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共 18153 条记录，以下是601-610 订阅

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See What a Strabismus Patient Sees Using Eye robots

See What a Strabismus Patient Sees Using Eye Robots

引用

IEEE/RSJ international conference on Intelligent robots and Systems (IROS)

作者： Huang, Yidi Wei, Qi Demer, Joseph L. Yao, Ningshi George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA George Mason Univ Dept Bioengn Fairfax VA 22030 USA Univ Calif Los Angeles Dept Ophthalmol Los Angeles CA 90054 USA Univ Calif Los Angeles Stein Eye Inst Los Angeles CA 90054 USA

ISBN: (纸本)9781665491907

Ocular mobility disorders such as strabismus affect millions of people. Patients' descriptions of their symptoms, such as what they see and how their vision has changed, are important for ophthalmologists to diagnose, monitor progression, and evaluate treatment effectiveness. However, such verbal depiction may be vague and subjective. A data-driven simulator that visualizes abnormal vision experienced by a strabismic patient can be helpful to objectively illustrate each individual's vision condition and thus to better understand and manage strabismus. To fulfill this technical void, this paper presents the first vision visualization robot that uses human eye movement data to simulate strabismic vision. We developed a robotic binocular eye platform, which is capable of displaying simulated visual scenes using its onboard cameras. Based on the hypothesis that a human's binocular vision fusion process can be mimicked as a homography transformation from one view to another view, we developed a pipeline to estimate the time-varying homography matrix, and generate the fused view of a human's binocular vision. The effectiveness of the proposed method is demonstrated through experiments with eye movement data from both healthy individuals and strabismic patients.

关键词： Stereo image processing

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Image Inpainting with Semantic-Aware Transformer 48

Image Inpainting with Semantic-Aware Transformer

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Chen, Shiyu Yu, Wenxin Wang, Qi Gong, Jun Chen, Peng Southwest University of Science and Technology China Southwest Automation Research Institute China Chengdu Hongchengyun Technology Co. Ltd China

ISBN: (纸本)9781728163277

Image inpainting has made huge strides benefiting from the advantages of convolutional neural networks (CNNs) in understanding high-level semantics. Recently, some studies have applied transformers to the visual field to solve the problem that the convolution kernel cannot attend to longdistance information. However, unlike other vision tasks, there is much interference from damaged information in image inpainting tasks. We propose a new Semantic-Aware Transformer, which in addition to including a self-attention block like previous vision transformers, also has a block for learning semantics from QSVM. Specifically, to provide more valid information, we design a Quantized Semantic Vector Memory (QSVM) that encodes and saves semantic features in images as quantized vectors in latent space. Experiments on different datasets demonstrate the effectiveness and superiority of our method compared with the existing state-of-the-art. © 2023 IEEE.

关键词： Computer vision Image Inpainting vision Transformer VQ-VAE

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Hybrid Object Tracking with Events and Frames

Hybrid Object Tracking with Events and Frames

引用

IEEE/RSJ international conference on Intelligent robots and Systems (IROS)

作者： Li, Zhichao Piga, Nicola A. Di Pietro, Franco Iacono, Massimiliano Glover, Arren Natale, Lorenzo Bartolozzi, Chiara Ist Italiano Tecnol Humanoid Sensing & Percept Genoa Italy Ist Italiano Tecnol Event Driven Percept Robot Genoa Italy Univ Genoa Genoa Italy

ISBN: (纸本)9781665491907

Robust object pose tracking plays an important role in robot manipulation, but it is still an open issue for quickly moving targets as motion blur and low frequency detection can reduce pose estimation accuracy even for state-of-the-art RGB-D-based methods. An event-camera is a low-latency vision sensor that can act complementary to RGB-D. Specifically, its sub-millisecond temporal resolution can be exploited to correct for pose estimation inaccuracies due to low frequency RGB-D based detection. To do so, we propose a dual Kalman filter: the first filter estimates an object's velocity from the spatio-temporal patterns of "events", the second filter fuses the tracked object velocity with a low-frequency object pose estimated from a deep neural network using RGB-D data. The full system outputs high frequency, accurate object poses also for fast moving objects. The proposed method works towards low-power robotics by replacing high-cost GPU-based optical flow used in prior work with event-cameras that inherently extract the required signal without costly processing. The proposed algorithm achieves comparable or better performance when compared to two state-of-the-art 6-DoF object pose estimation algorithms and one hybrid event/RGB-D algorithm on benchmarks with simulated and real data. We discuss the benefits and trade-offs for using the event-camera and contribute algorithm, code, and datasets to the community. The code and datasets are available at https://***/event-driven-robotics/Hybrid-object-tracking-with-events-and-frames.

