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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

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Proceedings of international conference on advanced Computing Applications 1

引用

丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Jyotsna Kumar Mandal Rajkumar Buyya Debashis De

ISBN: (数字)9789811652073

ISBN: (纸本)9789811652066

This book gathers selected high-quality research papers presented at the 2nd international conference on advanced Computing Applications (ICACA 2021), held virtually during 27––28 March 2021. The book is divided into four sections. These are communication and computing, signal processing and multimedia, computational intelligence and data analytics and decision computing. The topics covered are advanced communication technologies, IoT-based systems and applications, network security and reliability, virtualization technologies, compressed sensors and multimedia applications, signal image and video processing, machine learning, pattern recognitions, intelligent computing, big data analytics, analytics in bio-computing, AI-driven 6G mobile wireless networks and autonomous driving.

关键词： Computational Intelligence Artificial Intelligence signal, image and Speech processing Communications Engineering, Networks Cyber-physical systems, IoT Professional Computing

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Classification of A Pressing Movement by Background Removed Single- photon Calcium image Sequences 23

Classification of A Pressing Movement by Background Removed ...

引用

2023 8th international conference on Biomedical signal and image processing, ICBIP 2023

作者： Li, Mingkang Wang, Ruixue Han, Jiawei Yang, Yuqi Zhang, Shaomin Key Laboratory of Biomedical Engineering of Education Ministry Zhejiang Prov. Key Lab. of Cardio-Cerebr. Vasc. Detection Technol. and Med. Effectiveness Appraisal Department of Biomedical Engineering School of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China Department of Neurosurgery Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China

ISBN: (纸本)9798400707698

Calcium imaging, with its capability to record hundreds or even thousands of neurons simultaneously, has been widely applied in the fields of neuroscience and neural decoding. Current neural decoding experiments utilizing calcium imaging depend on offline processing algorithms to extract neuronal signals from the acquired calcium imaging data for training the decoder. However, the computational speed of the offline algorithms typically limits the processing efficiency of this approach. While a few studies have employed two-photon calcium imaging data for neural decoding with some success, no comparable studies have been conducted on single-photon data, which exhibits more complex background fluorescence. In this study, we utilized single-photon calcium image sequences acquired during a mouse lever-pressing experiment, improved image quality through the application of a background removal algorithm, and achieved successful classification of mouse lever-pressing behavior using a 3D residual network for decoding. Our findings demonstrate that the utilization of the background removal algorithm yields higher decoding accuracy compared to using raw images. Our study offers an efficient workflow for decoding tasks relying on single-photon calcium images and possesses extensive applications in real-time closed-loop experiments, such as those involving brain-machine interfaces. © 2023 ACM.

关键词： Neurons

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Satellite Attitude Estimation Based on ISAR image Sequence Interpretation

Satellite Attitude Estimation Based on ISAR Image Sequence I...

引用

2021 CIE international conference on Radar, Radar 2021

作者： Wang, Jiadong Li, Yachao Song, Ming Academy of Advanced Interdisciplinary Research Xidian University Xi'an China Xidian University National Laboratory of Radar Signal Processing Xi'an China Beijing Institute of Space Long March Vehicle Beijing China

ISBN: (纸本)9781665498142

This paper presents a novel approach for the estimation of satellite attitude based on ISAR image interpretation. The solar panel of the satellite is segmented from the initial ISAR image using Pix2pixGAN network, and morphological method is used for edge extraction according to the parallelogram characteristics of the solar panel. According to the projection process of ISAR imaging, the optimization function for the 3D absolute attitude estimation of the satellite solar panel is established, and the attitude of the satellite is solved by iteration. The experimental results show the effectiveness of the proposed method. © 2021 IEEE.

