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

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EEG signals of Chinese character cognition under simulated prosthetic vision 3

EEG signals of Chinese character cognition under simulated p...

引用

3rd ieee International conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Jiaqi, Jing Ying, Zhao Yi, Zhang Yan, Zhang Guangmiao, Jiang Yang, He Sheng, Wang Qing, Ji Tingting, Dai Inner Mongolia University of Science and Technology School of Information Engineering Baotou China

ISBN: (纸本)9798350398410

In order to explore the cognitive mechanisms of Chinese character recognition under prosthetic vision, event-related potential techniques were used on a "learning-recognition"experimental paradigm with 540 high-frequency Chinese characters subjected to pixelation processing. The experiment was divided into the first learning stage and the second recognition stage. In each stage, fourty black-on-white pixelated single characters were presented on a computer screen. Subjects were required to perform a judgment task on 40 single characters presented during the recognition stage to examine whether the repeated appearance of pixelated Chinese characters could elicit a repetition effect in recognition. The results of the experiment showed that repeated single characters in the recognition stage produced a larger positive wave in the 250-800 ms, which is known as the repetition effect of recognition. In the 250-500 ms time range after stimulus presentation, the N400 wave amplitude decreased for the repeated appearance of pixelated Chinese characters. From 400 to 800 ms, the late positive component (LPC) gradually increased. Combined prosthetic vision experiments with neuroscience to explore the brain mechanisms of pixelated Chinese character processing could prrovide new ideas for the experimental design of prosthetic vision. © 2023 ieee.

关键词： Design of experiments

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A Framework for Human Activity recognition in Multiview Environment Based on URILBP and ConvSTLSTM 16

A Framework for Human Activity Recognition in Multiview Envi...

引用

16th ieee International conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Kurchaniya, Diksha Kumar, Sanjay Delhi Technological University Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798331505264

Human Activity recognition(HAR) is an essential field of research with numerous applications in human-computer interaction, security, surveillance, and healthcare. Even with significant improvements, recognizing activities in the real world, multiview scenarios remain a challenging task due to factors like scale variations, cluttered background, and variations in viewpoints. Therefore, in order to deal with these issues, this paper presents a robust and view-invariant framework for human activity recognition. This paper presents a framework to efficiently utilize both handcrafted and deep features for temporal modeling in a multiview human activity recognition. In order to make the feature as being scale and rotation in-variant we applied the Uniform Rotation-Invariant Local Binary patterns (URI-LBP) which is a variant of local binary pattern. Subsequently, a streamelined VGG16 model with 10 layers is utilized to extract high-level spatial features while maintaining computational efficiency. These complementary features are then fed into a Spatio-Temporal Long Short-Term Memory (ST-LSTM) network, which captures temporal dependencies across video frames, enabling accurate recognition of dynamic activities. Comprehensive analyses on the benchmark datasets CASIA and IXMAS illustrate the efficiency of the proposed framework, with remarkable accuracies of 95.68% and 98.78%, respectively. The outcomes demonstrate the suggested methodology's resilience and versatility in difficult multiview human activity recognition situations. © 2024 ieee.

关键词： Convolutional neural networks

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Macro-Scale pattern recognition and Coordinate Identification in Real-time Spatio-temporal Overlap for Photonics Engineering Applications 22

Macro-Scale Pattern Recognition and Coordinate Identificatio...

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22nd IFAC conference on Technology, Culture and International Stability (TECIS)

作者： Al-Juboori, Haider South East Technol Univ Fac Engn Dept Elect Engn & Commun 806 Killeshin BldgKilkenny Rd Carlow R93 V960 Ireland

The significance of high-speed machine vision in scientific and technological fields is growing, especially with the era of Industry 4.0 technologies. There are several pattern-matching algorithms that have various intriguing applications in ultralow-latency machine vision processing. However, the low frame rate of image sensors-which usually operate at tens of hertz-fundamentally limits the processing rate. The paper will conceptualize and develop the computerized pattern recognition technique that can be applied to investigate light beam profiles and extract the desired information according to the purpose required in this case study. In the current work, the automatic detection and inspection of laser spots were designed to perform analysis and alignment for laser beam in comparison with the electron spot beam using the LabVIEW graphical programming environment, especially when the laser and electron beams overlap. This is one of the important steps for realizing the fundamental aim of test-FEL to produce short wavelengths with the second, third, and fifth harmonics at 131.5, 88, and 53 nm, respectively. The tentative version of the program achieved the elementary purpose, which fulfilled the accurate transversal alignment of the ultrashort laser pulses with the electron beam in the system of the FEL test facility at MAX-Lab, in addition to studying the beam's stability and jittering range. Copyright (C) 2024 The Authors.

