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检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

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Research on Real-Time Data Transmission and signal processing System of CIM Practical Training Teaching Platform Based on 5G Network

Research on Real-Time Data Transmission and Signal Processin...

引用

image processing and Computer applications (ICIPCA), ieee international conference on

作者： Xuemei Cheng Hao Zhang Shandong University of Engineering and Vocational Technology Jinan China Poly Shandong Real Estate Group Corporation Limited Jinan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

In order to solve the problems of “digital signal processing”, such as many common knowledge points, difficult teaching, difficult visual teaching, and so on, a software platform for “signal processing” open teaching is built based on Visual C++6.0. This system is to cooperate with the teaching content of CIM, but also to improve the practical ability of students, so that they can more easily and quickly understand some of the more complex digital signal theory. The timing of the beam position signal is optimized based on the existing down sampling method, and the digital AGC and slow application signal processing modules are completed. We need to get turn-by-turn, quick application, and track closing data simultaneously. The experiment shows that the system can obtain the multi-frequency position information including the actual running state well. The advantages of this system are easy to control parameters, easy to use, and strong expansibility. This project plans to further improve the system in the future research work so as to achieve better teaching and practical application purposes.

关键词： Training Human computer interaction Visualization image processing Education signal processing Sampling methods Software Real-time systems Timing

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Animal Species image Classification 3

Animal Species Image Classification

引用

3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Prudhivi, Likhith Narayana, M. Subrahmanyam, Ch Gopi Krishna, M. Chavan, Salim Infosys Ltd Hyderabad India Anurag University Electronics & Communication Engineering Hyderabad India Vignan Institute of Technology and Science. Electronics & Communication Engineering Telangana India Vardhaman College of Engineering Electronics & Communication Engineering Shamshabad India Govindrai Wanjari College of Engineering and Technology Nagpur India

ISBN: (纸本)9798350320749

In forests, in order to protect wild animals, real-time animal species identification is done via picture recognition. Many computer vision approaches were first presented in the past, however they couldn't meet the demands since accuracy decreased as technology evolved. Nonetheless, accuracy in the categorization, identification, and segmentation of pictures was significantly improved as a consequence of the inclusion of multiple approaches. In order to achieve a high degree of accuracy, this study intends to provide an effective approach for animal species picture categorization. Convolutional neural networks have been developed specifically for the categorization of images. To achieve high accuracy, final layer features are learned and synchronised to the pre-trained model. With the dataset, a variety of architectures for deep learning are evaluated. © 2023 ieee.

关键词： image recognition

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A Comparative Study of image Classification Models for Terrain Recognition 3

A Comparative Study of Image Classification Models for Terra...

引用

3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Krishna Kumar, V. Karthika, V. Mohana, T. Mugebur Rahman, A. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

Terrain recognition is vital for various applications such as autonomous navigation, environmental monitoring, military operations, and urban planning. It enables safe navigation for autonomous vehicles, aids in environmental surveillance, assists in military reconnaissance, and supports urban infrastructure development. Recent years have witnessed the augmentation of machine learning models with online feature extraction techniques and self-supervised learning frameworks. However, a notable challenge lies in managing the complexity inherent in processing extensive datasets or numerous features. In this study, this problem is addressed by employing transfer learning for classifying terrains using images. The effectiveness of three widely used pre-trained convolutional neural network models - ResNet50, VGG16, and InceptionV3 - was compared. The findings demonstrate the strong performance of the models, emphasizing the effectiveness of transfer learning in tasks focused on terrain recognition. © 2024 ieee.

关键词： Self-supervised learning

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Enhancing Dehazing Performance of Single Optical Satellite images using Gamma Correction and Improved DCP

Enhancing Dehazing Performance of Single Optical Satellite I...

引用

2023 international conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Regan, D. Priyan, S. Vishnu Siddartha Institute of Science and Technology Dept. of ECE Puttur India Kings Engineering College Dept. of Bio-Medical Engineering Chennai India

ISBN: (纸本)9798350319200

Dehazing is an essential preprocessing step for improving the quality of satellite imagery. Hazy conditions can significantly impact the visibility and clarity of satellite images, making it difficult to extract useful information from them. In recent years, several dehazing techniques have been proposed for satellite imagery, including contrast stretching, dark channel prior, and adaptive gamma correction. In this research work, we propose a novel haze removal approach that combines gamma correction with modified dark channel prior-based haze removal approach in the YCbCr color space. Our approach enhances the luminance component of the image by applying gamma correction to the Y channel, and reduces computational complexity by subsampling and combining the chrominance channels. The performance of the proposed system is assessed using peak signal to noise ratio (PSNR) and structural similarity index (SSIM) and compared with the state-of-the-art methods. Experimental results demonstrate that the proposed our approach outperforms other methods in terms of dehazing and contrast improvement, suggesting its potential for various applications that require high-quality satellite image processing. © 2023 ieee.

