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Author | Jorge Bernal; David Vazquez (eds) | ||||
Title | Computer vision Trends and Challenges | Type | Book Whole | ||
Year | 2013 | Publication | Computer vision Trends and Challenges | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | CVCRD; Computer Vision | ||||
Abstract | This book contains the papers presented at the Eighth CVC Workshop on Computer Vision Trends and Challenges (CVCR&D'2013). The workshop was held at the Computer Vision Center (Universitat Autònoma de Barcelona), the October 25th, 2013. The CVC workshops provide an excellent opportunity for young researchers and project engineers to share new ideas and knowledge about the progress of their work, and also, to discuss about challenges and future perspectives. In addition, the workshop is the welcome event for new people that recently have joined the institute.
The program of CVCR&D is organized in a single-track single-day workshop. It comprises several sessions dedicated to specific topics. For each session, a doctor working on the topic introduces the general research lines. The PhD students expose their specific research. A poster session will be held for open questions. Session topics cover the current research lines and development projects of the CVC: Medical Imaging, Medical Imaging, Color & Texture Analysis, Object Recognition, Image Sequence Evaluation, Advanced Driver Assistance Systems, Machine Vision, Document Analysis, Pattern Recognition and Applications. We want to thank all paper authors and Program Committee members. Their contribution shows that the CVC has a dynamic, active, and promising scientific community. We hope you all enjoy this Eighth workshop and we are looking forward to meeting you and new people next year in the Ninth CVCR&D. |
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Corporate Author | Thesis | ||||
Publisher | Place of Publication | Editor | Jorge Bernal; David Vazquez | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-940902-2-6 | Medium | ||
Area | Expedition | Conference | |||
Notes | Approved | no | |||
Call Number | ADAS @ adas @ BeV2013 | Serial | 2339 | ||
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Author | Javier Marin; David Vazquez; Antonio Lopez; Jaume Amores; Bastian Leibe | ||||
Title | Random Forests of Local Experts for Pedestrian Detection | Type | Conference Article | ||
Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 2592 - 2599 | ||
Keywords | ADAS; Random Forest; Pedestrian Detection | ||||
Abstract | Pedestrian detection is one of the most challenging tasks in computer vision, and has received a lot of attention in the last years. Recently, some authors have shown the advantages of using combinations of part/patch-based detectors in order to cope with the large variability of poses and the existence of partial occlusions. In this paper, we propose a pedestrian detection method that efficiently combines multiple local experts by means of a Random Forest ensemble. The proposed method works with rich block-based representations such as HOG and LBP, in such a way that the same features are reused by the multiple local experts, so that no extra computational cost is needed with respect to a holistic method. Furthermore, we demonstrate how to integrate the proposed approach with a cascaded architecture in order to achieve not only high accuracy but also an acceptable efficiency. In particular, the resulting detector operates at five frames per second using a laptop machine. We tested the proposed method with well-known challenging datasets such as Caltech, ETH, Daimler, and INRIA. The method proposed in this work consistently ranks among the top performers in all the datasets, being either the best method or having a small difference with the best one. | ||||
Address | Sydney; Australia; December 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE | Place of Publication | Editor | ||
Language | Summary Language | Original Title | |||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | 1550-5499 | ISBN | Medium | ||
Area | Expedition | Conference | ICCV | ||
Notes | ADAS; 600.057; 600.054 | Approved | no | ||
Call Number | ADAS @ adas @ MVL2013 | Serial | 2333 | ||
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Author | Gemma Roig; Xavier Boix; R. de Nijs; Sebastian Ramos; K. Kühnlenz; Luc Van Gool | ||||
Title | Active MAP Inference in CRFs for Efficient Semantic Segmentation | Type | Conference Article | ||
Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 2312 - 2319 | ||
Keywords | Semantic Segmentation | ||||
