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Author |
Monica Piñol |
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Title |
Reinforcement Learning of Visual Descriptors for Object Recognition |
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Book Whole |
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Year |
2014 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
The human visual system is able to recognize the object in an image even if the object is partially occluded, from various points of view, in different colors, or with independence of the distance to the object. To do this, the eye obtains an image and extracts features that are sent to the brain, and then, in the brain the object is recognized. In computer vision, the object recognition branch tries to learns from the human visual system behaviour to achieve its goal. Hence, an algorithm is used to identify representative features of the scene (detection), then another algorithm is used to describe these points (descriptor) and finally the extracted information is used for classifying the object in the scene. The selection of this set of algorithms is a very complicated task and thus, a very active research field. In this thesis we are focused on the selection/learning of the best descriptor for a given image. In the state of the art there are several descriptors but we do not know how to choose the best descriptor because depends on scenes that we will use (dataset) and the algorithm chosen to do the classification. We propose a framework based on reinforcement learning and bag of features to choose the best descriptor according to the given image. The system can analyse the behaviour of different learning algorithms and descriptor sets. Furthermore the proposed framework for improving the classification/recognition ratio can be used with minor changes in other computer vision fields, such as video retrieval. |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Ricardo Toledo;Angel Sappa |
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978-84-940902-5-7 |
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Notes |
ADAS; 600.076 |
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no |
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Call Number |
Admin @ si @ Piñ2014 |
Serial |
2464 |
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Author |
Jiaolong Xu |
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Title |
Domain Adaptation of Deformable Part-based Models |
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Book Whole |
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Year |
2015 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Abstract |
On-board pedestrian detection is crucial for Advanced Driver Assistance Systems
(ADAS). An accurate classication is fundamental for vision-based pedestrian detection.
The underlying assumption for learning classiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classiers. However, in practice, there are dierent reasons that can break this constancy assumption. Accordingly, reusing existing classiers by adapting them from the previous training environment (source domain) to the new testing one (target domain) is an approach with increasing acceptance in the computer vision community. In this thesis we focus on the domain adaptation of deformable part-based models (DPMs) for pedestrian detection. As a prof of concept, we use a computer graphic based synthetic dataset, i.e. a virtual world, as the source domain, and adapt the virtual-world trained DPM detector to various real-world dataset.
We start by exploiting the maximum detection accuracy of the virtual-world
trained DPM. Even though, when operating in various real-world datasets, the virtualworld trained detector still suer from accuracy degradation due to the domain gap of virtual and real worlds. We then focus on domain adaptation of DPM. At the rst step, we consider single source and single target domain adaptation and propose two batch learning methods, namely A-SSVM and SA-SSVM. Later, we further consider leveraging multiple target (sub-)domains for progressive domain adaptation and propose a hierarchical adaptive structured SVM (HA-SSVM) for optimization. Finally, we extend HA-SSVM for the challenging online domain adaptation problem, aiming at making the detector to automatically adapt to the target domain online, without any human intervention. All of the proposed methods in this thesis do not require
revisiting source domain data. The evaluations are done on the Caltech pedestrian detection benchmark. Results show that SA-SSVM slightly outperforms A-SSVM and avoids accuracy drops as high as 15 points when comparing with a non-adapted detector. The hierarchical model learned by HA-SSVM further boosts the domain adaptation performance. Finally, the online domain adaptation method has demonstrated that it can achieve comparable accuracy to the batch learned models while not requiring manually label target domain examples. Domain adaptation for pedestrian detection is of paramount importance and a relatively unexplored area. We humbly hope the work in this thesis could provide foundations for future work in this area. |
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April 2015 |
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Corporate Author |
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Thesis |
Ph.D. thesis |
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Place of Publication |
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Editor |
Antonio Lopez |
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978-84-943427-1-4 |
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Notes |
ADAS; 600.076 |
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no |
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Call Number |
Admin @ si @ Xu2015 |
Serial |
2631 |
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Author |
Alejandro Gonzalez Alzate |
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Title |
Multi-modal Pedestrian Detection |
Type |
Book Whole |
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Year |
2015 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
Abbreviated Journal |
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Abstract |
Pedestrian detection continues to be an extremely challenging problem in real scenarios, in which situations like illumination changes, noisy images, unexpected objects, uncontrolled scenarios and variant appearance of objects occur constantly. All these problems force the development of more robust detectors for relevant applications like vision-based autonomous vehicles, intelligent surveillance, and pedestrian tracking for behavior analysis. Most reliable vision-based pedestrian detectors base their decision on features extracted using a single sensor capturing complementary features, e.g., appearance, and texture. These features usually are extracted from the current frame, ignoring temporal information, or including it in a post process step e.g., tracking or temporal coherence. Taking into account these issues we formulate the following question: can we generate more robust pedestrian detectors by introducing new information sources in the feature extraction step?
In order to answer this question we develop different approaches for introducing new information sources to well-known pedestrian detectors. We start by the inclusion of temporal information following the Stacked Sequential Learning (SSL) paradigm which suggests that information extracted from the neighboring samples in a sequence can improve the accuracy of a base classifier.
