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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是231-240 订阅

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EPOS: Estimating 6D Pose of Objects With Symmetries

EPOS: Estimating 6D Pose of Objects With Symmetries

引用

conference on computer vision and pattern recognition (cvpr)

作者： Tomáš Hodaň Dániel Baráth Jiří Matas Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

We present a new method for estimating the 6D pose of rigid objects with available 3D models from a single RGB input image. the method is applicable to a broad range of objects, including challenging ones with global or partial symmetries. An object is represented by compact surface fragments which allow handling symmetries in a systematic manner. Correspondences between densely sampled pixels and the fragments are predicted using an encoder-decoder network. At each pixel, the network predicts: (i) the probability of each object's presence, (ii) the probability of the fragments given the object's presence, and (iii) the precise 3D location on each fragment. A data-dependent number of corresponding 3D locations is selected per pixel, and poses of possibly multiple object instances are estimated using a robust and efficient variant of the PnP-RANSAC algorithm. In the BOP Challenge 2019, the method outperforms all RGB and most RGB-D and D methods on the T-LESS and LM-O datasets. On the YCB-V dataset, it is superior to all competitors, with a large margin over the second-best RGB method. Source code is at: ***/epos.

关键词： three-dimensional displays Solid modeling Two dimensional displays Robustness Pose estimation Systematics Shape

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ESTIMATING PHYSICAL ACTIVITY INTENSITY AND ENERGY EXPENDITURE USING computer vision ON VIDEOS 26

ESTIMATING PHYSICAL ACTIVITY INTENSITY AND ENERGY EXPENDITUR...

引用

26th ieee International conference on Image Processing (ICIP)

作者： Saponaro, Philip Wei, Haoran Dominick, Gregory Kambhamettu, Chandra Univ Delaware Newark DE 19716 USA

ISBN: (纸本)9781538662496

Estimating physical activity (PA) intensity and energy expenditure (EE) is a problem that typically requires the use of wearable sensors such as a heart rate monitor, or accelerometer. We investigate the accuracy of a computer vision system using videos recorded from a pair of wearable video glasses to estimate PA strength and EE automatically using age, gender, speed, and activity cues. Age and gender are obtained using the Deep EXpectation network, while activity is estimated from joint angles and movement speed. We also present results on a study of 50 participants performing four different activities while measuring corresponding features of interest such as height, weight, age, sex, and ground truth EE and PA strength data collected via accelerometer. We present both the results of each computer vision subsystem and overall accuracy of the PA strength estimation (89.5%) and the average EE difference (1.96 kCal/min).

关键词： Convolutional neural networks energy expenditure physical activity intensity action recognition

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TEMPORAL INTERFRAME pattern ANALYSIS FOR STATIC AND DYNAMIC HAND GESTURE recognition 26

TEMPORAL INTERFRAME PATTERN ANALYSIS FOR STATIC AND DYNAMIC ...

引用

26th ieee International conference on Image Processing (ICIP)

作者： Hu, Kaoning Yin, Lijun Wang, Tianyang Texas A&M Univ Commerce TX 75428 USA SUNY Binghamton Binghamton NY USA Austin Peay State Univ Clarksville TN 37044 USA

ISBN: (纸本)9781538662496

Hand gesture, a common non-verbal language, is being studied for Human computer Interaction. Hand gestures can be categorized as static hand gestures and dynamic hand gestures. In recent years, effective approaches have been applied to hand gesture recognition. However, almost all of the previous works only focus on either of the two categories instead of both, and none of them has used the temporal information on the recognition of static hand gestures. In this paper, we propose a three-level scheme to utilize the temporal interframe pattern on the recognition of both static and dynamic hand gestures. the first classifier assigns a class label to each frame of the video sequence that contains both static and dynamic hand gestures. the second classifier uses the temporal pattern of the class labels to correct the errors of the first classifier and to distinguish between static and dynamic hand gestures. the third classifier is then used to recognize dynamic hand gestures. We believe that we are the first to propose such a strategy. the extensive experiments showed promising performance and demonstrated the feasibility of using temporal interframe pattern to recognize dynamic hand gestures and to correct the errors in static hand gesture recognition.

关键词： Hand gesture static dynamic interframe temporal pattern

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Enchanting Your Noodles: A Gustatory Manipulation Interface by Using GAN-based Real-time Food-to-Food Translation 26

Enchanting Your Noodles: A Gustatory Manipulation Interface ...

