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Author Naveen Onkarappa; Angel Sappa
Title Synthetic sequences and ground-truth flow field generation for algorithm validation Type Journal Article
Year 2015 Publication Multimedia Tools and Applications Abbreviated Journal MTAP
Volume 74 Issue 9 Pages 3121-3135
Keywords Ground-truth optical flow; Synthetic sequence; Algorithm validation
Abstract Research in computer vision is advancing by the availability of good datasets that help to improve algorithms, validate results and obtain comparative analysis. The datasets can be real or synthetic. For some of the computer vision problems such as optical flow it is not possible to obtain ground-truth optical flow with high accuracy in natural outdoor real scenarios directly by any sensor, although it is possible to obtain ground-truth data of real scenarios in a laboratory setup with limited motion. In this difficult situation computer graphics offers a viable option for creating realistic virtual scenarios. In the current work we present a framework to design virtual scenes and generate sequences as well as ground-truth flow fields. Particularly, we generate a dataset containing sequences of driving scenarios. The sequences in the dataset vary in different speeds of the on-board vision system, different road textures, complex motion of vehicle and independent moving vehicles in the scene. This dataset enables analyzing and adaptation of existing optical flow methods, and leads to invention of new approaches particularly for driver assistance systems.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1380-7501 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 601.215; 600.076 Approved no
Call Number (up) Admin @ si @ OnS2014b Serial 2472
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Author Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
Title Brightness and colour induction through contextual influences in V1 Type Conference Article
Year 2015 Publication Scottish Vision Group 2015 SGV2015 Abbreviated Journal
Volume 12 Issue 9 Pages 1208-2012
Keywords
Abstract
Address Carnoustie; Scotland; March 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference SGV
Notes NEUROBIT; Approved no
Call Number (up) Admin @ si @ OPC2015a Serial 2632
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Author Xavier Otazu; Olivier Penacchio; Xim Cerda-Company
Title An excitatory-inhibitory firing rate model accounts for brightness induction, colour induction and visual discomfort Type Conference Article
Year 2015 Publication Barcelona Computational, Cognitive and Systems Neuroscience Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Barcelona; June 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference BARCCSYN
Notes NEUROBIT; Approved no
Call Number (up) Admin @ si @ OPC2015b Serial 2634
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Author Miguel Oliveira; L. Seabra Lopes; G. Hyun Lim; S. Hamidreza Kasaei; Angel Sappa; A. Tom
Title Concurrent Learning of Visual Codebooks and Object Categories in Openended Domains Type Conference Article
Year 2015 Publication International Conference on Intelligent Robots and Systems Abbreviated Journal
Volume Issue Pages 2488 - 2495
Keywords Visual Learning; Computer Vision; Autonomous Agents
Abstract In open-ended domains, robots must continuously learn new object categories. When the training sets are created offline, it is not possible to ensure their representativeness with respect to the object categories and features the system will find when operating online. In the Bag of Words model, visual codebooks are constructed from training sets created offline. This might lead to non-discriminative visual words and, as a consequence, to poor recognition performance. This paper proposes a visual object recognition system which concurrently learns in an incremental and online fashion both the visual object category representations as well as the codebook words used to encode them. The codebook is defined using Gaussian Mixture Models which are updated using new object views. The approach contains similarities with the human visual object recognition system: evidence suggests that the development of recognition capabilities occurs on multiple levels and is sustained over large periods of time. Results show that the proposed system with concurrent learning of object categories and codebooks is capable of learning more categories, requiring less examples, and with similar accuracies, when compared to the classical Bag of Words approach using offline constructed codebooks.
Address Hamburg; Germany; October 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference IROS
Notes ADAS; 600.076 Approved no
Call Number (up) Admin @ si @ OSL2015 Serial 2664
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Author Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias
Title Scene Representations for Autonomous Driving: an approach based on polygonal primitives Type Conference Article
Year 2015 Publication 2nd Iberian Robotics Conference ROBOT2015 Abbreviated Journal
Volume 417 Issue Pages 503-515
Keywords Scene reconstruction; Point cloud; Autonomous vehicles
Abstract In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques.
