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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

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A Comparative Analysis of image Partitioning and Compression Mechanisms 15

A Comparative Analysis of Image Partitioning and Compression...

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15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaudhari, Sampada P. Thakur, Nileshsingh V. Dept. of Computer Science and Engineering Prof Ram Meghe College of Engineering & Management Badnera India Dept. of Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331300579

This paper presents a comprehensive comparative analysis of image partitioning and compression mechanisms, two fundamental techniques in image processing and data compression. image partitioning involves dividing an image into smaller components, while compression aims to reduce the size of digital images while preserving visual quality. The analysis encompasses various aspects of both techniques, including methodologies, algorithms, and performance metrics. For image partitioning, segmentation methods such as block-based, region-based, and hierarchical partitioning are examined, along with their respective advantages and limitations. Additionally, the paper explores compression mechanisms such as transform coding, quantization, entropy coding, and predictive coding, highlighting their roles in achieving efficient compression. Furthermore, the comparative analysis evaluates the tradeoffs between different partitioning and compression techniques in terms of compression ratio, image quality, computational complexity, and applicability across different types of images and applications. Various compression standards such as JPEG and PNG are also reviewed to provide insights into real-world implementations. Finally, the paper discusses future research directions and emerging trends in image partitioning and compression, including the integration of machine learning techniques for improved segmentation and compression performance. The findings presented in this comparative analysis can serve as a valuable resource for researchers, practitioners, and developers working in the fields of image processing, data compression, and multimedia systems. © Grenze Scientific Society, 2024.

关键词： image compression

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How to combine issues related to autonomous vehicles - a proposal with a literature review 26

How to combine issues related to autonomous vehicles - a pro...

引用

26th IEEE Signal processing: algorithms, Architectures, Arrangements, and Applications, SPA 2023

作者： Balcerek, Julian Pawlowski, Pawel Poznan University of Technology Division of Signal Processing and Electronic Systems Institute of Automatic Control and Robotics Poznan Poland

ISBN: (纸本)9798350304985

This article proposes a model that combines the issues related to autonomous vehicles into seven groups. The groups are included in mutual iterations between the user, the autonomous vehicle and the environment. They are: vehicle automation, environmental sensors, automatic perception of the environment based on the processing of vision signals, communication with external devices, interaction with the user, direct transmission of signals to the environment, and future development plans. Bearing in mind the proposed model, various latest solutions presented in the state-of-the-art literature or already offered in vehicles are collected and described. © 2023 Division of Signal processing and Electronic systems, Poznan University of Technology (DSPES PUT).

关键词： image processing

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Single image Rain Removal Algorithm Based on U-Net and Vision Transformer 16

Single Image Rain Removal Algorithm Based on U-Net and Visio...

引用

2023 16th International conference on Computer and Electrical Engineering, ICCEE 2023

作者： Cui, Hua Yang, Zhiqiang Shi, Yanzi School of Information Engineering Chang' An University Xi'an China School of Automobile Chang' An University Xi'an China

Rain will significantly lower image quality, which will undoubtedly have an impact on how well outdoor computer vision systems operate, such as autonomous driving. This paper proposes a two-branch deep neural network consisting of an attention guided U-Net and Vision Transformer, which could capture intra-field details and cross-patch relationships to obtain good global and local deraining effects. In order to ensure both branches pose positive effects on the target derain task, we specifically design a patch boot image module to achieve complemented feature fusion, which adaptively selects informative intra-field regions guided by the patch-wise importance map. Moreover, Wasserstein distance between the prediction and reference image is applied as the objective function to pursue better image quality measurement. In comparison to existing algorithms, the suggested method may more effectively remove rain and restore the background details, according to qualitative and quantitative results on the public datasets Rain200L and Rain200H. On the aforementioned datasets, the peaks of the structural similarity (SSIM) and signal-to-noise ratio (PSNR) were 32.55/0.9476 and 26.12/0.8826, respectively. © Published under licence by IOP Publishing Ltd.

关键词： Rain

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Application of Artificial Intelligence-Based Nonlinear Filtering Techniques in Chaotic Communication

Application of Artificial Intelligence-Based Nonlinear Filte...

引用

2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： Guo, Chengbo Wu, Ruihan Ji, Hongying Xi'an University of Posts & Telecommunications Xi'an China Institute of Disaster Prevention Sanhe China Yanbian University Yanji China

ISBN: (纸本)9798350314670

Chaotic communication, due to its good interference immunity and privacy, is widely used in confidential communication. However, the existence of real-life nonlinear channels interferes strongly with the actual communication. Conventional adaptive filters have good handling effect on nonlinear channels. However, the traditional LMS adaptive filter cannot satisfy both the adaptive filter convergence speed and steady-state error. The NLSM can better handle nonlinear data and capture the noise in the data, but the accuracy of the algorithm needs to be improved for pseudo-random sequences. Therefore, a nonlinear filtering algorithm based on LSTM neural network is proposed in this paper based on common algorithms of artificial intelligence in order to get better recovery of signals passing through nonlinear channels. The algorithm combines the ability of LSTM with strong generalization and can handle complex nonlinear relationships. Simulation results show that the algorithm proposed in this paper recovers the signal with less error and more accurate results, and can make the signal through the nonlinear channel get better recovery. The results verify the superiority of the algorithm in this paper and show that the LSTM neural network nonlinear filtering algorithm has a broader application prospect in the field of chaotic communication. © 2023 IEEE.