关键词： attitude estimation inverse synthetic aperture radar (ISAR) ISAR image interpretation Satellite targets

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image Electronic Evidence Screening Based on Improved SSD 6

Image Electronic Evidence Screening Based on Improved SSD

引用

6th IEEE international conference on Intelligent Computing and signal processing, ICSP 2021

作者： Liu, Yafei Jiang, Liehui Liu, Tieming Zhang, Youwei Zhengzhou University Zhongyuan Network Security Research Institute Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9780738143705

With the development of information technology, electronic evidence plays an increasingly important role in the judicial trial. In Judicial Forensics, it is difficult to extract effective electronic evidence accurately and quickly from a large number of electronic data. Traditional means generally take artificial identification to collect, which takes a long time and is not efficient. Using SSD target detection and recognition algorithm instead of traditional means can effectively reduce the time of screening evidence, but the basic SSD neural network is prone to miss detection for small targets. To solve the above problems, an improved SSD-based image electronic evidence screening method is proposed. This method optimizes the SSD neural network adaptively, introduces the attention mechanism module in the shallow convolution layer of the network to improve the representation ability of the feature map, and fuses the image features obtained from different convolution layers with multi-scale features to increase the shallow feature information. The experimental data are used to test the improved algorithm and analyze the experimental results. It is found that compared with the original SSD neural network algorithm, the detection mean average precision of the improved algorithm is increased by 4.7%, reaching 84.3%, which shows the feasibility and effectiveness of the improved algorithm. © 2021 IEEE.

关键词： Multilayer neural networks

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Neural Auto-Exposure for High-Dynamic Range Object Detection

Neural Auto-Exposure for High-Dynamic Range Object Detection

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Onzon, Emmanuel Mannan, Fahim Heide, Felix Algolux Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Real-world scenes have a dynamic range of up to 280 dB that todays imaging sensors cannot directly capture. Existing live vision pipelines tackle this fundamental challenge by relying on high dynamic range (HDR) sensors that try to recover HDR images from multiple captures with different exposures. While HDR sensors substantially increase the dynamic range, they are not without disadvantages, including severe artifacts for dynamic scenes, reduced fill factor, lower resolution, and high sensor cost. At the same time, traditional auto-exposure methods for low-dynamic range sensors have advanced as proprietary methods relying on image statistics separated from downstream vision algorithms. In this work, we revisit auto-exposure control as an alternative to HDR sensors. We propose a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. To this end, we use an HDR dataset for automotive object detection and an HDR training procedure. We validate that the proposed neural auto-exposure control, which is tailored to object detection, outperforms conventional auto-exposure methods by more than 6 points in mean average precision (mAP).

关键词： image sensors Training Computer vision Pipelines Object detection Computer architecture Dynamic range

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ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Computing at the Edge 43

ApproxBioWear: Approximating Additions for Efficient Biomedi...

引用

43rd Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Kanani, Alish Bhattacharjya, Rajat Banerjee, Dip Sankar Indian Inst Technol Jodhpur Elect Engn Dept Karwar India Parallel Wireless Inc Nashua NH USA Indian Inst Technol Jodhpur Comp Sci & Engn Dept Karwar India

ISBN: (纸本)9781728111797

Wearables in the biomedical domain have been of extensive use in the current era. Given the importance of wearable computing, it has become necessary to innovate on enhancing hardware efficiency. The domain of approximate computing offers a conclusive method to lower area, power and delay in hardware in addition to a marginal loss in accuracy. In this paper, we investigate ApproxBioWear, a technique which enables the use of approximate computing for efficient biomedical wearable computing at the edge. The methodology involves approximating additions during the functional stages of an error-resilient biomedical signal processing algorithm and determining the application accuracy. Upon evaluating the Pan-Tompkins algorithm, which is used to detect QRS peaks in ECG signals, it is observed that the ApproxBioWear approach reduces the power consumption and chip area by 19.27% and 19.71% respectively on an average with a marginal loss in accuracy.

关键词： Power demand Filtering Wearable computers image edge detection Approximate computing signal processing algorithms Electrocardiography

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Hexagonal image Compressionusing Singular Value Decomposition in Python 2

Hexagonal Image Compressionusing Singular Value Decompositio...

