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4th international conference on advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2021

作者： Lijun, Yao Xiaoming, Zhu Fan, Luo Gang, Sun Zhou, Chen Jing, Yan Min, Zhou Yongchang, Cai Lingling, Wang Zelong, Cao Lan, Peng Fengqing, Bai Zifang, You Hongqiu, Xiao Pengjian, Li Zhisheng, Lv Qiang, Tang Changzhutan Tobacco Logistics Co.Ltd Ltd. of Hunan Province ChangSha410000 China Hunan Tobacco Company Chagnsha410000 China Hunan Tobacco Workers Training Center Xiangtan411100 China Hunan Normal University College of Information Science and Engineering ChangSha410081 China Hunan Normal University College of Engineering and Design ChangSha410081 China

ISBN: (纸本)9781665415965

Although deep convolutional neural networks have made breakthroughs in the accuracy and speed of single-image super-resolution, there are still many unsolved problems: firstly, How to refine the texture problem when performing super-resolution processing at a larger magnification ratio. Secondly, the existing convolutional neural network image super-resolution algorithms are prone to overfitting and insufficient convergence of the loss function. Aiming at two problems, an image super-resolution method based on generative adversarial network is proposed. The feature map is spatially transformed on the network to solve the problem of refined texture, combined with CycleGAN and SRGAN, the network structure is improved and the loss function is optimized, and the SRCICGAN algorithm is proposed to restore the four times down-sampled image to solve the loss function problem. The experiment is compared with the latest six methods on three data sets. The PSNR and SSIM indicators are 1.92% and 5.49% higher in the Flickr2K data set, respectively, and better visual effects can be obtained in terms of detailed texture. © 2021 IEEE.

关键词： Generative adversarial networks

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Satellite Attitude Estimation Based on ISAR image Sequence Interpretation

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

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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

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

Classification of A Pressing Movement by Background Removed ...

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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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Color Light Field image Encryption Algorithm Based on DNA Coding and Self-Controlled Chaotic System

Color Light Field Image Encryption Algorithm Based on DNA Co...

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2021 international conference on signal processing and Communication Technology, SPCT 2021

作者： Zhang, Xinyue Bai, Enjian Jiang, Xue-Qin Wu, Yun School of Information Science & Technology Donghua University Shanghai China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

Color light field images encryption algorithm based on frequency domain DNA coding and integer wavelet transform (IWT) is introduced. First, the light field image is decomposed into sub-view images in the angular domain plane. Subsequently, a low-frequency coefficient matrix and three high-frequency coefficient matrices are obtained in the frequency domain using the two-dimensional IWT. The low-frequency coefficient matrix is encrypted using DNA coding and a self-controlled chaotic system based on PWLCM. The high-frequency coefficient matrix is scrambled, and then the reconstructed intermediate encrypted image is obtained by a two-dimensional integer wavelet inverse transform. Finally, in the spatial domain, a diffusion operation is performed to obtain the final encrypted image. The simulation shows that this algorithm has the advantages of large key space, high operation efficiency, good encryption security and strong resistance to various attacks. © 2022 SPIE.

关键词： Frequency domain analysis

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CLIFS: CLIP-DRIVEN FEW-SHOT LEARNING FOR BAGGAGE THREAT CLASSIFICATION 31

CLIFS: CLIP-DRIVEN FEW-SHOT LEARNING FOR BAGGAGE THREAT CLAS...

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2024 international conference on image processing

作者： Ahmed, Abdelfatah Velayudhan, Divya ElMezain, Mahmoud Al Radi, Muaz Boudiaf, Abderrahmene Hassan, Taimur Deriche, Mohamed Bennamoun, Mohammed Werghi, Naoufel Khalifa Univ Abu Dhabi U Arab Emirates Abu Dhabi Univ Abu Dhabi U Arab Emirates Ajman Univ Ajman U Arab Emirates Univ Western Australia Perth Australia

