| Records |
| Author |
Yifan Wang; Luka Murn; Luis Herranz; Fei Yang; Marta Mrak; Wei Zhang; Shuai Wan; Marc Gorriz Blanch |
| Title |
Efficient Super-Resolution for Compression Of Gaming Videos |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
Due to the increasing demand for game-streaming services, efficient compression of computer-generated video is more critical than ever, especially when the available bandwidth is low. This paper proposes a super-resolution framework that improves the coding efficiency of computer-generated gaming videos at low bitrates. Most state-of-the-art super-resolution networks generalize over a variety of RGB inputs and use a unified network architecture for frames of different levels of degradation, leading to high complexity and redundancy. Since games usually consist of a limited number of fixed scenarios, we specialize one model for each scenario and assign appropriate network capacities for different QPs to perform super-resolution under the guidance of reconstructed high-quality luma components. Experimental results show that our framework achieves a superior quality-complexity trade-off compared to the ESRnet baseline, saving at most 93.59% parameters while maintaining comparable performance. The compression efficiency compared to HEVC is also improved by more than 17% BD-rate gain. |
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Conference  |
ICASSP |
| Notes |
LAMP; MACO |
Approved |
no |
| Call Number |
Admin @ si @ WMH2023 |
Serial |
3911 |
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| Author |
Mingyi Yang; Luis Herranz; Fei Yang; Luka Murn; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang; Marta Mrak |
| Title |
Semantic Preprocessor for Image Compression for Machines |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
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| Abstract |
Visual content is being increasingly transmitted and consumed by machines rather than humans to perform automated content analysis tasks. In this paper, we propose an image preprocessor that optimizes the input image for machine consumption prior to encoding by an off-the-shelf codec designed for human consumption. To achieve a better trade-off between the accuracy of the machine analysis task and bitrate, we propose leveraging pre-extracted semantic information to improve the preprocessor’s ability to accurately identify and filter out task-irrelevant information. Furthermore, we propose a two-part loss function to optimize the preprocessor, consisted of a rate-task performance loss and a semantic distillation loss, which helps the reconstructed image obtain more information that contributes to the accuracy of the task. Experiments show that the proposed preprocessor can save up to 48.83% bitrate compared with the method without the preprocessor, and save up to 36.24% bitrate compared to existing preprocessors for machine vision. |
| Address |
Rodhes Islands; Greece; June 2023 |
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Expedition |
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Conference |
ICASSP |
| Notes |
MACO; LAMP |
Approved |
no |
| Call Number |
Admin @ si @ YHY2023 |
Serial |
3912 |
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| Author |
Lei Kang; Lichao Zhang; Dazhi Jiang |
| Title |
Learning Robust Self-Attention Features for Speech Emotion Recognition with Label-Adaptive Mixup |
Type |
Conference Article |
| Year |
2023 |
Publication |
IEEE International Conference on Acoustics, Speech and Signal Processing |
Abbreviated Journal |
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Issue |
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Pages |
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| Abstract |
Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods. |
| Address |
Rodhes Islands; Greece; June 2023 |
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Place of Publication |
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Conference |
ICASSP |
| Notes |
LAMP |
Approved |
no |
| Call Number |
Admin @ si @ KZJ2023 |
Serial |
3984 |
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| Author |
Maria Salamo; Sergio Escalera; Petia Radeva |
| Title |
Quality Enhancement based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender |
Type |
Conference Article |
| Year |
2009 |
Publication |
8th International Conference on Case-Based Reasoning |
Abbreviated Journal |
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| Volume |
5650 |
Issue |
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Pages |
298–312 |
| Keywords |
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| Abstract |
Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help users to navigate through product spaces, alternatively making product suggestions and eliciting users feedback. Critiquing is a common form of feedback and incremental critiquing-based recommender system has shown its efficiency to personalize products based primarily on a quality measure. This quality measure influences the recommendation process and it is obtained by the combination of compatibility and similarity scores. In this paper, we describe new compatibility strategies whose basis is on reinforcement learning and a new feature weighting technique which is based on the user’s history of critiques. Moreover, we show that our methodology can significantly improve recommendation efficiency in comparison with the state-of-the-art approaches. |
| Address |
Seattle, USA |
| Corporate Author |
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Thesis |
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| Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Original Title |
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Abbreviated Series Title |
LNCS |
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Series Issue |