关键词： Cameras

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Computer vision-based Method for Detecting Cracks in Reservoir Dams 4

Computer Vision-based Method for Detecting Cracks in Reservo...

引用

4th international conference on signal processing and Computer Science, SPCS 2023

作者： Qi, Bo Hou, Wenang Nenjiang Hydraulic Construction Company Nehe China Nanjing Hydraulic Research Institute Nanjing China

ISBN: (纸本)9781510672505

Reservoir dams serve as critical infrastructure. However, during their prolonged operation, cracks tend to appear on surfaces, particularly on slopes. The development of cracks poses risks to the stability of reservoir dams. This paper employs computer vision and deep learning methods to analyze images of reservoir dams. The primary directional characteristics of dam cracks are identified by leveraging deep learning method, and the distinctive features of cracks are calculated by utilizing the OpenCV platform and filtering techniques. Additionally, the fundamental features of cracks are determined through analysis and calculations using crack images of a dam as an example. © 2023 SPIE.

关键词： Computer vision

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An Integrated Technical Scheme for the Wheel Set Flaw Detection robot System of EMUs

An Integrated Technical Scheme for the Wheel Set Flaw Detect...

引用

2022 international conference on signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Tao, Xinao Guo, Zhida Sun, Peng Li, Zhuo Dalian Jiaotong University Dalian116028 China China Academy of Railway Sciences Beijing100081 China

ISBN: (纸本)9781510664616

Starting from the demand analysis, based on the intelligent six axis robot combined with ultrasonic flaw detection equipment, the advanced robot technology and ultrasonic acquisition, processing and identification technology are used to build a flaw detection identification acquisition system for the wheel set robot of the EMU. The goal is to realize the automatic collection and transmission of the detection information of the wheel set defects of the EMU, the automatic early warning and reporting of abnormal conditions, the centralized management and resource sharing of the monitoring data, and to form the EMU wheel set flaw detector human system that integrates monitoring automation, intelligent control and management decision-making. Focusing on the application of EMU wheel set flaw detection robot in the EMU maintenance process, this paper designs the overall architecture and physical architecture of the EMU wheel set flaw detection robot system, and designs its functional modules and interface modules. Finally, the overall scheme design of the EMU wheel set flaw detection robot system is completed. © 2023 SPIE.

关键词： Decision making

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17th international Workshop on Design and Architecture for signal and Image processing, DASIP 2024

17th International Workshop on Design and Architecture for S...

引用

17th international Workshop on Design and Architecture for signal and Image processing, DASIP 2024

ISBN: (纸本)9783031628733

The proceedings contain 9 papers. The special focus in this conference is on Design and Architecture for signal and Image processing. The topics include: sEMG-Based Gesture Recognition with Spiking Neural Networks on Low-Power FPGA;A Highly Configurable Platform for Advanced PPG Analysis;preface;Standalone Nested Loop Acceleration on CGRAs for signal processing Applications;optimising Graph Representation for Hardware Implementation of Graph Convolutional Networks for Event-Based vision;Improving the Energy Efficiency of CNN Inference on FPGA Using Partial Reconfiguration;scratchy: A Class of Adaptable Architectures with Software-Managed Communication for Edge Streaming Applications.

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Deep Learning-Based Stereo Camera Multi-Video Synchronization 48

Deep Learning-Based Stereo Camera Multi-Video Synchronizatio...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Boizard, Nicolas Haddad, Kevin El Ravet, Thierry Cresson, Francois Dutoit, Thierry University of Mons ISIA Lab Mons Belgium Big Projects Mons Belgium

ISBN: (纸本)9781728163277

Stereo vision is essential for many applications. Currently, the synchronization of the streams coming from two cameras is done using mostly hardware. A software-based synchronization method would reduce the cost, weight and size of the entire system and allow for more flexibility when building such systems. With this goal in mind, we present here a comparison of different deep learning-based systems and prove that some are efficient and generalizable enough for such a task. This study paves the way to a production ready software-based video synchronization system. © 2023 IEEE.