ISBN: (纸本)9798350349405;9798350349399

Baggage screening in airports is a cornerstone in airport security measures. The advent of computer vision technologies in recent years has led to the development of several automated systems for identifying security threats in baggage scans. However, existing methods struggle to adapt to new threat categories when faced with a scarcity of data samples, and the rapid emergence of new threats. Hence, in this paper, we propose a novel CLIP-driven few-shot framework (CLIFS) to explore the potential of multi-modality using text-image fusion through contrastive learning to learn relevant contextual features for recognizing security threats with limited samples. By integrating features from GPT-4 generated captions with image features, CLIFS leverages both visual and textual data to significantly improve threat classification performance with limited samples in a few-shot learning context. Our proposed CLIFS was rigorously tested on the SIXray public available baggage X-ray dataset, where it outperformed state-of-the-art by 31.3% in accuracy and 28.40% in F1-score for the challenging 5-shots scenario, demonstrating its robustness and effectiveness in classifying threats from limited data samples.

关键词： Vision-Langugae Model Contrastive Loss Few-shot learning Contrastive Language image Pretraining Baggage threat classification

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JOINT PROBABILITY DISTRIBUTION REGRESSION FOR image CROPPING 30

JOINT PROBABILITY DISTRIBUTION REGRESSION FOR IMAGE CROPPING

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30th IEEE international conference on image processing (ICIP)

作者： Shi, Tengfei Chen, Chenglizhao He, Yuanbo Song, Wenfeng Hao, Aimin Beihang Univ Beijing Peoples R China China Univ Petr East China Qingdao Peoples R China Qingdao Res Inst Qingdao Peoples R China Peng Cheng Lab Shenzhen Peoples R China Informat Sci & Technol Univ Beijing Peoples R China

ISBN: (纸本)9781728198354

image cropping aims at locating a candidate (rectangle region) with the highest aesthetic quality in professional photography. One solution of the previous methods is to generate a large number of candidates and then filter them, which leads to low efficiency. Another idea directly regresses the candidate coordinates to speed up but ignores the aesthetic subjectivity of the candidate's evaluation, limiting the model's performance. In this paper, we present an Aesthetic and Composition joint Probability Distribution regression Network (ACPD-Net) to explicitly investigate the process of generating the candidate with a joint probability distribution paradigm to improve the performance of cropping results in an efficient way. The joint probability distribution paradigm between location and size branch can identify the subjective aesthetic region and satisfy the objective composition rules in an end-to-end manner. Our method has been tested on the FCDB and FLMS datasets, which shows the superiority of ACPD-Net. The code is available at https://***/flyingbird93/ACPD-Net.

关键词： image cropping image aesthetics image composition deep learning computer vision

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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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DBSwin: Transformer based dual branch network for single image deraining

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 5109-5123页

作者： Tan, Fuxiang Qian, Yurong Kong, Yuting Zhang, Hao Zhou, Daxin Fan, Yingying Chen, Long Xiao, Zhengqing Xinjiang Univ Sch Software Urumqi Peoples R China Xinjiang Univ Key Lab Software Engn Urumqi Peoples R China Key Lab Signal Detect & Proc Xinjiang Uygur Auton Urumqi Peoples R China

Rain streaks severely affect the perception of the content and structure of an image so high-performance deraining algorithms are needed in order to eliminate the effects of various rain streaks for high-level computer vision tasks. Although much progress has been made with existing deraining methods, the task of single image deraining remains challenging. In this paper, we first point out that existing Transformers lack sufficient ability to capture channel attention which restricted the ability of models in deraining. To improve the performance of deraining model, we propose a dual branch deraining network based on Transformer. One branch uses dense connections to connect Transformer modules which embed the attention of a composite channel. This branch captures channel attention more finely to learn the representation of rain streaks features. The other branch first obtains features at different scales by gradually expanding the receptive field, then uses these features to obtain attention for regional features, and finally uses the attention to guide the model to focus on areas of high rain streaks density and large scales. By fusing these two branches, the model is able to capture channel attention more finely and to focus on regions of high rain streaks density and large scales. The extensive experimental results on synthetic and real datasets demonstrate that the proposed method outperforms most advanced deraining methods.

关键词： Deraining transformer deep learning image processing channel attention

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

ApproxBioWear: Approximating Additions for Efficient Biomedi...

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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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