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Jiaolong Xu, Sebastian Ramos, David Vazquez and Antonio Lopez. 2014. Incremental Domain Adaptation of Deformable Part-based Models. In Valstar, M. and F., Andrew and Pridmore, Tony, ed. 25th British Machine Vision Conference. BMVA Press.
Abstract: Nowadays, classifiers play a core role in many computer vision tasks. The underlying assumption for learning classifiers is that the training set and the deployment environment (testing) follow the same probability distribution regarding the features used by the classifiers. However, in practice, there are different reasons that can break this constancy assumption. Accordingly, reusing existing classifiers 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 paper we focus on the domain adaptation of deformable part-based models (DPMs) for object detection. In particular, we focus on a relatively unexplored scenario, i.e. incremental domain adaptation for object detection assuming weak-labeling. Therefore, our algorithm is ready to improve existing source-oriented DPM-based detectors as soon as a little amount of labeled target-domain training data is available, and keeps improving as more of such data arrives in a continuous fashion. For achieving this, we follow a multiple
instance learning (MIL) paradigm that operates in an incremental per-image basis. As proof of concept, we address the challenging scenario of adapting a DPM-based pedestrian detector trained with synthetic pedestrians to operate in real-world scenarios. The obtained results show that our incremental adaptive models obtain equally good accuracy results as the batch learned models, while being more flexible for handling continuously arriving target-domain data.
Keywords: Pedestrian Detection; Part-based models; Domain Adaptation
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Aura Hernandez-Sabate, Debora Gil, David Roche, Monica M. S. Matsumoto and Sergio S. Furuie. 2011. Inferring the Performance of Medical Imaging Algorithms. In Pedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano and Walter Kropatsch, eds. 14th International Conference on Computer Analysis of Images and Patterns. Berlin, Springer-Verlag Berlin Heidelberg, 520–528. (LNCS.)
Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.
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Muhammad Anwer Rao, David Vazquez and Antonio Lopez. 2011. Color Contribution to Part-Based Person Detection in Different Types of Scenarios. In P. Real, D.D., H. Molina, A. Berciano, W. Kropatsch, ed. 14th International Conference on Computer Analysis of Images and Patterns. Berlin Heidelberg, Springer, 463–470.
Abstract: Camera-based person detection is of paramount interest due to its potential applications. The task is diffcult because the great variety of backgrounds (scenarios, illumination) in which persons are present, as well as their intra-class variability (pose, clothe, occlusion). In fact, the class person is one of the included in the popular PASCAL visual object classes (VOC) challenge. A breakthrough for this challenge, regarding person detection, is due to Felzenszwalb et al. These authors proposed a part-based detector that relies on histograms of oriented gradients (HOG) and latent support vector machines (LatSVM) to learn a model of the whole human body and its constitutive parts, as well as their relative position. Since the approach of Felzenszwalb et al. appeared new variants have been proposed, usually giving rise to more complex models. In this paper, we focus on an issue that has not attracted suficient interest up to now. In particular, we refer to the fact that HOG is usually computed from RGB color space, but other possibilities exist and deserve the corresponding investigation. In this paper we challenge RGB space with the opponent color space (OPP), which is inspired in the human vision system.We will compute the HOG on top of OPP, then we train and test the part-based human classifer by Felzenszwalb et al. using PASCAL VOC challenge protocols and person database. Our experiments demonstrate that OPP outperforms RGB. We also investigate possible differences among types of scenarios: indoor, urban and countryside. Interestingly, our experiments suggest that the beneficts of OPP with respect to RGB mainly come for indoor and countryside scenarios, those in which the human visual system was designed by evolution.
Keywords: Pedestrian Detection; Color
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Naveen Onkarappa and Angel Sappa. 2011. Space Variant Representations for Mobile Platform Vision Applications. In P. Real, D.D., H. Molina, A. Berciano, W. Kropatsch, ed. 14th International Conference on Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 146–154.
