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Author Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich
Title DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition Type Conference Article
Year 2015 Publication Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II Abbreviated Journal
Volume 9475 Issue Pages 463-473
Keywords Projector-camera systems; Feature descriptors; Object recognition
Abstract Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection.
Address
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-319-27862-9 Medium
Area Expedition Conference ISVC
Notes CIC Approved no
Call Number Admin @ si @ SMG2015 Serial 2736
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Author Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez
Title GPU-based pedestrian detection for autonomous driving Type Conference Article
Year 2016 Publication GPU Technology Conference Abbreviated Journal
Volume Issue Pages
Keywords Pedestrian Detection; GPU
Abstract Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results.
Address Silicon Valley; San Francisco; USA; April 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GTC
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ CSM2016 Serial 2737
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Author Daniel Hernandez; Juan Carlos Moure; Toni Espinosa; Alejandro Chacon; David Vazquez; Antonio Lopez
Title Real-time 3D Reconstruction for Autonomous Driving via Semi-Global Matching Type Conference Article
Year 2016 Publication GPU Technology Conference Abbreviated Journal
Volume Issue Pages
Keywords Stereo; Autonomous Driving; GPU; 3d reconstruction
Abstract Robust and dense computation of depth information from stereo-camera systems is a computationally demanding requirement for real-time autonomous driving. Semi-Global Matching (SGM) [1] approximates heavy-computation global algorithms results but with lower computational complexity, therefore it is a good candidate for a real-time implementation. SGM minimizes energy along several 1D paths across the image. The aim of this work is to provide a real-time system producing reliable results on energy-efficient hardware. Our design runs on a NVIDIA Titan X GPU at 104.62 FPS and on a NVIDIA Drive PX at 6.7 FPS, promising for real-time platforms
Address Silicon Valley; San Francisco; USA; April 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference GTC
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ HME2016 Serial 2738
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Author German Ros; Laura Sellart; Joanna Materzynska; David Vazquez; Antonio Lopez
Title The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes Type Conference Article
Year 2016 Publication 29th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal
Volume Issue Pages 3234-3243
Keywords Domain Adaptation; Autonomous Driving; Virtual Data; Semantic Segmentation
Abstract Vision-based semantic segmentation in urban scenarios is a key functionality for autonomous driving. The irruption of deep convolutional neural networks (DCNNs) allows to foresee obtaining reliable classifiers to perform such a visual task. However, DCNNs require to learn many parameters from raw images; thus, having a sufficient amount of diversified images with this class annotations is needed. These annotations are obtained by a human cumbersome labour specially challenging for semantic segmentation, since pixel-level annotations are required. In this paper, we propose to use a virtual world for automatically generating realistic synthetic images with pixel-level annotations. Then, we address the question of how useful can be such data for the task of semantic segmentation; in particular, when using a DCNN paradigm. In order to answer this question we have generated a synthetic diversified collection of urban images, named SynthCity, with automatically generated class annotations. We use SynthCity in combination with publicly available real-world urban images with manually provided annotations. Then, we conduct experiments on a DCNN setting that show how the inclusion of SynthCity in the training stage significantly improves the performance of the semantic segmentation task
Address Las Vegas; USA; June 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CVPR
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ RSM2016 Serial 2739
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Author Daniel Hernandez; Alejandro Chacon; Antonio Espinosa; David Vazquez; Juan Carlos Moure; Antonio Lopez
Title Embedded real-time stereo estimation via Semi-Global Matching on the GPU Type Conference Article
Year 2016 Publication 16th International Conference on Computational Science Abbreviated Journal
Volume 80 Issue Pages 143-153
Keywords Autonomous Driving; Stereo; CUDA; 3d reconstruction
Abstract Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles. Semi-Global Matching (SGM) is a widely used algorithm that propagates consistency constraints along several paths across the image. This work presents a real-time system producing reliable disparity estimation results on the new embedded energy-efficient GPU devices. Our design runs on a Tegra X1 at 41 frames per second for an image size of 640x480, 128 disparity levels, and using 4 path directions for the SGM method.