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Edition |
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| ISSN |
0302-9743 |
ISBN |
978-3-642-02998-1 |
Medium |
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Expedition |
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Conference |
ICCBR |
| Notes |
HuPBA; MILAB |
Approved |
no |
| Call Number |
BCNPCL @ bcnpcl @ SER2009 |
Serial |
1187 |
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| Author |
Partha Pratim Roy; Josep Llados |
| Title |
Multi-Oriented Character Recognition from Graphical Documents |
Type |
Conference Article |
| Year |
2008 |
Publication |
2nd International Conference on Cognition and Recognition |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
30–35 |
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| Abstract |
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| Address |
Mandya (India) |
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Series Issue |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICCR |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RLP2008 |
Serial |
965 |
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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 |
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| Volume |
80 |
Issue |
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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 |
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Edition |
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Expedition |
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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 |
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| Volume |
80 |
Issue |
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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 |
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Original Title |
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Abbreviated Series Title |
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Edition |
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ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
ICCS |
| Notes |
ADAS; 600.085; 600.082; 600.076 |
Approved |
no |
| Call Number |
ADAS @ adas @ CSE2016 |
Serial |
2741 |
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| Author |
Partha Pratim Roy; Josep Llados; Umapada Pal |
| Title |
Text/Graphics Separation in Color Maps |
Type |
Conference Article |
| Year |
2007 |
Publication |
International Conference on Computing: Theory and Applications |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
545–551 |
| Keywords |
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| Abstract |
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| Address |
Kolkata (India) |
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Place of Publication |
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Conference |
ICCTA |
| Notes |
DAG |
Approved |
no |
| Call Number |
DAG @ dag @ RLP2007a |
Serial |
806 |
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| Author |
Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell |
| Title |
Top-Down Color Attention for Object Recognition |
Type |
Conference Article |
| Year |
2009 |
Publication |
12th International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
979 - 986 |
| Keywords |
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| Abstract |
Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information. |
| Address |
Kyoto, Japan |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1550-5499 |
ISBN |
978-1-4244-4420-5 |
Medium |
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Expedition |
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Conference |
ICCV |
| Notes |
CIC |
Approved |
no |
| Call Number |
CAT @ cat @ SWV2009 |
Serial |
1196 |
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| Author |
Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez |
| Title |
Detection and Removal of Chromatic Moving Shadows in Surveillance Scenarios |
Type |
Conference Article |
| Year |
2009 |
Publication |
12th International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1499 - 1506 |
| Keywords |
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| Abstract |
Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering “a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions. |
| Address |
Kyoto, Japan |
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Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Abbreviated Series Title |
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Edition |
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| ISSN |
1550-5499 |
ISBN |
978-1-4244-4420-5 |
Medium |
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Expedition |
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Conference |
ICCV |
| Notes |
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Approved |
no |
| Call Number |
ISE @ ise @ HHM2009 |
Serial |
1213 |
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| Author |
Gemma Roig; Xavier Boix; Fernando De la Torre |
| Title |
Optimal Feature Selection for Subspace Image Matching |
Type |
Conference Article |
| Year |
2009 |
Publication |
2nd IEEE International Workshop on Subspace Methods in conjunction |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Abstract |
