Gold Coast; Australia; June 2013 | ||||
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| Publisher | IEEE | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1931-0587 | ISBN | 978-1-4673-2754-1 | Medium | |
| Area | Expedition | Conference | IV | ||
| Notes | ADAS; 600.054; 600.057 | Approved | no | ||
| Call Number | XVL2013; ADAS @ adas @ xvl2013a | Serial | 2214 | ||
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| Author |
Jiaolong Xu; David Vazquez; Sebastian Ramos; Antonio Lopez; Daniel Ponsa | ||||
| Title | Adapting a Pedestrian Detector by Boosting LDA Exemplar Classifiers | Type | Conference Article | ||
| Year | 2013 | Publication | CVPR Workshop on Ground Truth – What is a good dataset? | Abbreviated Journal | |
| Volume | Issue | Pages | 688 - 693 | ||
| Keywords | Pedestrian Detection; Domain Adaptation | ||||
| Abstract | Training vision-based pedestrian detectors using synthetic datasets (virtual world) is a useful technique to collect automatically the training examples with their pixel-wise ground truth. However, as it is often the case, these detectors must operate in real-world images, experiencing a significant drop of their performance. In fact, this effect also occurs among different real-world datasets, i.e. detectors' accuracy drops when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, in order to avoid this problem, it is required to adapt the detector trained with synthetic data to operate in the real-world scenario. In this paper, we propose a domain adaptation approach based on boosting LDA exemplar classifiers from both virtual and real worlds. We evaluate our proposal on multiple real-world pedestrian detection datasets. The results show that our method can efficiently adapt the exemplar classifiers from virtual to real world, avoiding drops in average precision over the 15%. | ||||
| Address | Portland; oregon; June 2013 | ||||
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| Language | English | Summary Language | English | Original Title | |
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| Area | Expedition | Conference | CVPRW | ||
| Notes | ADAS; 600.054; 600.057; 601.217 | Approved | yes | ||
| Call Number | XVR2013; ADAS @ adas @ xvr2013a | Serial | 2220 | ||
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| Author |
Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
| Title | DA-DPM Pedestrian Detection | Type | Conference Article | ||
| Year | 2013 | Publication | ICCV Workshop on Reconstruction meets Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Domain Adaptation; Pedestrian Detection | ||||
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| Area | Expedition | Conference | ICCVW-RR | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ XRV2013 | Serial | 2569 | ||
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| Author |
Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez | ||||
| Title | Multi-task Bilinear Classifiers for Visual Domain Adaptation | Type | Conference Article | ||
| Year | 2013 | Publication | Advances in Neural Information Processing Systems Workshop | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Domain Adaptation; Pedestrian Detection; ADAS | ||||
| Abstract | We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation. The bilinear classifier encodes the feature transformation and classification parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
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| Address | Lake Tahoe; Nevada; USA; December 2013 | ||||
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| Area | Expedition | Conference | NIPSW | ||
| Notes | ADAS; 600.054; 600.057; 601.217;ISE | Approved | no | ||
| Call Number | ADAS @ adas @ XRH2013 | Serial | 2340 | ||
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| Author |
Joan M. Nuñez; Debora Gil; Fernando Vilariño | ||||
| Title | Finger joint characterization from X-ray images for rheymatoid arthritis assessment | Type | Conference Article | ||
| Year | 2013 | Publication | 6th International Conference on Biomedical Electronics and Devices | Abbreviated Journal | |
| Volume | Issue | Pages | 288-292 | ||
| Keywords | Rheumatoid Arthritis; X-Ray; Hand Joint; Sclerosis; Sharp Van der Heijde | ||||
| Abstract | In this study we propose amodular systemfor automatic rheumatoid arthritis assessment which provides a joint space width measure. A hand joint model is proposed based on the accurate analysis of a X-ray finger joint image sample set. This model shows that the sclerosis and the lower bone are the main necessary features in order to perform a proper finger joint characterization. We propose sclerosis and lower bone detection methods as well as the experimental setup necessary for its performance assessment. Our characterization is used to propose and compute a joint space width score which is shown to be related to the different degrees of arthritis. This assertion is verified by comparing our proposed score with Sharp Van der Heijde score, confirming that the lower our score is the more advanced is the patient affection. | ||||