Abstract | Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are instantiated by slow classifiers fed with costly features. We introduce Active MAP inference 1) to on-the-fly select a subset of potentials to be instantiated in the energy function, leaving the rest of the parameters of the potentials unknown, and 2) to estimate the MAP labeling from such incomplete energy function. Results for semantic segmentation benchmarks, namely PASCAL VOC 2010 [5] and MSRC-21 [19], show that Active MAP inference achieves similar levels of accuracy but with major efficiency gains. | ||||
Address | Sydney; Australia; December 2013 | ||||
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 | 1550-5499 | ISBN | Medium | ||
Area | Expedition | Conference | ICCV | ||
Notes | ADAS; 600.057 | Approved | no | ||
Call Number | ADAS @ adas @ RBN2013 | Serial | 2377 | ||
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Author | Yainuvis Socarras; Sebastian Ramos; David Vazquez; Antonio Lopez; Theo Gevers | ||||
Title | Adapting Pedestrian Detection from Synthetic to Far Infrared Images | Type | Conference Article | ||
Year | 2013 | Publication | ICCV Workshop on Visual Domain Adaptation and Dataset Bias | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Domain Adaptation; Far Infrared; Pedestrian Detection | ||||
Abstract | We present different techniques to adapt a pedestrian classifier trained with synthetic images and the corresponding automatically generated annotations to operate with far infrared (FIR) images. The information contained in this kind of images allow us to develop a robust pedestrian detector invariant to extreme illumination changes. | ||||
Address | Sydney; Australia; December 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | Place of Publication | Sydney, Australy | Editor | ||
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | ICCVW-VisDA | ||
Notes | ADAS; 600.054; 600.055; 600.057; 601.217;ISE | Approved | no | ||
Call Number | ADAS @ adas @ SRV2013 | Serial | 2334 | ||
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Author | David Vazquez | ||||
Title | Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection | Type | Book Whole | ||
Year | 2013 | Publication | PhD Thesis, Universitat de Barcelona-CVC | Abbreviated Journal | |
Volume | 1 | Issue | 1 | Pages | 1-105 |
Keywords | Pedestrian Detection; Domain Adaptation | ||||
Abstract | Pedestrian detection is of paramount interest for many applications, e.g. Advanced Driver Assistance Systems, Intelligent Video Surveillance and Multimedia systems. Most promising pedestrian detectors rely on appearance-based classifiers trained with annotated data. However, the required annotation step represents an intensive and subjective task for humans, what makes worth to minimize their intervention in this process by using computational tools like realistic virtual worlds. The reason to use these kind of tools relies in the fact that they allow the automatic generation of precise and rich annotations of visual information. Nevertheless, the use of this kind of data comes with the following question: can a pedestrian appearance model learnt with virtual-world data work successfully for pedestrian detection in real-world scenarios?. To answer this question, we conduct different experiments that suggest a positive answer. However, the pedestrian classifiers trained with virtual-world data can suffer the so called dataset shift problem as real-world based classifiers does. Accordingly, we have designed different domain adaptation techniques to face this problem, all of them integrated in a same framework (V-AYLA). We have explored different methods to train a domain adapted pedestrian classifiers by collecting a few pedestrian samples from the target domain (real world) and combining them with many samples of the source domain (virtual world). The extensive experiments we present show that pedestrian detectors developed within the V-AYLA framework do achieve domain adaptation. Ideally, we would like to adapt our system without any human intervention. Therefore, as a first proof of concept we also propose an unsupervised domain adaptation technique that avoids human intervention during the adaptation process. To the best of our knowledge, this Thesis work is the first demonstrating adaptation of virtual and real worlds for developing an object detector. Last but not least, we also assessed a different strategy to avoid the dataset shift that consists in collecting real-world samples and retrain with them in such a way that no bounding boxes of real-world pedestrians have to be provided. We show that the generated classifier is competitive with respect to the counterpart trained with samples collected by manually annotating pedestrian bounding boxes. The results presented on this Thesis not only end with a proposal for adapting a virtual-world pedestrian detector to the real world, but also it goes further by pointing out a new methodology that would allow the system to adapt to different situations, which we hope will provide the foundations for future research in this unexplored area. | ||||