关键词： intelligent systems pattern matching real-time tracking computer vision concepts supporting control automation and semi-robotic systems

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Surrogate Gradient Field for Latent Space Manipulation

Surrogate Gradient Field for Latent Space Manipulation

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

作者： Li, Minjun Jin, Yanghua Zhu, Huachun Preferred Networks Tokyo Japan

ISBN: (纸本)9781665445092

Generative adversarial networks (GANs) can generate high-quality images from sampled latent codes. Recent works attempt to edit an image by manipulating its underlying latent code, but rarely go beyond the basic task of attribute adjustment. We propose the first method that enables manipulation with multidimensional condition such as keypoints and captions. Specifically, we design an algorithm that searches for a new latent code that satisfies the target condition based on the Surrogate Gradient Field (SGF) induced by an auxiliary mapping network. For quantitative comparison, we propose a metric to evaluate the disentanglement of manipulation methods. Thorough experimental analysis on the facial attribute adjustment task shows that our method outperforms state-of-the-art methods in disentanglement. We further apply our method to tasks of various condition modalities to demonstrate that our method can alter complex image properties such as keypoints and captions.

关键词： Measurement computer vision Codes Natural languages Aerospace electronics Generative adversarial networks pattern recognition

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Learning Semantic-Aware Dynamics for Video Prediction

Learning Semantic-Aware Dynamics for Video Prediction

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

作者： Bei, Xinzhu Yang, Yanchao Soatto, Stefano Univ Calif Los Angeles Vis Lab Los Angeles CA 90024 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

We propose an architecture and training scheme to predict video frames by explicitly modeling dis-occlusions and capturing the evolution of semantically consistent regions in the video. The scene layout (semantic map) and motion (optical flow) are decomposed into layers, which are predicted and fused with their context to generate future layouts and motions. The appearance of the scene is warped from past frames using the predicted motion in co-visible regions;dis-occluded regions are synthesized with content-aware inpainting utilizing the predicted scene layout. The result is a predictive model that explicitly represents objects and learns their class-specific motion, which we evaluate on video prediction benchmarks.

关键词： Training computer vision Layout Semantics Dynamics computer architecture Predictive models

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Classification of Colorectal Cancer Tissues Using Stacking Ensemble Learning 7th

Classification of Colorectal Cancer Tissues Using Stacking E...

引用

7th International conference on Communication, Device and Networking, ICCDN 2024

作者： Das, Abhrodeep Hazra, Animesh Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri735102 India

ISBN: (纸本)9789819764648

Advancement in digital pathology has enabled deep learning-based computer vision techniques for automated diagnosis and prognosis of diseases. The essentiality of early detection and prognosis of any cancer category lies in the fact that it can speed up the subsequent medical treatment procedures of patients. About 10% of all cancer cases worldwide are related to colorectal cancer (CRC), and it is also the third most common category of cancer (Egeblad et al. in Dev Cell 18:884–901, 2010). So, it is clinically important to classify and make an objective evaluation of colorectal cancer histological images. The classification performance of current methodologies primarily relies on the use of various combinations of texture-based features and classifiers or transfer learning to classify different organisational kinds. As a result of the diversity of tissue types and characteristics present in histological images, classification is still a challenging task. In this study, we have put forth a novel and effective stacking (Wolpert in Neural Netw 5:241–59, 1992) ensemble (Zhang and Yunqian (eds) Ensemble machine learning: methods and applications. Springer Science;Business Media, 2012) technique for classification on the histopathological image analysis benchmark dataset Kather-5 K (Kather et al. in Sci Rep 6:27988, 2016). The ensemble consists of two cutting-edge deep learning architectures, ResNet18 (He et al. in Proceedings of the ieee conference on computer vision and pattern recognition 2016, pp 770–778) and EfficientNetB0 (Tan and Le Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning 2019 May 24. PMLR, pp 6105–6114), acting as weak learners, and an ANN acting as the meta-learner. The proposed approach obtained a remarkable accuracy of 97.20% in CRC classification on the said dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Colorectal cancer Convolutional neural network Ensemble learning Residual network Stacking

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Private-Shared Disentangled Multimodal VAE for Learning of Latent Representations

Private-Shared Disentangled Multimodal VAE for Learning of L...