Address San Diego; CA; USA; June 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCS
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ HCE2016a Serial 2740
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Author Victor Campmany; Sergio Silva; Antonio Espinosa; Juan Carlos Moure; David Vazquez; Antonio Lopez
Title GPU-based pedestrian detection for autonomous driving Type Conference Article
Year 2016 Publication 16th International Conference on Computational Science Abbreviated Journal
Volume 80 Issue Pages 2377-2381
Keywords Pedestrian detection; Autonomous Driving; CUDA
Abstract We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.
Address San Diego; CA; USA; June 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICCS
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ CSE2016 Serial 2741
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Author Maedeh Aghaei; Mariella Dimiccoli; Petia Radeva
Title Multi-face tracking by extended bag-of-tracklets in egocentric photo-streams Type Journal Article
Year 2016 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 149 Issue Pages 146-156
Keywords
Abstract Wearable cameras offer a hands-free way to record egocentric images of daily experiences, where social events are of special interest. The first step towards detection of social events is to track the appearance of multiple persons involved in them. In this paper, we propose a novel method to find correspondences of multiple faces in low temporal resolution egocentric videos acquired through a wearable camera. This kind of photo-stream imposes additional challenges to the multi-tracking problem with respect to conventional videos. Due to the free motion of the camera and to its low temporal resolution, abrupt changes in the field of view, in illumination condition and in the target location are highly frequent. To overcome such difficulties, we propose a multi-face tracking method that generates a set of tracklets through finding correspondences along the whole sequence for each detected face and takes advantage of the tracklets redundancy to deal with unreliable ones. Similar tracklets are grouped into the so called extended bag-of-tracklets (eBoT), which is aimed to correspond to a specific person. Finally, a prototype tracklet is extracted for each eBoT, where the occurred occlusions are estimated by relying on a new measure of confidence. We validated our approach over an extensive dataset of egocentric photo-streams and compared it to state of the art methods, demonstrating its effectiveness and robustness.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; Approved no
Call Number Admin @ si @ ADR2016b Serial 2742
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Author Hugo Jair Escalante; Victor Ponce; Sergio Escalera; Xavier Baro; Alicia Morales-Reyes; Jose Martinez-Carranza
Title Evolving weighting schemes for the Bag of Visual Words Type Journal Article
Year 2017 Publication Neural Computing and Applications Abbreviated Journal Neural Computing and Applications
Volume 28 Issue 5 Pages 925–939
Keywords Bag of Visual Words; Bag of features; Genetic programming; Term-weighting schemes; Computer vision
Abstract The Bag of Visual Words (BoVW) is an established representation in computer vision. Taking inspiration from text mining, this representation has proved
to be very effective in many domains. However, in most cases, standard term-weighting schemes are adopted (e.g.,term-frequency or TF-IDF). It remains open the question of whether alternative weighting schemes could boost the
performance of methods based on BoVW. More importantly, it is unknown whether it is possible to automatically learn and determine effective weighting schemes from
scratch. This paper brings some light into both of these unknowns. On the one hand, we report an evaluation of the most common weighting schemes used in text mining, but rarely used in computer vision tasks. Besides, we propose an evolutionary algorithm capable of automatically learning weighting schemes for computer vision problems. We report empirical results of an extensive study in several computer vision problems. Results show the usefulness of the proposed method.