Address | Barcelona | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Barcelona | Editor | Antonio Lopez;Daniel Ponsa |
Language | English | Summary Language | Original Title | ||
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | 978-84-940530-1-6 | Medium | ||
Area | Expedition | Conference | |||
Notes | adas | Approved | yes | ||
Call Number | ADAS @ adas @ Vaz2013 | Serial | 2276 | ||
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Author | David Vazquez; Jiaolong Xu; Sebastian Ramos; Antonio Lopez; Daniel Ponsa | ||||
Title | Weakly Supervised Automatic Annotation of Pedestrian Bounding Boxes | Type | Conference Article | ||
Year | 2013 | Publication | CVPR Workshop on Ground Truth – What is a good dataset? | Abbreviated Journal | |
Volume | Issue | Pages | 706 - 711 | ||
Keywords | Pedestrian Detection; Domain Adaptation | ||||
Abstract | Among the components of a pedestrian detector, its trained pedestrian classifier is crucial for achieving the desired performance. The initial task of the training process consists in collecting samples of pedestrians and background, which involves tiresome manual annotation of pedestrian bounding boxes (BBs). Thus, recent works have assessed the use of automatically collected samples from photo-realistic virtual worlds. However, learning from virtual-world samples and testing in real-world images may suffer the dataset shift problem. Accordingly, in this paper we assess an strategy to collect samples from the real world and retrain with them, thus avoiding the dataset shift, but in such a way that no BBs of real-world pedestrians have to be provided. In particular, we train a pedestrian classifier based on virtual-world samples (no human annotation required). Then, using such a classifier we collect pedestrian samples from real-world images by detection. After, a human oracle rejects the false detections efficiently (weak annotation). Finally, a new classifier is trained with the accepted detections. We show that this classifier is competitive with respect to the counterpart trained with samples collected by manually annotating hundreds of pedestrian BBs. | ||||
Address | Portland; Oregon; June 2013 | ||||
Corporate Author | Thesis | ||||
Publisher | IEEE | Place of Publication | Editor | ||
Language | English | Summary Language | English | Original Title | |
Series Editor | Series Title | Abbreviated Series Title | |||
Series Volume | Series Issue | Edition | |||
ISSN | ISBN | Medium | |||
Area | Expedition | Conference | CVPRW | ||
Notes | ADAS; 600.054; 600.057; 601.217 | Approved | no | ||
Call Number | ADAS @ adas @ VXR2013a | Serial | 2219 | ||
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Author | Jiaolong Xu; Sebastian Ramos; Xu Hu; David Vazquez; Antonio Lopez | ||||
Title | Multi-task Bilinear Classifiers for Visual Domain Adaptation | Type | Conference Article | ||
Year | 2013 | Publication | Advances in Neural Information Processing Systems Workshop | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Domain Adaptation; Pedestrian Detection; ADAS | ||||
Abstract | We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation. The bilinear classifier encodes the feature transformation and classification parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines. |
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Address | Lake Tahoe; Nevada; USA; December 2013 | ||||
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 | NIPSW | ||
Notes | ADAS; 600.054; 600.057; 601.217;ISE | Approved | no | ||
Call Number | ADAS @ adas @ XRH2013 | Serial | 2340 | ||
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Author | Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados | ||||
Title | Classification of Administrative Document Images by Logo Identification | Type | Conference Article | ||
Year | 2013 | Publication | 10th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | |||||
Abstract | This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier's graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. | ||||
Address | Bethlehem; PA; USA; August 2013 | ||||
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 | GREC | ||
Notes | DAG; 600.056; 600.045; 605.203 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 2348 | ||
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Author | Marina Alberti; Simone Balocco; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
Title | Automatic non-rigid temporal alignment of IVUS sequences: method and quantitative validation | Type | Journal Article | ||
