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检索条件"任意字段=Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing."

共 8406 条记录，以下是301-310 订阅

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Gait Analysis with Trinocular computer Vision Using Deep Learning

Gait Analysis with Trinocular Computer Vision Using Deep Lea...

引用

2023 ieee International conference on image processing.Challenges and Workshops, ICIPCW 2023

作者： Stavrakakis, Odysseas Mastrogeorgiou, Athanasios Smyrli, Aikaterini Papadopoulos, Evangelos School of Mechanical Engineering National Technical University of Athens Control Systems Lab Greece

ISBN: (纸本)9798350302585

The recent years' progress in deep learning (DL) technology has resulted in convolutional neural networks (CNNs) capable of producing fast and accurate results, with minimal data preprocessing. Currently, gait analysis (GA) is attracting the attention of the field of deep learning due to its seamless integration applicability. Our approach focuses on CNN-based GA application on healthcare and orthopaedics. Using CNNs and visual fiducial systems for recognition and 3D mesh re-construction of the human form and the floor respectively, we can virtually recreate the human-floor interaction, which can be particularly useful in the study of gait dynamics, through the per-frame gait phase classification. However, the current state-of-the-art (SOTA) is that most CNN mesh reconstruction software produces a mesh from a monocular input. The use of photogrammetry could alternatively be implemented, but would require multiple cameras and expensive equipment. Our approach aims to create a refined mesh obtained from trinocular footage, along with an interactive and easy-to-use interface. © 2023 ieee.

关键词： Floors

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Importance resampling based on surfels with dynamic lighting

Importance resampling based on surfels with dynamic lighting

引用

2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Wang, Tingyu Wu, Zhihong College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9781510666351

DDGI, GIBS, and other similar dynamic global illumination algorithms solve the irradiance integral by shortening and reusing the light path, obtaining the multi-bounce diffuse reflection effect without recursion. However, these algorithms may perform poorly in scenes with complex lighting. GIBS introduced a simplified path guiding algorithm, which uses lighting information from history, and is limited by the resolution of radiance maps, causing inaccurate sample pdf under dynamic lighting. We propose an importance resampling method based on surfels that is more adaptable to scenes with dynamic lighting and provides more converged diffuse integral results. First, we derive the radiance composition under next event estimation(NEE) framework, then gather visibility maps and calculate the direct irradiance additionally to obtain the target pdf. Finally, we build reservoirs for each ray used for diffuse integral, and generate ray samples by reservoir resampling. We also provide a feasible temporal reuse strategy for single and persistent reservoirs based on surfels. Experimental results demonstrate that our method can generate higher quality samples, improving the accuracy and stability of diffuse global illumination under dynamic lighting. © 2023 SPIE.

关键词： Importance sampling

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Dual Collaborative Visual-Semantic Mapping for Multi-Label Zero-Shot image recognition 48

Dual Collaborative Visual-Semantic Mapping for Multi-Label Z...

引用

48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Hu, Yunqing Jin, Xuan Chen, Xi Zhang, Yin Zhejiang University College of Computer Science and Technology Hangzhou China Alibaba Group Hangzhou China

ISBN: (纸本)9781728163277

Multi-label zero-shot learning (ML-ZSL), with the difficulty of both multi-label learning and zero-shot learning, aims to recognize various unseen objects that are not observed during training. Previous methods mainly use a single directional visual-semantic mapping to associate the visual and semantic embedding space, which is not sufficient to adequately realize knowledge transfer from seen to unseen classes. In this paper, we propose a novel dual collaborative visual-semantic mapping framework, constructing abundant connection relationships by exploring two aspects of mapping streams, i.e., the visual-to-semantic (V2S) mapping and the semantic-to-visual (S2V) mapping. Through the collaborative learning of these two effective mappings, our method achieves state-of-the-art performance on the MS-COCO and PASCAL-VOC, two benchmarks for ML-ZSL. © 2023 ieee.

关键词： multi-label image recognition visual-semantic zero-shot learning

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A Stealthy Backdoor Attack Method Based on Generative Adversarial Networks 5

A Stealthy Backdoor Attack Method Based on Generative Advers...