引用

26th ieee conference on Virtual Reality and 3D User Interfaces (VR)

作者： Nakano, Kizashi Horita, Daichi Sakata, Nobuchika Kiyokawa, Kiyoshi Yanai, Keiji Narumi, Takuji Nara Inst Sci & Technol Nara Japan Univ Electrocommun Chofu Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781728113777

In this demonstration, we present a novel gustatory manipulation interface which utilizes the cross-modal effect of vision on taste elicited with real-time food appearance modulation using a generative adversarial network (GAN). Unlike existing systems which only change color or texture pattern of a particular type of food in an inflexible manner, our system changes the appearance of food into multiple types of food in real-time flexibly, dynamically and interactively in accordance with the deformation of the food that the user is actually eating by using GAN-based image-to-image translation. Our system can turn somen noodles into ramen noodles or fried noodles, or steamed rice into curry and rice or fried rice. Users of our demonstration system will taste what is visually presented to some extent rather than what they are actually eating.

关键词： Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality Computing methodologies-computer graphics-Graphics systems and interfaces-Perception Computing methodologies-Machine learning Machine learning approaches-Neural networks

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FACIAL EXPRESSION recognition WIth SKIP-CONNECTION TO LEVERAGE LOW-LEVEL FEATURES 26

FACIAL EXPRESSION RECOGNITION WITH SKIP-CONNECTION TO LEVERA...

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26th ieee International conference on Image Processing (ICIP)

作者： Verma, Manisha Kobori, Hirokazu Nakashima, Yuta Takemura, Noriko Nagahara, Hajime Osaka Univ Inst Databil Sci Osaka Japan Daikin Ind Ltd Osaka Japan

ISBN: (纸本)9781538662496

Deep convolutional neural networks (CNNs) have established their feet in the ground of computer vision and machine learning, used in various applications. In this work, an attempt is made to learn a CNN for a task of facial expression recognition (FER). Our network has convolutional layers linked with an FC layer with a skip-connection to the classification layer. Motivation behind this design is that lower layers of a CNN are responsible for lower level features, and facial expressions can be mainly encoded in low-to-mid level features. Hence, in order to leverage the responses from lower layers, all convolutional layers are integrated via FC layers. Moreover, a network with shared parameters is used to extract landmark motion trajectory features. these visual and landmark features are fused to improve the performance. Our method is evaluated on the CK+ and Oulu-CASIA facial expression datasets.

关键词： Facial expression recognition facial landmarks convolutional neural network low level features

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TEXT recognition IN IMAGES BASED ON TRANSFORMER WIth HIERARCHICAL ATTENTION 26

TEXT RECOGNITION IN IMAGES BASED ON TRANSFORMER WITH HIERARC...

引用

26th ieee International conference on Image Processing (ICIP)

作者： Zhu, Yiwei Wang, Shilin Huang, Zheng Chen, Kai Shanghai Jiao Tong Univ Sch EIEE Shanghai Peoples R China

ISBN: (纸本)9781538662496

Recognizing text in images has been a hot research topic in computer vision for decades due to its various application. However, the variations in text appearance in term of perspective distortion, text line curvature, text styles, etc., cause great trouble in text recognition. Inspired by the Transformer structure [1] that achieved outstanding performance in many natural language processing related applications, we propose a new Transformer-like structure for text recognition in images, which is referred to as the Hierarchical Attention Transformer Network (HATN). the entire network can be trained end-to-end by using only images and sentence-level annotations. A new hierarchical attention mechanism is proposed to lean the character-level, word-level and sentence-level contexts more efficiently and sufficiently. Extensive experiments on seven public datasets with regular and irregular text arrangements have demonstrated that the proposed HATN can achieve accurate recognition results with high efficiency.

关键词： text recognition in images transformer hierarchical attention structure

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Shadow Inducers: Inconspicuous Highlights for Casting Virtual Shadows on OST-HMDs 26

Shadow Inducers: Inconspicuous Highlights for Casting Virtua...

引用

26th ieee conference on Virtual Reality and 3D User Interfaces (VR)

作者： Manabe, Shinnosuke Ikeda, Sei Kimura, Asako Shibata, Fumihisa Ritsumeikan Univ Kyoto Japan

ISBN: (纸本)9781728113777

Virtual shadows provide important cues to determine the positional relationship between virtual objects and a real scene. However, it' s difficult to render shadows in the real scene on optical seethrough head -mounted displays without occlusion -capability. In the previous work, we cast virtual shadows not with physical light attenuation but with brightness induction caused by virtual objects, referred to as shadow inducers, which surround the shadow area to gradually amplify the intensity of the real scene pattern [4]. However, because the shadow inducer was prepared beforehand, the shape of the shadow is constant, the real scene shadowing is limited to a flat surface, and a large of viewpoint change is impossible. In this demonstration, we propose a method to generate shadow inducers in real time that can change the shape of virtual objects and the viewpoint of users. In this method, depending on the appearance of shadows, the surrounding luminance is gradually amplified with the difference of gaussian (DOG) representing characteristics of human vision. Users can observe shadows of moving and deforming virtual objects on areal tabletop and other non-planar objects.

