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Patricia Marquez, Debora Gil, Aura Hernandez-Sabate and Daniel Kondermann. 2013. When Is A Confidence Measure Good Enough? 9th International Conference on Computer Vision Systems. Springer Link, 344–353. (LNCS.)
Abstract: Confidence estimation has recently become a hot topic in image processing and computer vision.Yet, several definitions exist of the term “confidence” which are sometimes used interchangeably. This is a position paper, in which we aim to give an overview on existing definitions,
thereby clarifying the meaning of the used terms to facilitate further research in this field. Based on these clarifications, we develop a theory to compare confidence measures with respect to their quality.
Keywords: Optical flow, confidence measure, performance evaluation
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Gemma Rotger, Francesc Moreno-Noguer, Felipe Lumbreras and Antonio Agudo. 2019. Single view facial hair 3D reconstruction. 9th Iberian Conference on Pattern Recognition and Image Analysis.423–436. (LNCS.)
Abstract: n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches.
Keywords: 3D Vision; Shape Reconstruction; Facial Hair Modeling
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Mohammad Rouhani and Angel Sappa. 2009. A Novel Approach to Geometric Fitting of Implicit Quadrics. 8th International Conference on Advanced Concepts for Intelligent Vision Systems. Springer Berlin Heidelberg, 121–132. (LNCS.)
Abstract: This paper presents a novel approach for estimating the geometric distance from a given point to the corresponding implicit quadric curve/surface. The proposed estimation is based on the height of a tetrahedron, which is used as a coarse but reliable estimation of the real distance. The estimated distance is then used for finding the best set of quadric parameters, by means of the Levenberg-Marquardt algorithm, which is a common framework in other geometric fitting approaches. Comparisons of the proposed approach with previous ones are provided to show both improvements in CPU time as well as in the accuracy of the obtained results.
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Naveen Onkarappa and Angel Sappa. 2010. On-Board Monocular Vision System Pose Estimation through a Dense Optical Flow. 7th International Conference on Image Analysis and Recognition. Springer Berlin Heidelberg, 230–239. (LNCS.)
Abstract: 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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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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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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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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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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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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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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