Year | 2013 | Publication | Ultrasound in Medicine and Biology | Abbreviated Journal | UMB |
Volume | 39 | Issue | 9 | Pages | 1698-712 |
Keywords | Intravascular ultrasound; Dynamic time warping; Non-rigid alignment; Sequence matching; Partial overlapping strategy | ||||
Abstract | Clinical studies on atherosclerosis regression/progression performed by intravascular ultrasound analysis would benefit from accurate alignment of sequences of the same patient before and after clinical interventions and at follow-up. In this article, a methodology for automatic alignment of intravascular ultrasound sequences based on the dynamic time warping technique is proposed. The non-rigid alignment is adapted to the specific task by applying it to multidimensional signals describing the morphologic content of the vessel. Moreover, dynamic time warping is embedded into a framework comprising a strategy to address partial overlapping between acquisitions and a term that regularizes non-physiologic temporal compression/expansion of the sequences. Extensive validation is performed on both synthetic and in vivo data. The proposed method reaches alignment errors of approximately 0.43 mm for pairs of sequences acquired during the same intervention phase and 0.77 mm for pairs of sequences acquired at successive intervention stages. | ||||
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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 | MILAB | Approved | no | ||
Call Number | Admin @ si @ ABC2013 | Serial | 2313 | ||
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Author | Francisco Alvaro; Francisco Cruz; Joan Andreu Sanchez; Oriol Ramos Terrades; Jose Miguel Bemedi | ||||
Title | Page Segmentation of Structured Documents Using 2D Stochastic Context-Free Grammars | Type | Conference Article | ||
Year | 2013 | Publication | 6th Iberian Conference on Pattern Recognition and Image Analysis | Abbreviated Journal | |
Volume | 7887 | Issue | Pages | 133-140 | |
Keywords | |||||
Abstract | In this paper we define a bidimensional extension of Stochastic Context-Free Grammars for page segmentation of structured documents. Two sets of text classification features are used to perform an initial classification of each zone of the page. Then, the page segmentation is obtained as the most likely hypothesis according to a grammar. This approach is compared to Conditional Random Fields and results show significant improvements in several cases. Furthermore, grammars provide a detailed segmentation that allowed a semantic evaluation which also validates this model. | ||||
Address | Madeira; Portugal; June 2013 | ||||
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 | 978-3-642-38627-5 | Medium | |
Area | Expedition | Conference | IbPRIA | ||
Notes | DAG; 605.203 | Approved | no | ||
Call Number | Admin @ si @ ACS2013 | Serial | 2328 | ||
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Author | Jon Almazan; Alicia Fornes; Ernest Valveny | ||||
Title | A Deformable HOG-based Shape Descriptor | Type | Conference Article | ||
Year | 2013 | Publication | 12th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
Volume | Issue | Pages | 1022-1026 | ||
Keywords | |||||
Abstract | In this paper we deal with the problem of recognizing handwritten shapes. We present a new deformable feature extraction method that adapts to the shape to be described, dealing in this way with the variability introduced in the handwriting domain. It consists in a selection of the regions that best define the shape to be described, followed by the computation of histograms of oriented gradients-based features over these points. Our results significantly outperform other descriptors in the literature for the task of hand-drawn shape recognition and handwritten word retrieval | ||||
Address | Washington; USA; August 2013 | ||||
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 | 1520-5363 | ISBN | Medium | ||
Area | Expedition | Conference | ICDAR | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ AFV2013 | Serial | 2326 | ||
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Author | Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez | ||||
Title | Road Geometry Classification by Adaptative Shape Models | Type | Journal Article | ||
Year | 2013 | Publication | IEEE Transactions on Intelligent Transportation Systems | Abbreviated Journal | TITS |
Volume | 14 | Issue | 1 | Pages | 459-468 |
Keywords | road detection | ||||
Abstract | Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions. | ||||
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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 | 1524-9050 | ISBN | Medium | ||