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5th Asia-Pacific conference on image processing. Electronics and computers, IPEC 2024

作者： Tan, Yayue Yu, Gao Zhang, Yaqi Tang, Yongqing Southwest University College of Computer and Information Science School of Software Chongqing China Payment core platform Ant Group Co. Ltd Department of Payment Platform Technology Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350374407

Deep neural networks have been widely and successfully applied to many critical tasks, such as image pattern recognition, natural language processing.and autonomous driving, and large amounts of training data and increasing computational power are key factors in their success. Many studies in recent years have shown that deep neural network models face many practical security threats, such as adversarial attacks and backdoor attacks, which can have very serious consequences in the real world. Aiming at the problems of stealth, backdoor trigger design and universality of current backdoor attack methods, a novel method of invisible backdoor attack based on generative adversarial network is proposed. As a backdoor attack method based on data poisoning, inspired by image steganography, this method trains an encoder to generate poisoned samples through generative adversarial network to implement backdoor attack. The secret image used to guide the generation of backdoor triggers does not need to be carefully designed, and the encoder has good generalization ability across datasets. These features not only make its attack effective and stealthy, but also greatly reduce the cost of backdoor. Extensive experimental and analytical results show that the proposed backdoor attack method is effective, stealthy, sustainable and universal. © 2024 ieee.

关键词： Generative adversarial networks

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One-Shot Medical Action recognition With A Cross-Attention Mechanism And Dynamic Time Warping 48

One-Shot Medical Action Recognition With A Cross-Attention M...

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48th ieee International conference on Acoustics, Speech and Signal processing. ICASSP 2023

作者： Xie, Leiyu Yang, Yuxing Fu, Zeyu Naqvi, Syed Mohsen Newcastle University Intelligent Sensing and Communications Research Group United Kingdom University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9781728163277

In this paper, we address the classification of medical actions with only one single sample by developing a novel one-shot learning framework which contains both cross-attention and dynamic time warping (DTW) modules. To be concrete, we firstly transform the raw skeleton sequence into the signal-level image representation. We exploit a metric learning approach, which is the prototypical network for the proposed one-shot learning framework and choose the residual network (ResNet18) as the backbone which is widely used in recent years. Cross-attention is applied for guiding the network to focus on the more important joints from each specific action. The cross-attention mechanism that applies between the support and query set will be adapted for mining and matching the relationships with the human body. Furthermore, a DTW module is introduced to mitigate the temporal information mismatching issue between the actions from the support and query sets. The experimental results on the NTU RGB+D 120 dataset demonstrate the effectiveness of our proposed approach and the improved performance compared to the baseline approach. The code of this work is available at 1 © 2023 ieee.

关键词： image representation

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A Method for Soybean Germination Rate Detection Based on image processing 49

A Method for Soybean Germination Rate Detection Based on Ima...

引用

49th Annual conference of the ieee Industrial Electronics society, IECON 2023

作者： Han, Ru Shu, Lei Yin, Menghan Deng, Der-Jiunn Li, Kailiang Zhang, Jin Yi, Xuefang School of Artificial Intelligence Nanjing Agricultural University China Nanjing Agricultural University NAU-Lincoln Joint Research Center of Intelligent Engineering China School of Engineering University of Lincoln United Kingdom National Changhua University of Education Department of Computer Science and Information Engineering Taiwan China Telecom Hongxin Infomation Technology Company Nanjing China

ISBN: (纸本)9798350331820

As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns in *** solve this problem, this paper aims to study a soybean germination rate detection method based on image processing. By replacing manual detection of soybean germination rate and measuring soybean root length with machine-based approaches, basic image processing.techniques including grayscale conversion, denoising, binarization, dilation, and erosion are applied to preprocess the images of soybean germination. An improved object detection algorithm is used to extract phenotypic features during soybean germination. The main focus of the study is twofold: (1) Measuring the seed area and the minimum bounding rectangle area of the image regions to determine the germination of seeds by comparing their ratio, thus obtaining the germination rate;(2) Using skeleton extraction and embryo-root separation methods to measure the changes in seed root length during germination, thereby selecting seeds with high vitality and improving the quality of soybean seeds. © 2023 ieee.