Address Lisboa; Portugal; November 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ROBOT
Notes ADAS; 600.076; 600.086 Approved no
Call Number (up) Admin @ si @ OSS2015a Serial 2662
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Author Miguel Oliveira; Angel Sappa; Victor Santos
Title A probabilistic approach for color correction in image mosaicking applications Type Journal Article
Year 2015 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 14 Issue 2 Pages 508 - 523
Keywords Color correction; image mosaicking; color transfer; color palette mapping functions
Abstract Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1057-7149 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.076 Approved no
Call Number (up) Admin @ si @ OSS2015b Serial 2554
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Author Miguel Oliveira; Victor Santos; Angel Sappa
Title Multimodal Inverse Perspective Mapping Type Journal Article
Year 2015 Publication Information Fusion Abbreviated Journal IF
Volume 24 Issue Pages 108–121
Keywords Inverse perspective mapping; Multimodal sensor fusion; Intelligent vehicles
Abstract Over the past years, inverse perspective mapping has been successfully applied to several problems in the field of Intelligent Transportation Systems. In brief, the method consists of mapping images to a new coordinate system where perspective effects are removed. The removal of perspective associated effects facilitates road and obstacle detection and also assists in free space estimation. There is, however, a significant limitation in the inverse perspective mapping: the presence of obstacles on the road disrupts the effectiveness of the mapping. The current paper proposes a robust solution based on the use of multimodal sensor fusion. Data from a laser range finder is fused with images from the cameras, so that the mapping is not computed in the regions where obstacles are present. As shown in the results, this considerably improves the effectiveness of the algorithm and reduces computation time when compared with the classical inverse perspective mapping. Furthermore, the proposed approach is also able to cope with several cameras with different lenses or image resolutions, as well as dynamic viewpoints.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 600.076 Approved no
Call Number (up) Admin @ si @ OSS2015c Serial 2532
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Author J.Poujol; Cristhian A. Aguilera-Carrasco; E.Danos; Boris X. Vintimilla; Ricardo Toledo; Angel Sappa
Title Visible-Thermal Fusion based Monocular Visual Odometry Type Conference Article
Year 2015 Publication 2nd Iberian Robotics Conference ROBOT2015 Abbreviated Journal
Volume 417 Issue Pages 517-528
Keywords Monocular Visual Odometry; LWIR-RGB cross-spectral Imaging; Image Fusion.
Abstract The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained
representations are evaluated under a visual odometry framework, highlighting
their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.
Address Lisboa; Portugal; November 2015
Corporate Author Thesis
Publisher Springer International Publishing Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2194-5357 ISBN 978-3-319-27145-3 Medium
Area Expedition Conference ROBOT
Notes ADAS; 600.076; 600.086 Approved no
Call Number (up) Admin @ si @ PAD2015 Serial 2663
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Author C. Alejandro Parraga
Title Perceptual Psychophysics Type Book Chapter
Year 2015 Publication Biologically-Inspired Computer Vision: Fundamentals and Applications Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor G.Cristobal; M.Keil; L.Perrinet
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-3-527-41264-8 Medium
Area Expedition Conference
Notes CIC; 600.074 Approved no
Call Number (up) Admin @ si @ Par2015 Serial 2600
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Author Victor Ponce; Hugo Jair Escalante; Sergio Escalera; Xavier Baro
Title Gesture and Action Recognition by Evolved Dynamic Subgestures Type Conference Article
Year 2015 Publication 26th British Machine Vision Conference Abbreviated Journal
Volume Issue Pages 129.1-129.13
Keywords
Abstract This paper introduces a framework for gesture and action recognition based on the evolution of temporal gesture primitives, or subgestures. Our work is inspired on the principle of producing genetic variations within a population of gesture subsequences, with the goal of obtaining a set of gesture units that enhance the generalization capability of standard gesture recognition approaches. In our context, gesture primitives are evolved over time using dynamic programming and generative models in order to recognize complex actions. In few generations, the proposed subgesture-based representation
of actions and gestures outperforms the state of the art results on the MSRDaily3D and MSRAction3D datasets.