We then focus on the inclusion of complementary information from different sensors like 3D point clouds (LIDAR – depth), far infrared images (FIR), or disparity maps (stereo pair cameras). For this end we develop a multi-modal framework in which information from different sensors is used for increasing detection accuracy (by increasing information redundancy). Finally we propose a multi-view pedestrian detector, this multi-view approach splits the detection problem in n sub-problems.
Each sub-problem will detect objects in a given specific view reducing in that way the variability problem faced when a single detectors is used for the whole problem. We show that these approaches obtain competitive results with other state-of-the-art methods but instead of design new features, we reuse existing ones boosting their performance. |
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November 2015 |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
David Vazquez;Antonio Lopez; |
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ISBN |
978-84-943427-7-6 |
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Notes |
ADAS; 600.076 |
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no |
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Call Number |
Admin @ si @ Gon2015 |
Serial |
2706 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa; David Geronimo |
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Title |
Interactive Training of Human Detectors |
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Book Chapter |
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Year |
2013 |
Publication |
Multiodal Interaction in Image and Video Applications |
Abbreviated Journal |
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Volume |
48 |
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Pages |
169-182 |
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Keywords |
Pedestrian Detection; Virtual World; AdaBoost; Domain Adaptation |
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Abstract |
Image based human detection remains as a challenging problem. Most promising detectors rely on classifiers trained with labelled samples. However, labelling is a manual labor intensive step. To overcome this problem we propose to collect images of pedestrians from a virtual city, i.e., with automatic labels, and train a pedestrian detector with them, which works fine when such virtual-world data are similar to testing one, i.e., real-world pedestrians in urban areas. When testing data is acquired in different conditions than training one, e.g., human detection in personal photo albums, dataset shift appears. In previous work, we cast this problem as one of domain adaptation and solve it with an active learning procedure. In this work, we focus on the same problem but evaluating a different set of faster to compute features, i.e., Haar, EOH and their combination. In particular, we train a classifier with virtual-world data, using such features and Real AdaBoost as learning machine. This classifier is applied to real-world training images. Then, a human oracle interactively corrects the wrong detections, i.e., few miss detections are manually annotated and some false ones are pointed out too. A low amount of manual annotation is fixed as restriction. Real- and virtual-world difficult samples are combined within what we call cool world and we retrain the classifier with this data. Our experiments show that this adapted classifier is equivalent to the one trained with only real-world data but requiring 90% less manual annotations. |
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Address |
Springer Heidelberg New York Dordrecht London |
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Publisher |
Springer Berlin Heidelberg |
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English |
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ISSN |
1868-4394 |
ISBN |
978-3-642-35931-6 |
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Notes |
ADAS; 600.057; 600.054; 605.203 |
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no |
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Call Number |
VLP2013; ADAS @ adas @ vlp2013 |
Serial |
2193 |
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Author |
Ricardo Toledo |
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Title |
Cardiac workstation and dynamic model to assist in coronary tree analysis. |
Type |
Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Thesis |
Ph.D. thesis |
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Editor |
Petia Radeva;JuanJose Villanueva |
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ADAS |
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Call Number |
Admin @ si @ Tol2001 |
Serial |
166 |
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Author |
Antonio Lopez |
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Title |
Multilocal Methods for Ridge and Valley Delineation in Image Analysis. |
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Book Whole |
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2000 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Ph.D. thesis |
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Editor |
Joan Serrat |
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ADAS |
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Call Number |
ADAS @ adas @ Lop2000 |
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174 |
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Author |
Felipe Lumbreras |
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Title |
Segmentation, classification and modelization of textures by means of multiresolution decomposition techniques. |
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Book Whole |
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Year |
2001 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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ADAS |
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Call Number |
ADAS @ adas @ Lum2001 |
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188 |
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Author |
Angel Sappa; Niki Aifanti; N. Grammalidis; Sotiris Malassiotis |
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Title |
Advances in Vision-Based Human Body Modeling |
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Book Chapter |
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2004 |
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3D Modeling & Animation: Systhesis and Analysis Techniques for the Human Body |
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1-26 |
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N. Sarris and M. Strintzis. |
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1-59140-299-9 |
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ADAS |
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Call Number |
ADAS @ adas @ SAG2004a |
Serial |
458 |
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Author |
Angel Sappa; Fadi Dornaika |
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Title |
An Edge-Based Approach to Motion Detection |
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2006 |
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6th International Conference on Computational Science (ICCS´06), LNCS 3991:563–570 |
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Reading (United Kingdom) |
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ADAS |
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ADAS @ adas @ SaD2006 |
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654 |
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Author |
Fadi Dornaika; Angel Sappa |
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Title |
3D Face Tracking using Appearance Registration and Robust Iterative Closest Point Algorithm |
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2006 |
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21st International Symposium on Computer and Information Sciences (ISCIS´06), LNCS 4263: 532–541 |
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Istanbul (Turkey) |
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ADAS |
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Call Number |
ADAS @ adas @ DoS2006d |
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688 |
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