Area | Expedition | Conference | |||
Notes | ADAS;ISE | Approved | no | ||
Call Number | Admin @ si @ AGD2013;; ADAS @ adas @ | Serial | 2269 | ||
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Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
Title | Handwritten Word Spotting with Corrected Attributes | Type | Conference Article | ||
Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 1017-1024 | ||
Keywords | |||||
Abstract | We propose an approach to multi-writer word spotting, where the goal is to find a query word in a dataset comprised of document images. We propose an attributes-based approach that leads to a low-dimensional, fixed-length representation of the word images that is fast to compute and, especially, fast to compare. This approach naturally leads to an unified representation of word images and strings, which seamlessly allows one to indistinctly perform query-by-example, where the query is an image, and query-by-string, where the query is a string. We also propose a calibration scheme to correct the attributes scores based on Canonical Correlation Analysis that greatly improves the results on a challenging dataset. We test our approach on two public datasets showing state-of-the-art results. | ||||
Address | Sydney; Australia; December 2013 | ||||
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 | 1550-5499 | ISBN | Medium | ||
Area | Expedition | Conference | ICCV | ||
Notes | DAG | Approved | no | ||
Call Number | Admin @ si @ AGF2013 | Serial | 2327 | ||
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Author | Jose Manuel Alvarez; Theo Gevers; Antonio Lopez | ||||
Title | Evaluating Color Representation for Online Road Detection | Type | Conference Article | ||
Year | 2013 | Publication | ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars | Abbreviated Journal | |
Volume | Issue | Pages | 594-595 | ||
Keywords | |||||
Abstract | Detecting traversable road areas ahead a moving vehicle is a key process for modern autonomous driving systems. Most existing algorithms use color to classify pixels as road or background. These algorithms reduce the effect of lighting variations and weather conditions by exploiting the discriminant/invariant properties of different color representations. However, up to date, no comparison between these representations have been conducted. Therefore, in this paper, we perform an evaluation of existing color representations for road detection. More specifically, we focus on color planes derived from RGB data and their most com-
mon combinations. The evaluation is done on a set of 7000 road images acquired using an on-board camera in different real-driving situations. |
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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 | CVVT:E2M | ||
Notes | ADAS;ISE | Approved | no | ||
Call Number | Admin @ si @ AGL2013 | Serial | 2794 | ||
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Author | Fares Alnajar; Theo Gevers; Roberto Valenti; Sennay Ghebreab | ||||
Title | Calibration-free Gaze Estimation using Human Gaze Patterns | Type | Conference Article | ||
Year | 2013 | Publication | 15th IEEE International Conference on Computer Vision | Abbreviated Journal | |
Volume | Issue | Pages | 137-144 | ||
Keywords | |||||
Abstract | We present a novel method to auto-calibrate gaze estimators based on gaze patterns obtained from other viewers. Our method is based on the observation that the gaze patterns of humans are indicative of where a new viewer will look at [12]. When a new viewer is looking at a stimulus, we first estimate a topology of gaze points (initial gaze points). Next, these points are transformed so that they match the gaze patterns of other humans to find the correct gaze points. In a flexible uncalibrated setup with a web camera and no chin rest, the proposed method was tested on ten subjects and ten images. The method estimates the gaze points after looking at a stimulus for a few seconds with an average accuracy of 4.3 im. Although the reported performance is lower than what could be achieved with dedicated hardware or calibrated setup, the proposed method still provides a sufficient accuracy to trace the viewer attention. This is promising considering the fact that auto-calibration is done in a flexible setup , without the use of a chin rest, and based only on a few seconds of gaze initialization data. To the best of our knowledge, this is the first work to use human gaze patterns in order to auto-calibrate gaze estimators. | ||||
Address | Sydney | ||||
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 | ICCV | ||
Notes | ALTRES;ISE | Approved | no | ||
Call Number | Admin @ si @ AGV2013 | Serial | 2365 | ||
Permanent link to this record |