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2nd international conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2021

作者： Varghese, Prathibha Selva Saroja, G. Arockia Electronics and Communication Department Noorul Islam Centre for Higher Education Tamil Nadu India Dept. Electronics and Communication Engineering Noorul Islam Centre for Higher Education Tamil Nadu India

ISBN: (纸本)9781728171364

With the advent of multimedia technologies in last two decades, there is a widespread need for efficient storage and transmission of data. Dealing with the vast information interchangein this digital era, image compression for reduction in byte size of graphics image file without loss of image quality to an acceptable level becomes the large interest area. Inspired from the biological models of human fovea, hexagonal image processing has gained a lot of attention in artificial intelligence era that deals with the application of image processing system that combines the benefits of biologically motivated structures. In this paper a singular value decomposition (SVD) over hexagonal image compression which is a missing stone in computer vision which provides higher packing density, higher angular symmetry and uniform connectivity. Due to lack of developments in hexagonal imaging devices, different resampling methods like alternate pixel shift method, half pixel shift method, pseudo hexagonal pixel method for sourcing hexagonal images. SVD is one of the powerful cutting-edge technology for image compression algorithms. SVD based image compression is performed on hexagonal grid and is compared with square grid using different parameters like compression ratio, compression size, PSNR and MSE using PYTHON’s SVD function. SVD based hexagonal image achieves the goal of compression by preserving good image quality at higher compression ratios, high computational efficiency, provides low mean square error (MSE), acceptable compression size depending on application and high peak to signal ratio (PSNR). © 2021 IEEE

关键词： Singular value decomposition

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Proceedings of 2021 international conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021) 1

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丛书名： Lecture Notes in Electrical Engineering

1000年

作者： Ruidan Su Yu-Dong Zhang Han Liu

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A Band Selection Method for Hyperspectral image Based on Binary Coded Hybrid Rice Optimization Algorithm 11

A Band Selection Method for Hyperspectral Image Based on Bin...

引用

11th IEEE international conference on Intelligent Data Acquisition and advanced Computing Systems - Technology and Applications (IDAACS)

作者： Ye, Zhiwei Liu, Shiqin Zong, Xinlu Shu, Zhe Xia, Xiaoyu Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665426053

Hyperspectral image analysis is one of the most important topics in the field of remote sensing. As the band dimension of hyperspectral image increases, it may cause a curse of dimensionality. Band selection could choose a subset of bands that is the most effective for classification recognition to achieve dimensionality reduction. Furthermore, binary coding is more suitable for solving the band selection problem. Therefore, a band selection method for hyperspectral image based on binary coded hybrid rice optimization algorithm is proposed in the paper. The band selection problem is solved as a combinatorial optimization problem by defining an objective function based on the classification accuracy and the number of bands. The proposed method is compared with other nature-inspired algorithms on Indian Pines, Salinas, Kennedy Space Center, and Pavia University hyperspectral datasets. Experimental results demonstrate that the proposed method achieves satisfactory results in terms of performance and execution time for band selection.

关键词： hyperspectral image band selection hybrid rice optimization algorithm image processing

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Improving the spatial resolution of digital images and video sequences using subpixel scanning

Improving the spatial resolution of digital images and video...

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2021 All-Russian conference with international Participation "Spatial Data processing for Monitoring of Natural and Anthropogenic Processes", SDM 2021

作者： Reznik, Aleksandr L. Soloviev, Aleksandr A. Torgov, Andrey V. Institute of Automation and Electrometry of the Siberian Branch of the Russian Academy of Sciences Novosibirsk Russia

High-performance method for improving the resolution of digital images and video sequences based on minimum-variance signal reconstruction are considered. A distinctive feature of the developed algorithms is that they allow (with the availability of modern computing power) to obtain improved images and video in "real time". © 2021 Copyright for this paper by its authors.

关键词： High-performance algorithms image reconstruction

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