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共 4491 条记录，以下是61-70 订阅

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AFRIFASHION1600: A Contemporary African Fashion Dataset for computer vision

AFRIFASHION1600: A Contemporary African Fashion Dataset for ...

引用

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

作者： Oyewusi, Wuraola Fisayo Adekanmbi, Olubayo Ibejih, Sharon Osakuade, Opeyemi Okoh, Ifeoma Salami, Mary Data Sci Nigeria Lagos Nigeria

ISBN: (纸本)9781665448994

This work presents AFRIFASHION1600, an openly accessible contemporary African fashion image dataset containing 1600 samples labelled into 8 classes representing some African fashion styles. Each sample is coloured and has an image size of 128 x 128. This is a niche dataset that aims to improve visibility, inclusion, and familiarity of African fashion in computer vision ***1600 dataset is available here.

关键词： computer vision conferences Computational modeling Machine learning pattern recognition Task analysis

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X-ray Testing by computer vision

X-ray Testing by Computer Vision

引用

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

作者： Mery, Domingo Pontificia Univ Catolica Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9780769549903

X-ray imaging has been developed not only for its use in medical imaging for human beings, but also for materials or objects, where the aim is to analyze -nondestructively-those inner parts that are undetectable to the naked eye. Thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are analysis of food products, screening of baggage, inspection of automotive parts, and quality control of welds. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems are being developed to execute this task. In this paper, we present a general overview of computer vision methodologies that have been used in X-ray testing. In addition, we review some techniques that have been applied in certain relevant applications;and we introduce a public database of X-ray images that can be used for testing and evaluation of image analysis and computer vision algorithms. Finally, we conclude that the following: that there are some areas -like casting inspection-where automated systems are very effective, and other application areas -such as baggage screening-where human inspection is still used;there are certain application areas -like weld and cargo inspections-where the process is semi-automatic;and there is some research in areas including food analysis-where processes are beginning to be characterized by the use of X-ray imaging.

关键词： X-ray imaging casting computer vision computerised instrumentation inspection

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Using 3D Models to Recognize 2D Faces in the Wild

Using 3D Models to Recognize 2D Faces in the Wild

引用

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

作者： Masi, Iacopo Lisanti, Giuseppe Bagdanov, Andrew D. Pala, Pietro Del Bimbo, Alberto Univ Florence MICC I-50134 Florence Italy

ISBN: (纸本)9780769549903

In this paper we consider the problem of face recognition in imagery captured in uncooperative environments using PTZ cameras. For each subject enrolled in the gallery, we acquire a high-resolution 3D model from which we generate a series of rendered face images of varying viewpoint. The result of regularly sampling face pose for all subjects is a redundant basis that over represents each target. To recognize an unknown probe image, we perform a sparse reconstruction of SIFT features extracted from the probe using a basis of SIFT features from the gallery. While directly collecting images over varying pose for all enrolled subjects is prohibitive at enrollment, the use of high speed, 3D acquisition systems allows our face recognition system to quickly acquire a single model, and generate synthetic views offline. Finally we show, using two publicly available datasets, how our approach performs when using rendered gallery images to recognize 2D rendered probe images and 2D probe images acquired using PTZ cameras.

关键词： data acquisition face recognition feature extraction image reconstruction image resolution image sampling pose estimation rendering (computer graphics) solid modelling

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Efficient GPU-based Graph Cuts for Stereo Matching

Efficient GPU-based Graph Cuts for Stereo Matching

引用

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

作者： Choi, Young-kyu Park, In Kyu Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Inha Univ Sch Informat & Commun Engn Incheon South Korea

ISBN: (纸本)9780769549903

Although graph cuts (GC) is popularly used in many computer vision problems, slow execution time due to its high complexity hinders wide usage. Manycore solution using Graphics Processing Unit (GPU) may solve this problem. However, conventional GC implementation does not fully exploit GPU's computing power. To address this issue, a new GC algorithm which is suitable for GPU environment is presented in this paper. First, we present a novel graph construction method that accelerates the convergence speed of GC. Next, a repetitive block-based push and relabel method is used to increase the data transfer efficiency. Finally, we propose a low-overhead global relabeling algorithm to increase the GPU occupancy ratio. The experiments on Middlebury stereo dataset shows that 5.2X speedup can be achieved over the baseline implementation, with identical GPU platform and parameters.

关键词： computer vision graph theory graphics processing units image matching stereo image processing

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Mobile Exergames - Burn Calories While Playing Games on a Smartphone

Mobile Exergames - Burn Calories While Playing Games on a Sm...

引用

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

作者： Buddharaju, Pradeep Pamidi, Naga Siva Chandra Prasad Univ Houston Clear Lake Dept Comp Sci Houston TX 77058 USA

ISBN: (纸本)9780769549903

Exergames combine exercising with game play by requiring the users to perform some kind of physical activity (and exercise) in order to score points in the game. In this paper, we present a novel mobile exergaming framework, which requires the users to physically move and jump in order to score points in a game that is played on a smartphone. Our system uses a custom designed Exercising Pad (called ExerPad) in order to track the user's physical movement, and then automatically updates the corresponding game character's position on the screen. The ExerPad contains different shaped images, which are captured from the smartphone's inbuilt camera, and are automatically detected by our shape detection algorithm. We also use the smartphone's inbuilt accelerometer and gyroscope to detect other physical movements from the user such as jumping, turning etc. The experimental results show that the proposed mobile exergames helps its users to burn calories and have fun at the same time.

关键词： accelerometers cameras computer games gyroscopes health care mobile computing object detection object tracking smart phones

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Video Demo: An Egocentric vision based Assistive Co-robot

Video Demo: An Egocentric Vision based Assistive Co-robot

引用

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

作者： Zhang, Jingzhe Zhuang, Lishuo Wang, Yang Zhou, Yameng Meng, Yan Hua, Gang Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Stevens Inst Technol Dept Comp Sci Hoboken NJ 07030 USA

ISBN: (纸本)9780769549903

We present the video demo of the prototype of an egocentric vision based assistive co-robot system. In this co-robot system, the user is wearing a pair of glasses with a forward looking camera, and is actively engaged in the control loop of the robot in navigational tasks. The egocentric vision glasses serve for two purposes. First, it serves as a source of visual input to request the robot to find a certain object in the environment. Second, the motion patterns computed from the egocentric video associated with a specific set of head movements are exploited to guide the robot to find the object. These are especially helpful for quadriplegic individuals who do not have the needed hand functionality for control with other modalities (e.g., joystick). In our co-robot system, when the robot does not fulfill the object finding task in a pre-specified time window, it would actively solicit user controls for guidance. Then the users can use the egocentric vision based gesture interface to orient the robot towards the direction of the object. After that the robot will automatically navigate towards the object until it finds it. Our experiments validated the efficacy of the closed-loop design to engage the human in the loop.

关键词： closed loop systems gesture recognition handicapped aids human-robot interaction image motion analysis medical robotics mobile robots path planning robot vision video signal processing

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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...

引用

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

引用

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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On Visible Adversarial Perturbations & Digital Watermarking 31

On Visible Adversarial Perturbations & Digital Watermarking

引用

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

作者： Hayes, Jamie UCL London England

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

Given a machine learning model, adversarial perturbations transform images such that the model's output is classified as an attacker chosen class. Most research in this area has focused on adversarial perturbations that are imperceptible to the human eye. However, recent work has considered attacks that are perceptible but localized to a small region of the image. Under this threat model, we discuss both defenses that remove such adversarial perturbations, and attacks that can bypass these defenses.

关键词： Perturbation methods Watermarking Computational modeling Visualization Task analysis Image restoration computer vision

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