关键词： Stereo vision

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Dual-Stream Siamese vision Transformer With Mutual Attention For Radar Gait Verification 48

Dual-Stream Siamese Vision Transformer With Mutual Attention...

引用

48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Ji, Ran Li, Jiarui He, Wentao Ren, Jianfeng Jiang, Xudong University of Nottingham School of Computer Science Ningbo China University of Nottingham Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute Ningbo China Nanyang Technological University School of Electrical & Electronic Engineering Singapore

ISBN: (纸本)9781728163277

The inconspicuousness of human gait characteristic in radar signal makes it hard to differentiate different identities. In this work, a Dual-stream Siamese vision Transformer with Mutual Attention is proposed to verify whether a pair of radar gait sequences originate from the same person or not. The proposed Siamese vision Transformer extracts pairwise discriminant spectral information from spectrograms and cadence velocity diagrams (CVDs). The proposed Mutual Attention scheme extracts the discriminant information from each stream through a self-attention mechanism and discovers the complement information cross the two streams through a cross-attention mechanism. The proposed method is evaluated on a large benchmark radar gait verification dataset. It significantly outperforms state-of-the-art solutions. © 2023 IEEE.

关键词： Cadence velocity diagram Mutual attention Radar gait verification Siamese network Spectrogram vision transformer

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DAPLANKTON: BENCHMARK DATASET FOR MULTI-INSTRUMENT PLANKTON RECOGNITION VIA FINE-GRAINED DOMAIN ADAPTATION 31

DAPLANKTON: BENCHMARK DATASET FOR MULTI-INSTRUMENT PLANKTON ...

引用

2024 international conference on Image processing

作者： Batrakhanov, Daniel Eerola, Tuomas Kraft, Kaisa Haraguchi, Lumi Lensu, Lasse Suikkanen, Sanna Camarena-Gomez, Maria Teresa Seppala, Jukka Kalvidianen, Heikki LUT Univ Comp Vision & Pattern Recognit Lab Lappeenranta Finland Finnish Environm Inst Res Infrastruct Helsinki Finland Finnish Environm Inst Marine & Freshwater Solut Helsinki Finland Inst Espanol Oceanografia Ctr Oceanografico Malaga Malaga Spain

ISBN: (纸本)9798350349405;9798350349399

Plankton recognition provides novel possibilities to study various environmental aspects and an interesting real-world context to develop domain adaptation (DA) methods. Different imaging instruments cause domain shift between datasets hampering the development of general plankton recognition methods. A promising remedy for this is DA allowing to adapt a model trained on one instrument to other instruments. In this paper, we present a new DA dataset called DAPlankton which consists of phytoplankton images obtained with different instruments. Phytoplankton provides a challenging DA problem due to the fine-grained nature of the task and high class imbalance in real-world datasets. DAPlankton consists of two subsets. DAPlankton(LAB) contains images of cultured phytoplankton providing a balanced dataset with minimal label uncertainty. DAPlankton(SEA) consists of images collected from the Baltic Sea providing challenging real-world data with large intra-class variance and class imbalance. We further present a benchmark comparison of three widely used DA methods.

关键词： Plankton recognition domain adaptation fine-grained recognition benchmark dataset

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High Performance Target Detection Based on Swin Transformer 2

High Performance Target Detection Based on Swin Transformer

引用

2nd international conference on signal processing and Intelligent Computing, SPIC 2024

作者： Liang, Zhitian Liu, Jun Zhang, Jiabao Zhao, Jibin Shenyang Ligong University Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China

ISBN: (纸本)9798350368888

In modern computer vision tasks, Swin Transformer is an emerging deep learning model architecture that innovates on the basis of the traditional transformer architecture by the introduction of a mechanism of attention called Shifted Windows to improve the performance and efficiency of the model. This paper presents a novel target detection method based on the original YOLOv5 detection model. The method combines the multi-layer attention mechanism of Swin Transformer with adaptive features and introduces Swin Transformer as a backbone network to replace the original backbone network. On the one hand, swin transformer is used as the backbone network of the target detection, which adopts the multi-layer attention mechanism to perform self-attention operation on features with different scales, and divides the feature map of the target image through the window shift mechanism to achieve the fusion of multiple disjoint regions. On the other hand, swin transformer's adaptive ability in multi-scale and multi-level feature representation enables it to achieve more accurate data analysis, thus achieving high performance target detection and multi-scale perception. © 2024 IEEE.

关键词： Machine vision

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