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Author |
Ishaan Gulrajani; Kundan Kumar; Faruk Ahmed; Adrien Ali Taiga; Francesco Visin; David Vazquez; Aaron Courville |
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Title |
PixelVAE: A Latent Variable Model for Natural Images |
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Conference Article |
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Year |
2017 |
Publication |
5th International Conference on Learning Representations |
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Deep Learning; Unsupervised Learning |
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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. |
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Toulon; France; April 2017 |
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ICLR |
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ADAS; 600.085; 600.076; 601.281; 600.118 |
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ADAS @ adas @ GKA2017 |
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2815 |
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Author |
Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Automatic Ground-truthing using video registration for on-board detection algorithms |
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Conference Article |
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Year |
2009 |
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16th IEEE International Conference on Image Processing |
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4389 - 4392 |
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Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ADAS @ adas @ ADS2009 |
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1201 |
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Author |
Angel Sappa; Mohammad Rouhani |
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Title |
Efficient Distance Estimation for Fitting Implicit Quadric Surfaces |
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Conference Article |
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2009 |
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16th IEEE International Conference on Image Processing |
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3521–3524 |
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This paper presents a novel approach for estimating the shortest Euclidean distance from a given point to the corresponding implicit quadric fitting surface. It first estimates the orthogonal orientation to the surface from the given point; then the shortest distance is directly estimated by intersecting the implicit surface with a line passing through the given point according to the estimated orthogonal orientation. The proposed orthogonal distance estimation is easily obtained without increasing computational complexity; hence it can be used in error minimization surface fitting frameworks. Comparisons of the proposed metric with previous approaches are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. Surfaces fitted by using the proposed geometric distance estimation and state of the art metrics are presented to show the viability of the proposed approach. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ADAS |
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ADAS @ adas @ SaR2009 |
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1232 |
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Author |
Fernando Barrera; Felipe Lumbreras; Angel Sappa |
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Title |
Multimodal Template Matching based on Gradient and Mutual Information using Scale-Space |
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Conference Article |
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Year |
2010 |
Publication |
17th IEEE International Conference on Image Processing |
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2749–2752 |
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This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresolution context and (ii) information propagation through scale-space representations. Our method consists in combining mutual information with a shape descriptor based on gradient, and propagate them following a coarse-to-fine strategy. The main contributions of this work are: to offer a theoretical formulation towards a multimodal stereo matching; to show that gradient and mutual information can be reinforced while they are propagated between consecutive levels; and to show that they are valid cost functions in multimodal template matchings. Comparisons are presented showing the improvements and viability of the proposed approach. |
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Hong-Kong |
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1522-4880 |
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978-1-4244-7992-4 |
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ADAS |
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ADAS @ adas @ BLS2010 |
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1358 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
A Fast accurate Implicit Polynomial Fitting Approach |
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Conference Article |
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Year |
2010 |
Publication |
17th IEEE International Conference on Image Processing |
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1429–1432 |
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This paper presents a novel hybrid approach that combines state of the art fitting algorithms: algebraic-based and geometric-based. It consists of two steps; first, the 3L algorithm is used as an initialization and then, the obtained result, is improved through a geometric approach. The adopted geometric approach is based on a distance estimation that avoids costly search for the real orthogonal distance. Experimental results are presented as well as quantitative comparisons. |
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Hong-Kong |
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1522-4880 |
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978-1-4244-7992-4 |
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ADAS |
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ADAS @ adas @ RoS2010b |
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1359 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Implicit B-Spline Fitting Using the 3L Algorithm |
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Conference Article |
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2011 |
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18th IEEE International Conference on Image Processing |
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893-896 |
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Brussels, Belgium |
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ADAS |
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Admin @ si @ RoS2011a; ADAS @ adas @ |
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1782 |
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Author |
Cristhian A. Aguilera-Carrasco; Angel Sappa; Ricardo Toledo |
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Title |
LGHD: a Feature Descriptor for Matching Across Non-Linear Intensity Variations |
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Conference Article |
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2015 |
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22th IEEE International Conference on Image Processing |
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178 - 181 |
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Quebec; Canada; September 2015 |
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ADAS; 600.076 |
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Admin @ si @ AST2015 |
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2630 |
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Author |
Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu |
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Title |
Joint coarse-and-fine reasoning for deep optical flow |
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Conference Article |
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2017 |
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24th International Conference on Image Processing |
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2558-2562 |
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We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets. |
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Beijing; China; September 2017 |
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ADAS; 600.118 |
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Admin @ si @ VRM2017 |
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2898 |
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Author |
Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Comparing Combinations of Feature Regions for Panoramic VSLAM |
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2007 |
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4th International Conference on Informatics in Control, Automation and Robotics |
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292–297 |
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Angers (France) |
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ICINCO |
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RV;ADAS |
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Admin @ si @ RLA2007 |
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900 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title |
On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow |
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Conference Article |
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2010 |
Publication |
7th International Conference on Image Analysis and Recognition |
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6111 |
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230-239 |
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This paper presents a robust technique for estimating on-board monocular vision system pose. The proposed approach is based on a dense optical flow that is robust against shadows, reflections and illumination changes. A RANSAC based scheme is used to cope with the outliers in the optical flow. The proposed technique is intended to be used in driver assistance systems for applications such as obstacle or pedestrian detection. Experimental results on different scenarios, both from synthetic and real sequences, shows usefulness of the proposed approach. |
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Povoa de Varzim (Portugal) |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-13771-6 |
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ICIAR |
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ADAS @ adas @ OnS2010 |
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1342 |
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