Image matching has been a central research topic in computer vision over the last decades. Typical approaches to correspondence involve matching feature points between images. In this paper, we present a novel problem for establishing correspondences between a sparse set of image features and a previously learned subspace model. We formulate the matching task as an energy minimization, and jointly optimize over all possible feature assignments and parameters of the subspace model. This problem is in general NP-hard. We propose a convex relaxation approximation, and develop two optimization strategies: naïve gradient-descent and quadratic programming. Alternatively, we reformulate the optimization criterion as a sparse eigenvalue problem, and solve it using a recently proposed backward greedy algorithm. Experimental results on facial feature detection show that the quadratic programming solution provides better selection mechanism for relevant features. |
| Address |
Kyoto, Japan |
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Conference |
ICCV |
| Notes |
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Approved |
no |
| Call Number |
Admin @ si @ RBT2009 |
Serial |
1233 |
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| Author |
Xavier Boix; Josep M. Gonfaus; Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Marco Pedersoli; Jordi Gonzalez; Joan Serrat |
| Title |
Combining local and global bag-of-word representations for semantic segmentation |
Type |
Conference Article |
| Year |
2009 |
Publication |
Workshop on The PASCAL Visual Object Classes Challenge |
Abbreviated Journal |
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Issue |
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Pages |
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| Address |
Kyoto (Japan) |
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Editor |
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Edition |
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Expedition |
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Conference |
ICCV |
| Notes |
ADAS;ISE |
Approved |
no |
| Call Number |
ADAS @ adas @ BGS2009 |
Serial |
1273 |
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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 |
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| Volume |
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Issue |
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Pages |
137-144 |
| Keywords |
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| 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 |
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Conference |
ICCV |
| Notes |
ALTRES;ISE |
Approved |
no |
| Call Number |
Admin @ si @ AGV2013 |
Serial |
2365 |
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| Author |
Hamdi Dibeklioglu; Albert Ali Salah; Theo Gevers |
| Title |
Like Father, Like Son: Facial Expression Dynamics for Kinship Verification |
Type |
Conference Article |
| Year |
2013 |
Publication |
15th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1497-1504 |
| Keywords |
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| Abstract |
Kinship verification from facial appearance is a difficult problem. This paper explores the possibility of employing facial expression dynamics in this problem. By using features that describe facial dynamics and spatio-temporal appearance over smile expressions, we show that it is possible to improve the state of the art in this problem, and verify that it is indeed possible to recognize kinship by resemblance of facial expressions. The proposed method is tested on different kin relationships. On the average, 72.89% verification accuracy is achieved on spontaneous smiles. |
| Address |
Sydney |
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Place of Publication |
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ISBN |
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Medium |
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Expedition |
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Conference |
ICCV |
| Notes |
ALTRES;ISE |
Approved |
no |
| Call Number |
Admin @ si @ DSG2013 |
Serial |
2366 |
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| Author |
Koen E.A. van de Sande; Jasper Uilings; Theo Gevers; Arnold Smeulders |
| Title |
Segmentation as Selective Search for Object Recognition |
Type |
Conference Article |
| Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
1879-1886 |
| Keywords |
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| Abstract |
For object recognition, the current state-of-the-art is based on exhaustive search. However, to enable the use of more expensive features and classifiers and thereby progress beyond the state-of-the-art, a selective search strategy is needed. Therefore, we adapt segmentation as a selective search by reconsidering segmentation: We propose to generate many approximate locations over few and precise object delineations because (1) an object whose location is never generated can not be recognised and (2) appearance and immediate nearby context are most effective for object recognition. Our method is class-independent and is shown to cover 96.7% of all objects in the Pascal VOC 2007 test set using only 1,536 locations per image. Our selective search enables the use of the more expensive bag-of-words method which we use to substantially improve the state-of-the-art by up to 8.5% for 8 out of 20 classes on the Pascal VOC 2010 detection challenge. |
| Address |
Barcelona |
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Thesis |
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Place of Publication |
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| Language |
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Summary Language |
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Original Title |
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Series Title |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
1550-5499 |
ISBN |
978-1-4577-1101-5 |
Medium |
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Expedition |
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Conference |
ICCV |
| Notes |
ISE |
Approved |
no |
| Call Number |
Admin @ si @ SUG2011 |
Serial |
1780 |
| Permanent link to this record |
