Jose Manuel Alvarez, Y. LeCun, Theo Gevers, & Antonio Lopez. (2012). Semantic Road Segmentation via Multi-Scale Ensembles of Learned Features. In 12th European Conference on Computer Vision – Workshops and Demonstrations (Vol. 7584, pp. 586–595). LNCS. Springer Berlin Heidelberg.
Abstract: Semantic segmentation refers to the process of assigning an object label (e.g., building, road, sidewalk, car, pedestrian) to every pixel in an image. Common approaches formulate the task as a random field labeling problem modeling the interactions between labels by combining local and contextual features such as color, depth, edges, SIFT or HoG. These models are trained to maximize the likelihood of the correct classification given a training set. However, these approaches rely on hand–designed features (e.g., texture, SIFT or HoG) and a higher computational time required in the inference process.
Therefore, in this paper, we focus on estimating the unary potentials of a conditional random field via ensembles of learned features. We propose an algorithm based on convolutional neural networks to learn local features from training data at different scales and resolutions. Then, diversification between these features is exploited using a weighted linear combination. Experiments on a publicly available database show the effectiveness of the proposed method to perform semantic road scene segmentation in still images. The algorithm outperforms appearance based methods and its performance is similar compared to state–of–the–art methods using other sources of information such as depth, motion or stereo.
Keywords: road detection
|
Jose Manuel Alvarez, Theo Gevers, Y. LeCun, & Antonio Lopez. (2012). Road Scene Segmentation from a Single Image. In 12th European Conference on Computer Vision (Vol. 7578, pp. 376–389). LNCS. Springer Berlin Heidelberg.
Abstract: Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding.
In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road image. The novelty of the algorithm relies on generating training labels by applying an algorithm trained on a general image dataset to classify on–board images. Further, we propose a novel texture descriptor based on a learned color plane fusion to obtain maximal uniformity in road areas. Finally, acquired (off–line) and current (on–line) information are combined to detect road areas in single images.
From quantitative and qualitative experiments, conducted on publicly available datasets, it is concluded that convolutional neural networks are suitable for learning 3D scene layout from noisy labels and provides a relative improvement of 7% compared to the baseline. Furthermore, combining color planes provides a statistical description of road areas that exhibits maximal uniformity and provides a relative improvement of 8% compared to the baseline. Finally, the improvement is even bigger when acquired and current information from a single image are combined
Keywords: road detection
|
Ivo Everts, Jan van Gemert, & Theo Gevers. (2012). Per-patch Descriptor Selection using Surface and Scene Properties. In 12th European Conference on Computer Vision (Vol. 7577, pp. 172–186). LNCS. Springer Berlin Heidelberg.
Abstract: Local image descriptors are generally designed for describing all possible image patches. Such patches may be subject to complex variations in appearance due to incidental object, scene and recording conditions. Because of this, a single-best descriptor for accurate image representation under all conditions does not exist. Therefore, we propose to automatically select from a pool of descriptors the one that is best suitable based on object surface and scene properties. These properties are measured on the fly from a single image patch through a set of attributes. Attributes are input to a classifier which selects the best descriptor. Our experiments on a large dataset of colored object patches show that the proposed selection method outperforms the best single descriptor and a-priori combinations of the descriptor pool.
|
Hamdi Dibeklioglu, Theo Gevers, & Albert Ali Salah. (2012). Are You Really Smiling at Me? Spontaneous versus Posed Enjoyment Smiles. In 12th European Conference on Computer Vision (Vol. 7574, pp. 525–538). LNCS. Springer Berlin Heidelberg.
Abstract: Smiling is an indispensable element of nonverbal social interaction. Besides, automatic distinction between spontaneous and posed expressions is important for visual analysis of social signals. Therefore, in this paper, we propose a method to distinguish between spontaneous and posed enjoyment smiles by using the dynamics of eyelid, cheek, and lip corner movements. The discriminative power of these movements, and the effect of different fusion levels are investigated on multiple databases. Our results improve the state-of-the-art. We also introduce the largest spontaneous/posed enjoyment smile database collected to date, and report new empirical and conceptual findings on smile dynamics. The collected database consists of 1240 samples of 400 subjects. Moreover, it has the unique property of having an age range from 8 to 76 years. Large scale experiments on the new database indicate that eyelid dynamics are highly relevant for smile classification, and there are age-related differences in smile dynamics.
|
Mohammad Rouhani, & Angel Sappa. (2012). Non-Rigid Shape Registration: A Single Linear Least Squares Framework. In 12th European Conference on Computer Vision (Vol. 7578, pp. 264–277). LNCS. Springer Berlin Heidelberg.