关键词： Human-centered computing-Human computer interaction (HCI)-Interaction paradigms-Mixed / augmented reality Human-centered computing-Human computer interaction (HCI)-Interaction devices-Displays and imagers

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ADVERSARIAL FACE DE-IDENTIFICATION 26

ADVERSARIAL FACE DE-IDENTIFICATION

引用

26th ieee International conference on Image Processing (ICIP)

作者： Chatzikyriakidis, Efstathios Papaioannidis, Christos Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki Greece

ISBN: (纸本)9781538662496

Recently, much research has been done on how to secure personal data, notably facial images. Face de-identification is one example of privacy protection that protects person identity by fooling intelligent face recognition systems, while typically allowing face recognition by human observers. While many face de-identification methods exist, the generated de-identified facial images do not resemble the original ones. this paper proposes the usage of adversarial examples for face de-identification that introduces minimal facial image distortion, while fooling automatic face recognition systems. Specifically, it introduces P-FGVM, a novel adversarial attack method, which operates on the image spatial domain and generates adversarial de-identified facial images that resemble the original ones. A comparison between P-FGVM and other adversarial attack methods shows that P-FGVM both protects privacy and preserves visual facial image quality more efficiently.

关键词： Privacy Protection Face De-identification Adversarial Examples Deep Learning computer vision

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PEDESTRIAN ATTRIBUTE recognition BASED ON MTCNN WIth ONLINE BATCH WEIGHTED LOSS 26

PEDESTRIAN ATTRIBUTE RECOGNITION BASED ON MTCNN WITH ONLINE ...

引用

26th ieee International conference on Image Processing (ICIP)

作者： He, Xingting Shi, Qiuyue Su, Fei Zhao, Zhicheng Zhuang, Bojin Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China Ping An Technol Shenzhen Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781538662496

Due to the large number and huge diversity of attributes, pedestrian attribute recognition in video surveillance scenarios is a challenging task in the field of computer vision. Different from most previous works which only focus on extremely imbalanced attribute distribution problem, a new grouping way of attributes based multi-task convolutional neural network (MTCNN) is put forward, which exploits the spatial correlations among attributes and guarantees some independence of each attribute as well. Meanwhile, we propose a novel online batch weighted loss to narrow the performance differences among attributes and boost the model to gain a higher average recognition accuracy. the whole network can be trained end to end, and experimental results on PETA and RAP datasets show that our method achieves significant performance, comparing with those state-of-the-art methods.

关键词： Pedestrian Attribute recognition Grouping CNN Multi-task Learning Online Batch Weighted Loss

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AUTOMATIC NEURAL NETWORK SEARCH MEthOD FOR OPEN SET recognition 26

AUTOMATIC NEURAL NETWORK SEARCH METHOD FOR OPEN SET RECOGNIT...

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26th ieee International conference on Image Processing (ICIP)

作者： Sun, Li Yu, Xiaoyi Wang, Liuan Sun, Jun Inakoshi, Hiroya Kobayashi, Ken Kobashi, Hiromichi Fujitsu R&D Ctr Beijing Peoples R China Fujitsu Labs Ltd Kawasaki Kanagawa Japan

ISBN: (纸本)9781538662496

Real-world recognition or classification tasks in computer vision are not apparent in controlled environments and often get involved in open set. Previous research work on real-world recognition problem is knowledge- and labor-intensive to pursue good performance for there are numbers of task domains. Auto Machine Learning (AutoML) approaches supply an easier way to apply advanced machine learning technologies, reduce the demand for experienced human experts and improve classification performance on close set. this paper proposes an automatic neural network search method for designing effective convolution neural network (CNN) models for open set recognition (OSR). Feature distribution information is explicitly incorporated into the main objective. So during the search process, the sampled models will enlarge interclass differences and reduce intra-class variations. We design a flexible search space based on classic CNN models to diversify neural architectures and also add some search principles to limit the size of the search space. Experimental results on CIFAR-10 and Dunhuang historical Chinese datasets show that our approach improves performances on both close and open set. Comparing with the other two OSR algorithms, our method also achieves the best performance.

关键词： Neural network search open set search space feature distribution center loss

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