Address
Corporate Author Thesis
Publisher Place of Publication Editor Springer
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA;MV; no menciona Approved no
Call Number Admin @ si @ EPE2017 Serial 2743
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Author Onur Ferhat; Fernando Vilariño
Title Low Cost Eye Tracking: The Current Panorama Type Journal Article
Year 2016 Publication Computational Intelligence and Neuroscience Abbreviated Journal CIN
Volume Issue Pages Article ID 8680541
Keywords
Abstract Despite the availability of accurate, commercial gaze tracker devices working with infrared (IR) technology, visible light gaze tracking constitutes an interesting alternative by allowing scalability and removing hardware requirements. Over the last years, this field has seen examples of research showing performance comparable to the IR alternatives. In this work, we survey the previous work on remote, visible light gaze trackers and analyze the explored techniques from various perspectives such as calibration strategies, head pose invariance, and gaze estimation techniques. We also provide information on related aspects of research such as public datasets to test against, open source projects to build upon, and gaze tracking services to directly use in applications. With all this information, we aim to provide the contemporary and future researchers with a map detailing previously explored ideas and the required tools.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MV; 605.103; 600.047; 600.097;SIAI Approved no
Call Number Admin @ si @ FeV2016 Serial 2744
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Author M. Oliver; G. Haro; Mariella Dimiccoli; B. Mazin; C. Ballester
Title A Computational Model for Amodal Completion Type Journal Article
Year 2016 Publication Journal of Mathematical Imaging and Vision Abbreviated Journal JMIV
Volume 56 Issue 3 Pages 511–534
Keywords Perception; visual completion; disocclusion; Bayesian model;relatability; Euler elastica
Abstract This paper presents a computational model to recover the most likely interpretation
of the 3D scene structure from a planar image, where some objects may occlude others. The estimated scene interpretation is obtained by integrating some global and local cues and provides both the complete disoccluded objects that form the scene and their ordering according to depth.
Our method first computes several distal scenes which are compatible with the proximal planar image. To compute these different hypothesized scenes, we propose a perceptually inspired object disocclusion method, which works by minimizing the Euler's elastica as well as by incorporating the relatability of partially occluded contours and the convexity of the disoccluded objects. Then, to estimate the preferred scene we rely on a Bayesian model and define probabilities taking into account the global complexity of the objects in the hypothesized scenes as well as the effort of bringing these objects in their relative position in the planar image, which is also measured by an Euler's elastica-based quantity. The model is illustrated with numerical experiments on, both, synthetic and real images showing the ability of our model to reconstruct the occluded objects and the preferred perceptual order among them. We also present results on images of the Berkeley dataset with provided figure-ground ground-truth labeling.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; 601.235 Approved no
Call Number Admin @ si @ OHD2016b Serial 2745
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Author David Sanchez-Mendoza; David Masip; Agata Lapedriza
Title Emotion recognition from mid-level features Type Journal Article
Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 67 Issue Part 1 Pages 66–74
Keywords Facial expression; Emotion recognition; Action units; Computer vision
Abstract In this paper we present a study on the use of Action Units as mid-level features for automatically recognizing basic and subtle emotions. We propose a representation model based on mid-level facial muscular movement features. We encode these movements dynamically using the Facial Action Coding System, and propose to use these intermediate features based on Action Units (AUs) to classify emotions. AUs activations are detected fusing a set of spatiotemporal geometric and appearance features. The algorithm is validated in two applications: (i) the recognition of 7 basic emotions using the publicly available Cohn-Kanade database, and (ii) the inference of subtle emotional cues in the Newscast database. In this second scenario, we consider emotions that are perceived cumulatively in longer periods of time. In particular, we Automatically classify whether video shoots from public News TV channels refer to Good or Bad news. To deal with the different video lengths we propose a Histogram of Action Units and compute it using a sliding window strategy on the frame sequences. Our approach achieves accuracies close to human perception.
