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检索条件"任意字段=2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013"

共 4491 条记录，以下是71-80 订阅

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Ground Truth Evaluation for Event-Based Silicon Retina Stereo Data

Ground Truth Evaluation for Event-Based Silicon Retina Stere...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Kogler, Juergen Eibensteiner, Florian Humenberger, Martin Gelautz, Margrit Scharinger, Josef AIT Austrian Inst Technol GmbH Donau City Str 1 A-1220 Vienna Austria Upper Austria Univ Appl Sci A-4232 Hagenberg Austria Vienna Univ Technol A-1040 Vienna Austria Johannes Kepler Univ Linz A-4040 Linz Austria

ISBN: (纸本)9780769549903

In this paper we present a new approach for the evaluation of event-based Silicon Retina stereo matching results. The evaluation of stereo matching algorithm results is a necessary task for the development, comparison, and improvement of depth generating camera systems. In contrast to conventional frame-based cameras, the silicon retina sensors delivers asynchronous events instead of synchronous intensity or color images. The polarity of the events represents either an increase (on-event) or a decrease (off-event) of the brightness of the projected scene point. This is the reason why existing ground truth data and evaluation platforms are not suitable for testing silicon retina stereo camera systems. For the analysis of the introduced novel evaluation method, we use an area-based (sum of absolute difference) algorithm for the event-driven sensor system. A conventional video camera stereo vision system is used to produce reference data. The results show that the presented method offers new opportunities for the evaluation of stereo matching results computed from silicon retina stereo data.

关键词： computer vision image matching stereo image processing video cameras

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Fast Target recognition on Mobile Devices: Revisiting Gaussian Elimination for the the Estimation of Planar Homographies

Fast Target Recognition on Mobile Devices: Revisiting Gaussi...

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27th ieee conference on computer vision and pattern recognition (CVPR)

作者： Bilaniuk, Olexa Bazargani, Hamid Laganiere, Robert Univ Ottawa Sch EECS Ottawa ON K1N 6N5 Canada

ISBN: (纸本)9781479943098

This work analyzes the problem of homography estimation for robust target matching in the context of real-time mobile vision. We present a device-friendly implementation of the Gaussian Elimination algorithm and show that our optimized approach can significantly improve the homography estimation step in a hypothesize-and-verify scheme. Experiments are performed on image sequences in which both speed and accuracy are evaluated and compared with conventional homography estimation schemes.

关键词： feature matching homography estimation mobile vision target recognition

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A multi-sensor traffic scene dataset with omnidirectional video

A multi-sensor traffic scene dataset with omnidirectional vi...

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Koschorrek, Philipp Piccini, Tommaso Oberg, Per Felsberg, Michael Nielsen, Lars Mester, Rudolf Linkoping Univ Dept Elect Engn Comp Vis Lab S-58183 Linkoping Sweden Linkoping Univ Dept Elect Engn Vehicular Syst Linkoping 58183 Sweden

ISBN: (纸本)9780769549903

The development of vehicles that perceive their environment, in particular those using computer vision, indispensably requires large databases of sensor recordings obtained from real cars driven in realistic traffic situations. These datasets should be time shaped for enabling synchronization of sensor data from different sources. Furthermore, full surround environment perception requires high frame rates of synchronized omnidirectional video data to prevent information loss at any speeds. This paper describes an experimental setup and software environment for recording such synchronized multi-sensor data streams and storing them in a new open source format. The dataset consists of sequences recorded in various environments from a car equipped with an omnidirectional multi-camera, height sensors, an IMU, a velocity sensor, and a GPS. The software environment for reading these data sets will be provided to the public, together with a collection of long multi-sensor and multi-camera data streams stored in the developed format.

关键词： computer vision public domain software software engineering traffic information systems very large databases video signal processing

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Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action recognition

Bio-inspired Dynamic 3D Discriminative Skeletal Features for...

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26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Chaudhry, Rizwan Ofli, Ferda Kurillo, Gregorij Bajcsy, Ruzena Vidal, Rene Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Univ Calif Berkeley Tele Immers Lah Berkeley CA 94720 USA

ISBN: (纸本)9780769549903

Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that extract representative features from 3D depth data, a reconstructed 3D surface mesh or more commonly from the recovered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface features. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynamical Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of human activity recognition. Combined with simple classification frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets.

关键词： gesture recognition mesh generation neural nets pattern classification shape recognition solid modelling surface fitting

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Recognising Team Activities from Noisy Data

Recognising Team Activities from Noisy Data

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26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Bialkowski, Alina Lucey, Patrick Carr, Peter Denman, Simon Matthews, Iain Sridharan, Sridha Disney Res Pittburgh PA 94565 USA Queensland Univ Technol Image & Video Lab Brisbane Qld 4072 Australia

ISBN: (纸本)9780769549903

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e. g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e. g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

关键词： computer vision image sensors object recognition object tracking sport

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Local Sparse Discriminant Analysis For Robust Face recognition

Local Sparse Discriminant Analysis For Robust Face Recogniti...

