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Hanne Kause and 6 others. 2015. Quality Assessment of Optical Flow in Tagging MRI. 5th Dutch Bio-Medical Engineering Conference BME2015.
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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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Aura Hernandez-Sabate, Lluis Albarracin, Daniel Calvo and Nuria Gorgorio. 2016. EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game. 5th International Conference Games and Learning Alliance.50–59. (LNCS.)
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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Carme Julia, Angel Sappa, Felipe Lumbreras and Antonio Lopez. 2008. Recovery of Surface Normals and Reflectance from Different Lighting Conditions. 5th International Conference on Image Analysis and Recognition.315–325. (LNCS.)
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Ishaan Gulrajani and 6 others. 2017. PixelVAE: A Latent Variable Model for Natural Images. 5th International Conference on Learning Representations.
Abstract: Natural image modeling is a landmark challenge of unsupervised learning. Variational Autoencoders (VAEs) learn a useful latent representation and generate samples that preserve global structure but tend to suffer from image blurriness. PixelCNNs model sharp contours and details very well, but lack an explicit latent representation and have difficulty modeling large-scale structure in a computationally efficient way. In this paper, we present PixelVAE, a VAE model with an autoregressive decoder based on PixelCNN. The resulting architecture achieves state-of-the-art log-likelihood on binarized MNIST. We extend PixelVAE to a hierarchy of multiple latent variables at different scales; this hierarchical model achieves competitive likelihood on 64x64 ImageNet and generates high-quality samples on LSUN bedrooms.
Keywords: Deep Learning; Unsupervised Learning
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David Aldavert, Ricardo Toledo, Arnau Ramisa and Ramon Lopez de Mantaras. 2009. Efficient Object Pixel-Level Categorization using Bag of Features: Advances in Visual Computing. 5th International Symposium on Visual Computing. Springer Berlin Heidelberg, 44–55.
Abstract: In this paper we present a pixel-level object categorization method suitable to be applied under real-time constraints. Since pixels are categorized using a bag of features scheme, the major bottleneck of such an approach would be the feature pooling in local histograms of visual words. Therefore, we propose to bypass this time-consuming step and directly obtain the score from a linear Support Vector Machine classifier. This is achieved by creating an integral image of the components of the SVM which can readily obtain the classification score for any image sub-window with only 10 additions and 2 products, regardless of its size. Besides, we evaluated the performance of two efficient feature quantization methods: the Hierarchical K-Means and the Extremely Randomized Forest. All experiments have been done in the Graz02 database, showing comparable, or even better results to related work with a lower computational cost.
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Carme Julia, Angel Sappa, Felipe Lumbreras, Joan Serrat and Antonio Lopez. 2006. Factorization with Missing and Noisy Data. 6th International Conference on Computational Science.555–562.
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Muhammad Anwer Rao, Fahad Shahbaz Khan, Joost Van de Weijer and Jorma Laaksonen. 2016. Combining Holistic and Part-based Deep Representations for Computational Painting Categorization. 6th International Conference on Multimedia Retrieval.
Abstract: Automatic analysis of visual art, such as paintings, is a challenging inter-disciplinary research problem. Conventional approaches only rely on global scene characteristics by encoding holistic information for computational painting categorization.We argue that such approaches are sub-optimal and that discriminative common visual structures provide complementary information for painting classification. We present an approach that encodes both the global scene layout and discriminative latent common structures for computational painting categorization. The region of interests are automatically extracted, without any manual part labeling, by training class-specific deformable part-based models. Both holistic and region-of-interests are then described using multi-scale dense convolutional features. These features are pooled separately using Fisher vector encoding and concatenated afterwards in a single image representation. Experiments are performed on a challenging dataset with 91 different painters and 13 diverse painting styles. Our approach outperforms the standard method, which only employs the global scene characteristics. Furthermore, our method achieves state-of-the-art results outperforming a recent multi-scale deep features based approach [11] by 6.4% and 3.8% respectively on artist and style classification.
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David Aldavert, Ricardo Toledo, Arnau Ramisa and Ramon Lopez de Mantaras. 2009. Visual Registration Method For A Low Cost Robot: Computer Vision Systems. 7th International Conference on Computer Vision Systems. Springer Berlin Heidelberg, 204–214. (LNCS.)
Abstract: An autonomous mobile robot must face the correspondence or data association problem in order to carry out tasks like place recognition or unknown environment mapping. In order to put into correspondence two maps, most methods estimate the transformation relating the maps from matches established between low level feature extracted from sensor data. However, finding explicit matches between features is a challenging and computationally expensive task. In this paper, we propose a new method to align obstacle maps without searching explicit matches between features. The maps are obtained from a stereo pair. Then, we use a vocabulary tree approach to identify putative corresponding maps followed by the Newton minimization algorithm to find the transformation that relates both maps. The proposed method is evaluated in a typical office environment showing good performance.
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Diego Cheda, Daniel Ponsa and Antonio Lopez. 2012. Monocular Depth-based Background Estimation. 7th International Conference on Computer Vision Theory and Applications.323–328.
Abstract: In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences.
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