Address
Corporate Author Thesis
Publisher Elsevier B.V. Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes OR;MV Approved no
Call Number Admin @ si @ SML2015 Serial 2746
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Author C. Alejandro Parraga; Arash Akbarinia
Title NICE: A Computational Solution to Close the Gap from Colour Perception to Colour Categorization Type Journal Article
Year 2016 Publication PLoS One Abbreviated Journal Plos
Volume 11 Issue 3 Pages e0149538
Keywords
Abstract The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes NEUROBIT; 600.068 Approved no
Call Number Admin @ si @ PaA2016a Serial 2747
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Author Pedro Martins; Paulo Carvalho; Carlo Gatta
Title On the completeness of feature-driven maximally stable extremal regions Type Journal Article
Year 2016 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 74 Issue Pages 9-16
Keywords Local features; Completeness; Maximally Stable Extremal Regions
Abstract By definition, local image features provide a compact representation of the image in which most of the image information is preserved. This capability offered by local features has been overlooked, despite being relevant in many application scenarios. In this paper, we analyze and discuss the performance of feature-driven Maximally Stable Extremal Regions (MSER) in terms of the coverage of informative image parts (completeness). This type of features results from an MSER extraction on saliency maps in which features related to objects boundaries or even symmetry axes are highlighted. These maps are intended to be suitable domains for MSER detection, allowing this detector to provide a better coverage of informative image parts. Our experimental results, which were based on a large-scale evaluation, show that feature-driven MSER have relatively high completeness values and provide more complete sets than a traditional MSER detection even when sets of similar cardinality are considered.
Address
Corporate Author Thesis
Publisher Elsevier B.V. Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes LAMP;MILAB; Approved no
Call Number Admin @ si @ MCG2016 Serial 2748
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Author Eugenio Alcala; Laura Sellart; Vicenc Puig; Joseba Quevedo; Jordi Saludes; David Vazquez; Antonio Lopez
Title Comparison of two non-linear model-based control strategies for autonomous vehicles Type Conference Article
Year 2016 Publication 24th Mediterranean Conference on Control and Automation Abbreviated Journal
Volume Issue Pages 846-851
Keywords Autonomous Driving; Control
Abstract This paper presents the comparison of two nonlinear model-based control strategies for autonomous cars. A control oriented model of vehicle based on a bicycle model is used. The two control strategies use a model reference approach. Using this approach, the error dynamics model is developed. Both controllers receive as input the longitudinal, lateral and orientation errors generating as control outputs the steering angle and the velocity of the vehicle. The first control approach is based on a non-linear control law that is designed by means of the Lyapunov direct approach. The second approach is based on a sliding mode-control that defines a set of sliding surfaces over which the error trajectories will converge. The main advantage of the sliding-control technique is the robustness against non-linearities and parametric uncertainties in the model. However, the main drawback of first order sliding mode is the chattering, so it has been implemented a high order sliding mode control. To test and compare the proposed control strategies, different path following scenarios are used in simulation.
Address Athens; Greece; June 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference MED
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number ADAS @ adas @ ASP2016 Serial 2750
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Author Joan Mas; Alicia Fornes; Josep Llados
Title An Interactive Transcription System of Census Records using Word-Spotting based Information Transfer Type Conference Article
Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal
Volume Issue Pages 54-59
Keywords
Abstract This paper presents a system to assist in the transcription of historical handwritten census records in a crowdsourcing platform. Census records have a tabular structured layout. They consist in a sequence of rows with information of homes ordered by street address. For each household snippet in the page, the list of family members is reported. The censuses are recorded in intervals of a few years and the information of individuals in each household is quite stable from a point in time to the next one. This redundancy is used to assist the transcriber, so the redundant information is transferred from the census already transcribed to the next one. Household records are aligned from one year to the next one using the knowledge of the ordering by street address. Given an already transcribed census, a query by string word spotting is applied. Thus, names from the census in time t are used as queries in the corresponding home record in time t+1. Since the search is constrained, the obtained precision-recall values are very high, with an important reduction in the transcription time. The proposed system has been tested in a real citizen-science experience where non expert users transcribe the census data of their home town.
Address Santorini; Greece; April 2016
Corporate Author Thesis
Publisher Place of Publication Editor
Language (up) Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference DAS
Notes DAG; 603.053; 602.006; 600.061; 600.077; 600.097 Approved no
Call Number Admin @ si @ MFL2016 Serial 2751
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