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Author Sophie Wuerger; Kaida Xiao; Dimitris Mylonas; Q. Huang; Dimosthenis Karatzas; Galina Paramei
Title Blue green color categorization in mandarin english speakers Type Journal Article
Year 2012 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A
Volume 29 Issue 2 Pages A102-A1207
Keywords
Abstract Observers are faster to detect a target among a set of distracters if the targets and distracters come from different color categories. This cross-boundary advantage seems to be limited to the right visual field, which is consistent with the dominance of the left hemisphere for language processing [Gilbert et al., Proc. Natl. Acad. Sci. USA 103, 489 (2006)]. Here we study whether a similar visual field advantage is found in the color identification task in speakers of Mandarin, a language that uses a logographic system. Forty late Mandarin-English bilinguals performed a blue-green color categorization task, in a blocked design, in their first language (L1: Mandarin) or second language (L2: English). Eleven color singletons ranging from blue to green were presented for 160 ms, randomly in the left visual field (LVF) or right visual field (RVF). Color boundary and reaction times (RTs) at the color boundary were estimated in L1 and L2, for both visual fields. We found that the color boundary did not differ between the languages; RTs at the color boundary, however, were on average more than 100 ms shorter in the English compared to the Mandarin sessions, but only when the stimuli were presented in the RVF. The finding may be explained by the script nature of the two languages: Mandarin logographic characters are analyzed visuospatially in the right hemisphere, which conceivably facilitates identification of color presented to the LVF.
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 (down) Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ WXM2012 Serial 2007
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Author Yunchao Gong; Svetlana Lazebnik; Albert Gordo; Florent Perronnin
Title Iterative quantization: A procrustean approach to learning binary codes for Large-Scale Image Retrieval Type Journal Article
Year 2012 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI
Volume 35 Issue 12 Pages 2916-2929
Keywords
Abstract This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multi-class spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or “classemes” on the ImageNet dataset.
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 0162-8828 ISBN 978-1-4577-0394-2 Medium
Area (down) Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ GLG 2012b Serial 2008
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Author Albert Clapes; Miguel Reyes; Sergio Escalera
Title User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis Type Conference Article
Year 2012 Publication 7th Conference on Articulated Motion and Deformable Objects Abbreviated Journal
Volume 7378 Issue Pages 1-11
Keywords
Abstract We propose an automatic system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized online using robust statistical approaches over RGBD descriptions. Finally, the system saves the historic of user-object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
Address Mallorca
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-31566-4 Medium
Area (down) Expedition Conference AMDO
Notes HUPBA;MILAB Approved no
Call Number Admin @ si @ CRE2012 Serial 2010
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Author Diego Cheda; Daniel Ponsa; Antonio Lopez
Title Monocular Egomotion Estimation based on Image Matching Type Conference Article
Year 2012 Publication 1st International Conference on Pattern Recognition Applications and Methods Abbreviated Journal
Volume Issue Pages 425-430
Keywords SLAM
Abstract
Address Portugal
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 (down) Expedition Conference ICPRAM
Notes ADAS Approved no
Call Number Admin @ si @ CPL2012a;; ADAS @ adas @ Serial 2011
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Author Diego Cheda; Daniel Ponsa; Antonio Lopez
Title Monocular Depth-based Background Estimation Type Conference Article
Year 2012 Publication 7th International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 323-328
Keywords
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.
Address Roma
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 (down) Expedition Conference VISAPP
Notes ADAS Approved no
Call Number Admin @ si @ CPL2012b; ADAS @ adas @ cpl2012e Serial 2012
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Author Diego Cheda; Daniel Ponsa; Antonio Lopez
Title Pedestrian Candidates Generation using Monocular Cues Type Conference Article
Year 2012 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal
Volume Issue Pages 7-12
Keywords pedestrian detection
Abstract Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached.
Address
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1931-0587 ISBN 978-1-4673-2119-8 Medium
Area (down) Expedition Conference IV
Notes ADAS Approved no
Call Number Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d Serial 2013
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Author Fernando Barrera; Felipe Lumbreras; Angel Sappa
Title Evaluation of Similarity Functions in Multimodal Stereo Type Conference Article
Year 2012 Publication 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7324 Issue I Pages 320-329
Keywords Aveiro, Portugal
Abstract This paper presents an evaluation framework for multimodal stereo matching, which allows to compare the performance of four similarity functions. Additionally, it presents details of a multimodal stereo head that supply thermal infrared and color images, as well as, aspects of its calibration and rectification. The pipeline includes a novel method for the disparity selection, which is suitable for evaluating the similarity functions. Finally, a benchmark for comparing different initializations of the proposed framework is presented. Similarity functions are based on mutual information, gradient orientation and scale space representations. Their evaluation is performed using two metrics: i) disparity error, and ii) number of correct matches on planar regions. In addition to the proposed evaluation, the current paper also shows that 3D sparse representations can be recovered from such a multimodal stereo head.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-31294-6 Medium
Area (down) Expedition Conference ICIAR
Notes ADAS Approved no
Call Number BLS2012a Serial 2014
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Author Miguel Oliveira; Angel Sappa; V. Santos
Title Color Correction using 3D Gaussian Mixture Models Type Conference Article
Year 2012 Publication 9th International Conference on Image Analysis and Recognition Abbreviated Journal
Volume 7324 Issue I Pages 97-106
Keywords
Abstract The current paper proposes a novel color correction approach based on a probabilistic segmentation framework by using 3D Gaussian Mixture Models. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. The proposed approach is evaluated using both a recently published metric and two large data sets composed of seventy images. The evaluation is performed by comparing our algorithm with eight well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
Address
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 10.1007/978-3-642-31295-3_12 Medium
Area (down) Expedition Conference ICIAR
Notes ADAS Approved no
Call Number Admin @ si @ OSS2012a Serial 2015
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Author Fernando Barrera; Felipe Lumbreras; Cristhian Aguilera; Angel Sappa
Title Planar-Based Multispectral Stereo Type Conference Article
Year 2012 Publication 11th Quantitative InfraRed Thermography Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Naples, Italy
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 (down) Expedition Conference QIRT
Notes ADAS Approved no
Call Number Admin @ si @ BLA2012 Serial 2016
Permanent link to this record
 