Abstract: This paper proposes a non-rigid registration formulation capturing both global and local deformations in a single framework. This formulation is based on a quadratic estimation of the registration distance together with a quadratic regularization term. Hence, the optimal transformation parameters are easily obtained by solving a liner system of equations, which guarantee a fast convergence. Experimental results with challenging 2D and 3D shapes are presented to show the validity of the proposed framework. Furthermore, comparisons with the most relevant approaches are provided.
|
Bogdan Raducanu, & Fadi Dornaika. (2012). Pose-Invariant Face Recognition in Videos for Human-Machine Interaction. In 12th European Conference on Computer Vision (Vol. 7584, 566.575). LNCS. Springer Berlin Heidelberg.
Abstract: Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach.
|
Maria Ines Torres, Javier Mikel Olaso, Cesar Montenegro, Riberto Santana, A.Vazquez, Raquel Justo, et al. (2019). The EMPATHIC project: mid-term achievements. In 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments (pp. 629–638).
Abstract: Maria Ines Torres; Javier Mikel Olaso, César Montenegro, Riberto Santana, A. Vázquez, Raquel Justo, J. A. Lozano, Stephan Schlögl, Gérard Chollet, Nazim Dugan, M. Irvine, N. Glackin, C. Pickard, Anna Esposito, Gennaro Cordasco, Alda Troncone, Dijana Petrovska-Delacrétaz, Aymen Mtibaa, Mohamed Amine Hmani, M. S. Korsnes, L. J. Martinussen, Sergio Escalera, C. Palmero Cantariño, Olivier Deroo, O. Gordeeva, Jofre Tenorio-Laranga, E. Gonzalez-Fraile, Begoña Fernández-Ruanova, A. Gonzalez-Pinto
|
|
Josep Llados, Jaime Lopez-Krahe, Gemma Sanchez, & Enric Marti. (2000). Interprétation de cartes et plans par mise en correspondance de graphes de attributs. In 12 Congrès Francophone AFRIF–AFIA (Vol. 3, pp. 225–234).
|
|
Fernando Barrera, Felipe Lumbreras, Cristhian Aguilera, & Angel Sappa. (2012). Planar-Based Multispectral Stereo. In 11th Quantitative InfraRed Thermography.
|
|
Cristhian Aguilera, Fernando Barrera, Angel Sappa, & Ricardo Toledo. (2012). A Novel SIFT-Like-Based Approach for FIR-VS Images Registration. In 11th Quantitative InfraRed Thermography.
|
Arnau Ramisa, David Aldavert, Shrihari Vasudevan, Ricardo Toledo, & Ramon Lopez de Mantaras. (2011). The IIIA30 MObile Robot Object Recognition Datset. In 11th Portuguese Robotics Open.
Abstract: Object perception is a key feature in order to make mobile robots able to perform high-level tasks. However, research aimed at addressing the constraints and limitations encountered in a mobile robotics scenario, like low image resolution, motion blur or tight computational constraints, is still very scarce. In order to facilitate future research in this direction, in this work we present an object detection and recognition dataset acquired using a mobile robotic platform. As a baseline for the dataset, we evaluated the cascade of weak classifiers object detection method from Viola and Jones.
|
Xavier Perez, Cecilio Angulo, & Sergio Escalera. (2011). Biologically Inspired Path Execution Using SURF Flow in Robot Navigation. In 11th International Work Conference on Artificial Neural Networks (Vol. II, pp. 581–588). Springer Berlin Heidelberg.
Abstract: An exportable and robust system using only camera images is proposed for path execution in robot navigation. Motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames, so the method is called SURF flow. This information is used to correct robot displacement when a straight forward path command is sent to the robot, but it is not really executed due to several robot and environmental concerns. The proposed system has been successfully tested on the legged robot Aibo.
|
Simone Zini, Alex Gomez-Villa, Marco Buzzelli, Bartlomiej Twardowski, Andrew D. Bagdanov, & Joost Van de Weijer. (2023). Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training. In 11th International Conference on Learning Representations.
Abstract: Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation – which we call Planckian Jitter – that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information. Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets. In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations.
|
Francesco Brughi, Debora Gil, Llorenç Badiella, Eva Jove Casabella, & Oriol Ramos Terrades. (2014). Exploring the impact of inter-query variability on the performance of retrieval systems. In 11th International Conference on Image Analysis and Recognition (Vol. 8814, 413–420). LNCS. Springer International Publishing.
Abstract: This paper introduces a framework for evaluating the performance of information retrieval systems. Current evaluation metrics provide an average score that does not consider performance variability across the query set. In this manner, conclusions lack of any statistical significance, yielding poor inference to cases outside the query set and possibly unfair comparisons. We propose to apply statistical methods in order to obtain a more informative measure for problems in which different query classes can be identified. In this context, we assess the performance variability on two levels: overall variability across the whole query set and specific query class-related variability. To this end, we estimate confidence bands for precision-recall curves, and we apply ANOVA in order to assess the significance of the performance across different query classes.
|
|
A. Pujol, H. Wechsler, & Juan J. Villanueva. (2001). Learning and Caricaturing the Face Space Using Self-Organization and Hebbian Learning for Face Processing..
|