关键词： DCP Gamma correction Satellite image dehazing YCbCr Color space

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Privacy-Assured and Multi-Prior Recovered Compressed Sensing for image Compression-Encryption applications

Privacy-Assured and Multi-Prior Recovered Compressed Sensing...

引用

Data Compression conference (DCC)

作者： Huang, Hui Xiao, Di Li, Min Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

ISBN: (纸本)9781665478939

Compressed sensing (CS), a popular signal processing technique, can achieve compression and encryption simultaneously. Therefore, it has extension applications in various fields. However, CS is vulnerable to cryptographic attacks for its linear encoding process. To solve this problem, a permutation-diffusion structure is designed and embedded to the CS encoding process. In addition, it can increase the key space while compressing. Since the permutation-diffusion structure reduces the sparseness, superior recovery performance cannot be achieved. Therefore, the multi-prior regularization recovery strategy is designed to improve the recovery performance, where the multi-prior regularization term denotes l(1) norm, total variation (TV) and low rank. The simulation results and analyses demonstrate that the proposed encoding scheme can resist cryptographic attacks, increase the key space while compressing, and achieve 1.54dB PSNR gain on average in comparison with the existing schemes.

关键词： image coding TV Simulation Data compression Resists signal processing Encoding

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QUARTER LAPLACIAN FILTER FOR EDGE AWARE image processing

QUARTER LAPLACIAN FILTER FOR EDGE AWARE IMAGE PROCESSING

引用

ieee international conference on image processing (ICIP)

作者： Gong, Yuanhao Tang, Wenming Zhou, Lebin Yu, Lantao Qiu, Guoping Shenzhen Univ Coll Elect & Informat Engn Shenzhen Peoples R China Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

ISBN: (纸本)9781665441155

This paper presents a quarter Laplacian filter that can preserve corners and edges during image smoothing. Its support region is 2x2, which is smaller than the 3x3 support region of the classical Laplacian filter. Thus, it is more local. Moreover, this filter can be implemented via the classical box filter, leading to high performance for real time applications. Finally, we show its edge preserving property in several image processing tasks, including image smoothing, texture enhancement, and low-light image enhancement. The proposed filter can be adopted in a wide range of image processing applications.

关键词： quarter Laplacian edge preserve box

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Evolutionary Algorithms Based Feature Selection for Remote Sensing image Classification 8

Evolutionary Algorithms Based Feature Selection for Remote S...

引用

8th international conference on Frontiers of signal processing, ICFSP 2023

作者： Amri, Mohammed Bilel Yedjour, Dounia Larabi, Mohammed El Amin Centre des Techniques Spatiales Agence Spatiale Algérienne Arzew Algeria Université Mohamed Boudiaf USTO-MB Département Informatique Laboratoire SIMPA Oran Algeria Université des Sciences et de la Technologie D'Oran Mohamed Boudiaf USTO-MB Oran Algeria Algerian Space Agency Algeria

ISBN: (纸本)9798350308792

The amount of spatial datasets currently available has fundamentally increased and become an essential source of information for extremely important and strategic decisions. Due to the redundant and unused features, the direct usage of raw data might significantly affect the final decisions. To solve this issue, feature selection (FS), as a crucial preprocessing step, is used. It seeks to pick out the most relevant information in order to reduce the data dimensionality and the computational resources, and then increases the accuracy. This paper proposes the Wrapper Feature Selection (WFS) technique for remote sensing data classification. More specifically, evolutionary algorithms as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have been utilized to pinpoint the optimum features of the Support Vector Machine (SVM) classifier. To this end, hyperspectral images from Earth Observing-1 (EO-1) have been used to test this strategy.1 our work demonstrates that a good performance can be achieved for FS and image classification. © 2023 ieee.

关键词： Genetic algorithms

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A Tiny Efficient U-Net with Gated Linear Attention for Medical image Segmentation

A Tiny Efficient U-Net with Gated Linear Attention for Medic...