 
Author Cristhian Aguilera; Fernando Barrera; Angel Sappa; Ricardo Toledo
Title A Novel SIFT-Like-Based Approach for FIR-VS Images Registration Type Conference Article
Year 2012 Publication 11th Quantitative InfraRed Thermography Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Naples, Italy
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 (down) Expedition Conference QIRT
Notes ADAS; TV Approved no
Call Number Admin @ si @ ABS2012 Serial 2017
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Author Monica Piñol; Angel Sappa; Angeles Lopez; Ricardo Toledo
Title Feature Selection Based on Reinforcement Learning for Object Recognition Type Conference Article
Year 2012 Publication Adaptive Learning Agents Workshop Abbreviated Journal
Volume Issue Pages 33-39
Keywords
Abstract
Address Valencia
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 (down) Expedition Conference ALA
Notes ADAS; RV Approved no
Call Number Admin @ si @ PSL2012 Serial 2018
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Author German Ros; Angel Sappa; Daniel Ponsa; Antonio Lopez
Title Visual SLAM for Driverless Cars: A Brief Survey Type Conference Article
Year 2012 Publication IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles Abbreviated Journal
Volume Issue Pages
Keywords SLAM
Abstract
Address Alcalá de Henares
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 (down) Expedition Conference IVW
Notes ADAS Approved no
Call Number Admin @ si @ RSP2012; ADAS @ adas Serial 2019
Permanent link to this record
 
 
Author Naveen Onkarappa; Angel Sappa
Title An Empirical Study on Optical Flow Accuracy Depending on Vehicle Speed Type Conference Article
Year 2012 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal
Volume Issue Pages 1138-1143
Keywords
Abstract Driver assistance and safety systems are getting attention nowadays towards automatic navigation and safety. Optical flow as a motion estimation technique has got major roll in making these systems a reality. Towards this, in the current paper, the suitability of polar representation for optical flow estimation in such systems is demonstrated. Furthermore, the influence of individual regularization terms on the accuracy of optical flow on image sequences of different speeds is empirically evaluated. Also a new synthetic dataset of image sequences with different speeds is generated along with the ground-truth optical flow.
Address Alcalá de Henares
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1931-0587 ISBN 978-1-4673-2119-8 Medium
Area (down) Expedition Conference IV
Notes ADAS Approved no
Call Number Admin @ si @ NaS2012 Serial 2020
Permanent link to this record
 
 
Author Miguel Oliveira; Angel Sappa; V. Santos
Title Color Correction for Onboard Multi-camera Systems using 3D Gaussian Mixture Models Type Conference Article
Year 2012 Publication IEEE Intelligent Vehicles Symposium Abbreviated Journal
Volume Issue Pages 299-303
Keywords
Abstract The current paper proposes a novel color correction approach for onboard multi-camera systems. It works by segmenting the given images into several regions. A probabilistic segmentation framework, using 3D Gaussian Mixture Models, is proposed. Regions are used to compute local color correction functions, which are then combined to obtain the final corrected image. An image data set of road scenarios is used to establish a performance comparison of the proposed method with other seven well known color correction algorithms. Results show that the proposed approach is the highest scoring color correction method. Also, the proposed single step 3D color space probabilistic segmentation reduces processing time over similar approaches.
Address Alcalá de Henares
Corporate Author Thesis
Publisher IEEE Xplore Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1931-0587 ISBN 978-1-4673-2119-8 Medium
Area (down) Expedition Conference IV
Notes ADAS Approved no
Call Number Admin @ si @ OSS2012b Serial 2021
Permanent link to this record
 
 
Author Jose Manuel Alvarez; Theo Gevers; Y. LeCun; Antonio Lopez
Title Road Scene Segmentation from a Single Image Type Conference Article
Year 2012 Publication 12th European Conference on Computer Vision Abbreviated Journal
Volume 7578 Issue VII Pages 376-389
Keywords road detection
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
Address Florence, Italy
Corporate Author Thesis
Publisher Springer Berlin Heidelberg Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33785-7 Medium
Area (down) Expedition Conference ECCV
Notes ADAS;ISE Approved no
Call Number Admin @ si @ AGL2012; ADAS @ adas @ agl2012a Serial 2022
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