| Address | Barcelona; February 2013 | ||||
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| Publisher | SciTePress | Place of Publication | Editor | ||
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| Area | 800 | Expedition | Conference | BIODEVICES | |
| Notes | IAM;MV; 600.057; 600.054;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ NGV2013 | Serial | 2196 | ||
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| Author |
Joan M. Nuñez; Jorge Bernal; F. Javier Sanchez; Fernando Vilariño | ||||
| Title | Blood Vessel Characterization in Colonoscopy Images to Improve Polyp Localization | Type | Conference Article | ||
| Year | 2013 | Publication | Proceedings of the International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 162-171 | |
| Keywords | Colonoscopy; Blood vessel; Linear features; Valley detection | ||||
| Abstract | This paper presents an approach to mitigate the contribution of blood vessels to the energy image used at different tasks of automatic colonoscopy image analysis. This goal is achieved by introducing a characterization of endoluminal scene objects which allows us to differentiate between the trace of 2-dimensional visual objects,such as vessels, and shades from 3-dimensional visual objects, such as folds. The proposed characterization is based on the influence that the object shape has in the resulting visual feature, and it leads to the development of a blood vessel attenuation algorithm. A database consisting of manually labelled masks was built in order to test the performance of our method, which shows an encouraging success in blood vessel mitigation while keeping other structures intact. Moreover, by extending our method to the only available polyp localization
algorithm tested on a public database, blood vessel mitigation proved to have a positive influence on the overall performance. |
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| Address | Barcelona; February 2013 | ||||
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| Publisher | SciTePress | Place of Publication | Editor | ||
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| Area | 800 | Expedition | Conference | VISIGRAPP | |
| Notes | MV; 600.054; 600.057;SIAI | Approved | no | ||
| Call Number | IAM @ iam @ NBS2013 | Serial | 2198 | ||
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| Author |
Joan Serrat; Felipe Lumbreras; Antonio Lopez | ||||
| Title | Cost estimation of custom hoses from STL files and CAD drawings | Type | Journal Article | ||
| Year | 2013 | Publication | Computers in Industry | Abbreviated Journal | COMPUTIND |
| Volume | 64 | Issue | 3 | Pages | 299-309 |
| Keywords | On-line quotation; STL format; Regression; Gaussian process | ||||
| Abstract | We present a method for the cost estimation of custom hoses from CAD models. They can come in two formats, which are easy to generate: a STL file or the image of a CAD drawing showing several orthogonal projections. The challenges in either cases are, first, to obtain from them a high level 3D description of the shape, and second, to learn a regression function for the prediction of the manufacturing time, based on geometric features of the reconstructed shape. The chosen description is the 3D line along the medial axis of the tube and the diameter of the circular sections along it. In order to extract it from STL files, we have adapted RANSAC, a robust parametric fitting algorithm. As for CAD drawing images, we propose a new technique for 3D reconstruction from data entered on any number of orthogonal projections. The regression function is a Gaussian process, which does not constrain the function to adopt any specific form and is governed by just two parameters. We assess the accuracy of the manufacturing time estimation by k-fold cross validation on 171 STL file models for which the time is provided by an expert. The results show the feasibility of the method, whereby the relative error for 80% of the testing samples is below 15%. | ||||
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| Publisher | Elsevier | Place of Publication | Editor | ||
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| Notes | ADAS; 600.057; 600.054; 605.203 | Approved | no | ||
| Call Number | Admin @ si @ SLL2013; ADAS @ adas @ | Serial | 2161 | ||
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| Author |
Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
| Title | Handwritten Word Spotting with Corrected Attributes | Type | Conference Article | ||
| Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 1017-1024 | ||
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| Abstract | We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. | ||||
| Address | Sydney; Australia; December 2013 | ||||
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| ISSN | 1550-5499 | ISBN | Medium | ||
| Area | Expedition | Conference | ICCV | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ AGF2013 | Serial | 2327 | ||
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