Abstract: The log-polar space variant representation, motivated by biological vision, has been widely studied in the literature. Its data reduction and invariance properties made it useful in many vision applications. However, due to its nature, it fails in preserving features in the periphery. In the current work, as an attempt to overcome this problem, we propose a novel space-variant representation. It is evaluated and proved to be better than the log-polar representation in preserving the peripheral information, crucial for on-board mobile vision applications. The evaluation is performed by comparing log-polar and the proposed representation once they are used for estimating dense optical flow.
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Marçal Rusiñol, David Aldavert, Dimosthenis Karatzas, Ricardo Toledo and Josep Llados. 2011. Interactive Trademark Image Retrieval by Fusing Semantic and Visual Content. Advances in Information Retrieval. In P. Clough and 6 others, eds. 33rd European Conference on Information Retrieval. Berlin, Springer, 314–325. (LNCS.)
Abstract: In this paper we propose an efficient queried-by-example retrieval system which is able to retrieve trademark images by similarity from patent and trademark offices' digital libraries. Logo images are described by both their semantic content, by means of the Vienna codes, and their visual contents, by using shape and color as visual cues. The trademark descriptors are then indexed by a locality-sensitive hashing data structure aiming to perform approximate k-NN search in high dimensional spaces in sub-linear time. The resulting ranked lists are combined by using the Condorcet method and a relevance feedback step helps to iteratively revise the query and refine the obtained results. The experiments demonstrate the effectiveness and efficiency of this system on a realistic and large dataset.
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Muhammad Anwer Rao, David Vazquez and Antonio Lopez. 2011. Opponent Colors for Human Detection. In J. Vitria, J.M. Sanches and M. Hernandez, eds. 5th Iberian Conference on Pattern Recognition and Image Analysis. Berlin Heidelberg, Springer, 363–370. (LNCS.)
Abstract: Human detection is a key component in fields such as advanced driving assistance and video surveillance. However, even detecting non-occluded standing humans remains a challenge of intensive research. Finding good features to build human models for further detection is probably one of the most important issues to face. Currently, shape, texture and motion features have deserve extensive attention in the literature. However, color-based features, which are important in other domains (e.g., image categorization), have received much less attention. In fact, the use of RGB color space has become a kind of choice by default. The focus has been put in developing first and second order features on top of RGB space (e.g., HOG and co-occurrence matrices, resp.). In this paper we evaluate the opponent colors (OPP) space as a biologically inspired alternative for human detection. In particular, by feeding OPP space in the baseline framework of Dalal et al. for human detection (based on RGB, HOG and linear SVM), we will obtain better detection performance than by using RGB space. This is a relevant result since, up to the best of our knowledge, OPP space has not been previously used for human detection. This suggests that in the future it could be worth to compute co-occurrence matrices, self-similarity features, etc., also on top of OPP space, i.e., as we have done with HOG in this paper.
Keywords: Pedestrian Detection; Color; Part Based Models
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2007. Motion Segmentation from Feature Trajectories with Missing Data. In J. Marti et al.(Eds.), ed. 3rd. Iberian Conference on Pattern Recognition and Image Analysis.483–490.
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David Geronimo, Antonio Lopez and Angel Sappa. 2007. Computer Vision Approaches for Pedestrian Detection: Visible Spectrum Survey. In J. Marti et al., ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477.547–554.
Abstract: 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.
Keywords: Pedestrian detection
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David Geronimo, Antonio Lopez, Daniel Ponsa and Angel Sappa. 2007. Haar Wavelets and Edge Orientation Histograms for On-Board Pedestrian Detection. In J. Marti et al., ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis, LNCS 4477.418–425.
Keywords: Pedestrian detection
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Joan Serrat, Ferran Diego, Felipe Lumbreras and Jose Manuel Alvarez. 2007. Synchronization of Video Sequences from Free-moving Cameras. In J. Marti et al., ed. 3rd Iberian Conference on Pattern Recognition and Image Analysis.620–627. (LNCS.)
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