Address Swansea; uk; September 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference BMVC
Notes HuPBA;MV Approved no
Call Number (up) Admin @ si @ PEE2015 Serial 2657
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Author Eloi Puertas; Sergio Escalera; Oriol Pujol
Title Generalized Multi-scale Stacked Sequential Learning for Multi-class Classification Type Journal Article
Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume 18 Issue 2 Pages 247-261
Keywords Stacked sequential learning; Multi-scale; Error-correct output codes (ECOC); Contextual classification
Abstract In many classification problems, neighbor data labels have inherent sequential relationships. Sequential learning algorithms take benefit of these relationships in order to improve generalization. In this paper, we revise the multi-scale sequential learning approach (MSSL) for applying it in the multi-class case (MMSSL). We introduce the error-correcting output codesframework in the MSSL classifiers and propose a formulation for calculating confidence maps from the margins of the base classifiers. In addition, we propose a MMSSL compression approach which reduces the number of features in the extended data set without a loss in performance. The proposed methods are tested on several databases, showing significant performance improvement compared to classical approaches.
Address
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-7541 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number (up) Admin @ si @ PEP2013 Serial 2251
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Author Victor Ponce; Sergio Escalera; Marc Perez; Oriol Janes; Xavier Baro
Title Non-Verbal Communication Analysis in Victim-Offender Mediations Type Journal Article
Year 2015 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 67 Issue 1 Pages 19-27
Keywords Victim–Offender Mediation; Multi-modal human behavior analysis; Face and gesture recognition; Social signal processing; Computer vision; Machine learning
Abstract We present a non-invasive ambient intelligence framework for the semi-automatic analysis of non-verbal communication applied to the restorative justice field. We propose the use of computer vision and social signal processing technologies in real scenarios of Victim–Offender Mediations, applying feature extraction techniques to multi-modal audio-RGB-depth data. We compute a set of behavioral indicators that define communicative cues from the fields of psychology and observational methodology. We test our methodology on data captured in real Victim–Offender Mediation sessions in Catalonia. We define the ground truth based on expert opinions when annotating the observed social responses. Using different state of the art binary classification approaches, our system achieves recognition accuracies of 86% when predicting satisfaction, and 79% when predicting both agreement and receptivity. Applying a regression strategy, we obtain a mean deviation for the predictions between 0.5 and 0.7 in the range [1–5] for the computed social signals.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language 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 Approved no
Call Number (up) Admin @ si @ PEP2015 Serial 2583
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Author Olivier Penacchio; Xavier Otazu; A. wilkins; J. Harris
Title Uncomfortable images prevent lateral interactions in the cortex from providing a sparse code Type Conference Article
Year 2015 Publication European Conference on Visual Perception ECVP2015 Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Liverpool; uk; August 2015
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ECVP
Notes NEUROBIT; Approved no
Call Number (up) Admin @ si @ POW2015 Serial 2633
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Author Monica Piñol; Angel Sappa; Ricardo Toledo
Title Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy Type Journal Article
Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 150 Issue A Pages 106–115
Keywords Reinforcement learning; Q-learning; Bag of features; Descriptors
Abstract This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed approach.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 600.076 Approved no
Call Number (up) Admin @ si @ PST2015 Serial 2473
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Author Marco Pedersoli; Andrea Vedaldi; Jordi Gonzalez; Xavier Roca
Title A coarse-to-fine approach for fast deformable object detection Type Journal Article
Year 2015 Publication Pattern Recognition Abbreviated Journal PR
Volume 48 Issue 5 Pages 1844-1853
Keywords
Abstract We present a method that can dramatically accelerate object detection with part based models. The method is based on the observation that the cost of detection is likely to be dominated by the cost of matching each part to the image, and not by the cost of computing the optimal configuration of the parts as commonly assumed. Therefore accelerating detection requires minimizing the number of
part-to-image comparisons. To this end we propose a multiple-resolutions hierarchical part based model and a corresponding coarse-to-fine inference procedure that recursively eliminates from the search space unpromising part
placements. The method yields a ten-fold speedup over the standard dynamic programming approach and is complementary to the cascade-of-parts approach of [9]. Compared to the latter, our method does not have parameters to be determined empirically, which simplifies its use during the training of the model. Most importantly, the two techniques can be combined to obtain a very significant speedup, of two orders of magnitude in some cases. We evaluate our method extensively on the PASCAL VOC and INRIA datasets, demonstrating a very high increase in the detection speed with little degradation of the accuracy.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE; 600.078; 602.005; 605.001; 302.012 Approved no
Call Number (up) Admin @ si @ PVG2015 Serial 2628
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