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

作者： Lee, Mihee Pavlovic, Vladimir Rutgers State Univ Piscataway NJ 08854 USA

ISBN: (纸本)9781665448994

Multi-modal generative models represent an important family of deep models, whose goal is to facilitate representation learning on data with multiple views or modalities. However, current deep multi-modal models focus on the inference of shared representations, while neglecting the important private aspects of data within individual modalities. In this paper, we introduce a disentangled multi-modal variational autoencoder (DMVAE) that utilizes disentangled VAE strategy to separate the private and shared latent spaces of multiple modalities. We demonstrate the utility of DMVAE two image modalities of MNIST and Google Street View House Number (SVHN) datasets as well as image and text modalities from the Oxford-102 Flowers dataset. Our experiments indicate the essence of retaining the private representation as well as the private-shared disentanglement to effectively direct the information across multiple analysis-synthesis conduits.

关键词： computer vision conferences Computational modeling Data models pattern recognition Internet Task analysis

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Rich features for perceptual quality assessment of UGC videos

Rich features for perceptual quality assessment of UGC video...

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

作者： Wang, Yilin Ke, Junjie Talebi, Hossein Yim, Joong Gon Birkbeck, Neil Adsumilli, Balu Milanfar, Peyman Yang, Feng Google Inc Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

Video quality assessment for User Generated Content (UGC) is an important topic in both industry and academia. Most existing methods only focus on one aspect of the perceptual quality assessment, such as technical quality or compression artifacts. In this paper, we create a large scale dataset to comprehensively investigate characteristics of generic UGC video quality. Besides the subjective ratings and content labels of the dataset, we also propose a DNN-based framework to thoroughly analyze importance of content, technical quality, and compression level in perceptual quality. Our model is able to provide quality scores as well as human-friendly quality indicators, to bridge the gap between low level video signals to human perceptual quality. Experimental results show that our model achieves state-ofthe-art correlation with Mean Opinion Scores (MOS).

关键词： Industries computer vision Correlation User-generated content Quality assessment pattern recognition Video recording

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Graph Stacked Hourglass Networks for 3D Human Pose Estimation

Graph Stacked Hourglass Networks for 3D Human Pose Estimatio...

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

作者： Xu, Tianhan Takano, Wataru Osaka Univ Osaka Japan

ISBN: (纸本)9781665445092

In this paper, we propose a novel graph convolutional network architecture, Graph Stacked Hourglass Networks, for 2D-to-3D human pose estimation tasks. The proposed architecture consists of repeated encoder-decoder, in which graph-structured features are processed across three different scales of human skeletal representations. This multi-scale architecture enables the model to learn both local and global feature representations, which are critical for 3D human pose estimation. We also introduce a multi-level feature learning approach using different-depth intermediate features and show the performance improvements that result from exploiting multi-scale, multi-level feature representations. Extensive experiments are conducted to validate our approach, and the results show that our model outperforms the state-of-the-art.

关键词： Solid modeling computer vision Three-dimensional displays Pose estimation computer architecture Network architecture Feature extraction

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Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton

Attention-guided Image Compression by Deep Reconstruction of...

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

作者： Zhang, Xi Wu, Xiaolin Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9781665445092

We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an attention-guided refinement layer. Unlike the existing ROI image compression methods that spend an extra bit budget equally on all pixels in ROI, AGDL employs a CNN module to predict those pixels on and near a saliency sketch within ROI that are critical to perceptual quality. Only the critical pixels are further sampled by compressive sensing (CS) to form a very compact refinement layer. Another novel CNN method is developed to jointly decode the two compression layers for a much refined reconstruction, while strictly satisfying the transmitted CS constraints on perceptually critical pixels. Extensive experiments demonstrate that the proposed AGDL system advances the state of the art in perception-aware image compression.

关键词： Deep learning computer vision Image coding Codes Skeleton pattern recognition Image reconstruction

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