引用

2024 ieee international conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ju, Sibo Chen, Zhaozhen Liao, Xiangwen Shen, Yiqing He, Junjun Su, Yanzhou Fuzhou University College of Computer and Data Science Fuzhou China Johns Hopkins University Department of Computer Science Baltimore United States Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Medical image segmentation is crucial for diagnosis and treatment planning. While recent advancements in deep learning, particularly UNet variants, have improved segmentation performance, they often result in increased model complexity, which limits their real-time applicability on resource-constrained devices in clinical settings. To address this challenge, we present the Tiny Efficient U-Net (TE-UNet), a novel lightweight model balancing efficiency and accuracy. TE-UNet uses a U-shaped encoder-decoder framework with a gated linear attention mechanism to process low-level and high-level features, preserving details and reducing complexity. It employs depth-wise separable convolutions for higher-level processing, enhancing efficiency without losing performance. Additionally, skip connections improve multi-scale feature extraction and information flow. Experiments on two public datasets across different modalities demonstrate that TE-UNet outperforms ten state-of-the-art methods, maintaining a parameter size under 40KB and low computational cost. TE-UNet makes real-time segmentation more accessible for various clinical applications. © 2024 ieee.

关键词： Deep learning

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Multi-Attention Spatio-Temporal Feature Extraction Network for image Deraining 8

Multi-Attention Spatio-Temporal Feature Extraction Network f...

引用

8th international conference on Intelligent Computing and signal processing, ICSP 2023

作者： Feng, Shangyu Yin, Dejun Sun, Shijun Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350302455

At present, the field of unmanned driving is facing the public's safety question, which directly hinders the further development of unmanned driving. In order to adapt to the complexity and variability of driving environment and enhance the anti-jamming ability of unmanned vehicles, we proposes a lightweight rain removal network based on temporal and spatial feature correlation (MASTF-Net). In this method, the dewatering process is divided into several stages. In each stage, spatial features are extracted by global and local feature extraction modules, and then feature spatio-temporal correlation is realized by residual memory modules integrated by multiple attention mechanisms. In this way, MASTF can restore fine images of rain noise and preserve the background information well. The MASTF also has an extremely low parameter quantity and FLOPs, making it deployable. A large number of experimental results on real datasets and synthetic datasets show that our scheme has higher performance and efficiency than some state-of-the-arts methods. © 2023 ieee.

关键词： Feature extraction

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3D Target Detection of Tractor in Agricultural Scene Based on Lidar 8

3D Target Detection of Tractor in Agricultural Scene Based o...

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8th international conference on Communication, image and signal processing, CCISP 2023

作者： Zhibo, Chen Caicong, Wu Banghao, Lin College of Information and Electrical Engineering China Agricultural University Key Laboratory of Agricultural Machinery Monitoring and Big Data Applications Ministry of Agriculture and Rural Affairs Beijing China

ISBN: (纸本)9798350305838

Real time and accurate detection of agricultural machinery in the working scene is an important basis to improve the safety of agricultural machinery driverless and autonomous operation. This paper presents a 3D target detection method of tractor based on GNSS/INS/Lidar/camera data. Firstly, an on-board data acquisition platform based on multi-source sensors is constructed to obtain the tractor image, point cloud and position and attitude data of the acquisition vehicle in the common working scenes of agricultural machinery (machinery depot, tractor road and farmland), the data are time synchronized and space calibrated. Then, distortion correction of point cloud, the point cloud and image are jointly labelled, and the tractor test data set of images, point cloud and GNSS/IMU information is constructed. 643 frames of machinery depot scene data, 321 frames of tractor track scene data, and 320 frames of farmland scene data are obtained. The number of tractors selected is 6250. Finally, according to the characteristics of tractor driving and operation, the region of interest of tractor target detection is defined, the point cloud within its range is extracted, and the ground point cloud is filtered by RANSAC method. Then the data set is input into PVRCNN++ network for training according to the proportion of 5:1 between training set and test set, and finally the tractor 3D target detection model is obtained. The test results show that the detection accuracy of the model for tractors in machinery depot, tractor road and farmland are 90.21%, 87.59%, and 99.85% respectively, the recall rates are 86.72%, 95.13%, and 98.89% respectively, and the F1 scores are 88.43%, 91.20%, and 99.37% respectively. The model can better adapt to the agricultural scene. © 2023 ieee.

关键词： Tractors (agricultural)

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