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26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Kang, Cuicui Liao, Shengcai Xiang, Shiming Pan, Chunhong Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9780769549903

The Linear Discriminant Analysis (LDA) algorithm plays an important role in pattern recognition. A common practice is that LDA and many of its variants generally learn dense bases, which are not robust to local image distortions and partial occlusions. Recently, the LASSO penalty has been incorporated into LDA to learn sparse bases. However, since the learned sparse coefficients are globally distributed all over the basis image, the solution is still not robust to partial occlusions. In this paper, we propose a Local Sparse Discriminant Analysis (LoSDA) method, which aims at learning discriminant bases that consist of local object parts. In this way, it is more robust than dense or global basis based LDA algorithms for visual classification. The proposed model is formulated as a constrained least square regression problem with a group sparse regularization. Furthermore, we derive a weighted LoSDA (WLoS-DA) approach to learn localized basis images, which also enables multi subspace learning and fusion. Finally, we develop an algorithm based on the Accelerated Proximal Gradient (APG) technique to solve the resulting weighted group sparse optimization problem. Experimental results on the FRGC v2.0 and the AR face databases show that the proposed LoSDA and WLoSDA algorithms both outperform the other state-of-the-art discriminant subspace learning algorithms under illumination variations and occlusions.

关键词： computer vision face recognition gradient methods image classification image fusion learning (artificial intelligence) least squares approximations optimisation regression analysis

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I Find Your Lack of Uncertainty in computer vision Disturbing

I Find Your Lack of Uncertainty in Computer Vision Disturbin...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Valdenegro-Toro, Matias German Res Ctr Artificial Intelligence Robert Hooke Str 1 D-28359 Bremen Germany

ISBN: (纸本)9781665448994

Neural networks are used for many real world applications, but often they have problems estimating their own confidence. This is particularly problematic for computer vision applications aimed at making high stakes decisions with humans and their lives. In this paper we make a meta-analysis of the literature, showing that most if not all computer vision applications do not use proper epistemic uncertainty quantification, which means that these models ignore their own limitations. We describe the consequences of using models without proper uncertainty quantification, and motivate the community to adopt versions of the models they use that have proper calibrated epistemic uncertainty, in order to enable out of distribution detection. We close the paper with a summary of challenges on estimating uncertainty for computer vision applications and recommendations.

关键词： computer vision Uncertainty Computational modeling conferences Neural networks pattern recognition

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Affective Gaming: A Comprehensive Survey

Affective Gaming: A Comprehensive Survey

引用

26th ieee conference on computer vision and pattern recognition (CVPR)

作者： Kotsia, Irene Zafeiriou, Stefanos Fotopoulos, Spiros Queen Mary Univ London Sch Elect Engn & Comp Sci London England Univ London Imperial Coll Sci Technol & Med Dept Comp London SW7 2AZ England Univ Patras Dept Phys Elect Lab GR-26110 Patras Greece

ISBN: (纸本)9780769549903

A typical gaming scenario involves a player interacting with a game by a specialized input device, such as a joystic, a mouse, a keyboard etc. Recent technological advances have enabled the introduction of more elaborated approaches in which the player is able to interact with the game using his/her body pose, facial expressions, actions, even his physiological signals (heart beat rate, encephalogram, skin conductivity etc). The future lies in 'affective gaming', that is games that will be 'intelligent' enough not only to extract the player's commands by his speech and gestures but also by his behavioral cues, and his/her emotional states and adjust their game plot accordingly, in order to ensure more realistic and satisfactory gameplay experience. In this paper, we review the area of affective gaming by describing existing approaches and discussing recent technological advances. We elaborate on different sources of affect information and summarize the existing commercial affective gaming applications. We proceed with outlining some of the most important problems that have to be tackled in order to create more realistic and efficient interactions between players and games and conclude by high-lighting the challenges such systems must overcome.

关键词： brain-computer interfaces computer games

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Delta Sampling R-BERT for limited data and low-light action recognition

Delta Sampling R-BERT for limited data and low-light action ...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Hira, Sanchit Das, Ritwik Modi, Abhinav Pakhomov, Daniil Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781665448994

We present an approach to perform supervised action recognition in the dark. In this work, we present our results on the ARID dataset[60]. Most previous works only evaluate performance on large, well illuminated datasets like Kinetics and HMDB51. We demonstrate that our work is able to achieve a very low error rate while being trained on a much smaller dataset of dark videos. We also explore a variety of training and inference strategies including domain transfer methodologies and also propose a simple but useful frame selection strategy. Our empirical results demonstrate that we beat previously published baseline models by 11%.

关键词： Training Deep learning Technological innovation Schedules computer vision conferences pattern recognition

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Rotated Rectangles for Symbolized Building Footprint Extraction 31

Rotated Rectangles for Symbolized Building Footprint Extract...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Dickenson, Matt Gueguen, Lionel UBER Technol Inc San Francisco CA 94103 USA

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Building footprints (BFP) provide useful visual context for users of digital maps when navigating in space. This paper proposes a method for extracting and symbolizing building footprints from satellite imagery using a convolutional neural network (CNN). The CNN architecture outputs rotated rectangles, providing a symbolized approximation that works well for small buildings. Experiments are conducted on the four cities in the DeepGlobe Challenge dataset (Las Vegas, Paris, Shanghai, Khartoum). Our method performs best on suburbs consisting of individual houses. These experiments show that either large buildings or buildings without clear delineation produce weaker results in terms of precision and recall.

关键词： Buildings computer architecture Microprocessors Architecture Urban areas computer vision Satellites

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