关键词： Germination

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Human Motion pattern Perception Algorithm Based on Heterogeneous Multiprocessor and Deep Learning

Human Motion Pattern Perception Algorithm Based on Heterogen...

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2023 Asia-Europe conference on Electronics, Data processing.and Informatics, ACEDPI 2023

作者： Qiu, Sanjun Police Physical Education Guangxi Police College Guangxi Nanning530029 China

ISBN: (纸本)9798350301151

With the continuous progress of mankind, the level of sports is particularly important in today's society. Mobile communication system is one of the key parts. This paper proposes a method of human biological signal processing.based on heterogeneous multiprocessor and deep learning. The algorithm utilizes the characteristics of real-time, scalability and high reliability, and can adapt to the needs of users with good performance, easy to use, and can realize human-computer interactive perception. After that, this paper describes the concept of heterogeneous multiprocessor and studies the application of deep learning algorithm. In this paper, the MATLAB programming language is used to verify its effectiveness, and the simulation results prove the accuracy of this scheme. The experimental results show that the correct rate of recognition features is basically above 90%. © 2023 ieee.

关键词： Mobile telecommunication systems

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Contour and key point based eye feature extraction

Contour and key point based eye feature extraction

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2023 International conference on image, Signal processing. and pattern recognition, ISPP 2023

作者： Wang, Yue Wan, Yaping Min, Xiaoshan Fan, Zhiqing He, Yasha Zou, Gang School of Computer Science University of South China Hengyang421001 China Hunan Medical Big Data International Science and Technology Cooperation Base Hengyang421001 China Xiangya School of Medicine Central South University Changsha410205 China School of Computer Science and Technology North University of China Taiyuan030000 China Hunan ZhongkeZhuying Intelligent Technology Research Institute Changsha410205 China

ISBN: (纸本)9781510666351

The use of eye movements for identifying intrinsic human psychological changes is a hot topic, and the identification of eye states in specific scenarios is the basis for analyzing the test taker's instantaneous psychological response state and thus for testing implicit driving quality deficits. Infrared eye images are difficult to visualize eye movements, and traditional eye image processing.results are not suitable for eye state analysis. In order to make the eye images show the eye movements, a feature extraction method based on contours and key point is proposed for infrared eye images. To extract the eye contour features, the iris and eyelid contours are extracted based on the variation of gray values between the points located on the boundary and their surroundings;for the extraction of eye key point features, a network design with a combination of dlib and residual structure is used to extract the pupil and eyelid key points, respectively. The experimental results show that the method can represent the relative positions of the pupil, iris and eyelid separately with little computational effort, which can intuitively show the eye movement and thus reduce the stress of subsequent eye state recognition. © 2023 SPIE.

关键词： Eye movements

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No-reference image quality assessment based on deep learning 3

No-reference image quality assessment based on deep learning

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3rd International conference on Advanced Algorithms and Signal image processing. AASIP 2023

作者： Wang, Yifan Zhang, Fan Chang, Sheng Zhang, Xinhong School of Computer and Information Engineering Henan University Kaifeng475004 China School of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510668522

images form the basis of human vision and are an important source of information for both human perception and machine pattern recognition. Since the development of the image quality evaluation field, a large number of image quality evaluation algorithms have emerged. In recent years, deep learning has become a hot area and has also been applied to the image domain. This paper focuses on reference-free image quality evaluation and reviews the no-reference image quality assessment based on deep learning. Firstly, the classification of IQA, technical indexes for IQA algorithm evaluation, and several public IQA databases available online are introduced. Then, several deep learning models applied to NR-IQA are discussed, compared, and evaluated in detail. Finally, an outlook on future research is provided. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Classification (of information)

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Compact and Effective Representations for Sketch-based image Retrieval

Compact and Effective Representations for Sketch-based Image...

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

作者： Torres, Pablo Saavedra, Jose M. Univ Chile DCC Av Beauchef 851 Santiago Chile Impresee Inc 600 Calif St San Francisco CA USA

ISBN: (纸本)9781665448994

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.

关键词： Dimensionality reduction computer vision conferences image retrieval Memory management Search engines pattern recognition

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