关键词： Adaptive filters

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Few-Shot Fine-Grained image Classification: A Survey 4

Few-Shot Fine-Grained Image Classification: A Survey

引用

4th International conference on Natural Language processing, ICNLP 2022

作者： Liu, Ying Bai, Yilun Che, xin He, Jinglu Xi'an University of Posts and Telecommunications Center for Image and Information Processing Xi'an China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

With the development of deep learning, fine-grained image classification task has made remarkable achievements, but it largely depends on a large number of annotated data samples. However, in practical applications, such as public safety, medicine, endangered species and other professional fields, such fine-grained samples with annotations are difficult to obtain. When the samples are insufficient, the traditional deep learning training method is easy to yield the over fitting phenomenon, which greatly limits the applicability of such algorithms. The general few-shot algorithm is not designed for the fine-grained characteristics of the image, so it can not effectively extract the subtle differences in the image, which results in poor performance. Therefore, fine-grained image classification task based on few-shot learning has attracted more and more attentions in recent years. This paper introduces the task of few-shot fine-grained image classification, and summarizes the literatures in this field over the recent years. According to the few-shot learning methods, the existing algorithms are divided into the following three categories: meta learning based methods, multimodal based methods, and data augmentation based methods. In addition, the common datasets and performance evaluation indexes in the field of few-shot fine-grained image classification are summarized. Finally, the technical difficulties and research trends in this field are discussed. © 2022 IEEE.

关键词： image classification

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A Two Stage Learning Algorithm for Hyperspectral image Classification 5

A Two Stage Learning Algorithm for Hyperspectral Image Class...

引用

5th International conference on Natural Language processing, ICNLP 2023

作者： Li, Shuying Zhang, Qiang Cheng, Lei Peng, Baidong Xi'an University of Posts and Telecommunications School of Automation Xi'an China

ISBN: (纸本)9798350302219

Since the excellent performance of Support Vector Machine (SVM) in handling with high-dimensional data, it is often used in the field of hyperspectral image (HSI) classification. However, traditional SVM methods only uses a single Mercer kernel function as base kernel, which does not represent the similarity of samples well. Meanwhile, it cannot utilize the spatial background information to enhance the HSI classification results. To address these issues, the paper proposes a two-stage learning (TSL) algorithm for HSI classification. In the first stage, a new Kernel Singular Value Decomposition-Multiple Kernel learning (KSVD-MKL) method is proposed for SVM Multiple Kernel Learning (MKL), which allows the best combination of kernels to be composed by using Gaussian kernels with different bandwidth scales. In the second stage, the KSVD-MKL classification is used as the initial spectral term classification results. Then, spatial information is modeled by using Conditional Random Field (CRF) observation fields and labels, and the KSVD-MKL classification results are optimized. Experiment results on public Indian pines and Botswana datasets demonstrate that the classification accuracy of the proposed method is effectively improved against existing algorithms. © 2023 IEEE.

关键词： Singular value decomposition

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Intelligent algorithms of digital processing of biomedical images in wavelet methods

Intelligent algorithms of digital processing of biomedical i...

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International conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Zaynidinov, H. Xuramov, L. Khodjaeva, D. Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Uzbekistan Samarkand State University Samarkand Uzbekistan

ISBN: (纸本)9781032669663

To date, the use of computer vision in biomedicine with the use of artificial intelligent systems, which in turn receive information from images, and then give out new knowledge and final conclusions about the disease is the most relevant. images are obtained from different video sensors using them in different frequency ranges, which can be represented using two-dimensional brightness matrices and two-dimensional sensors. You can improve the image quality in several steps: preprocessing, highlighting the characteristic properties of the image (filtering, brightness equalization) classification and conclusion. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Luminance

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Terahertz security detection of hazardous materials based on Unet 3

Terahertz security detection of hazardous materials based on...

引用

3rd International conference on Advanced algorithms and Signal image processing, AASIP 2023

作者： Guo, Yufeng Jin, Shangzhong Li, Hongguang China Jiliang University HangZhou China Xi'an Institute of Applied Optics China

ISBN: (纸本)9781510668522

A dangerous item segmentation algorithm for passive terahertz imaging security inspection is proposed in response to the difficulty and low precision of dangerous item recognition in the passive terahertz imaging. First of all, the hypothesis of the dangerous item local structural difference and the hypothesis of the local luminance difference are made to locate the Region Of Interest(ROI) where dangerous items might exist in terahertz images. Meanwhile, the shallow convolutional network containing a few feature channels and nerve cells is chosen for super-resolution processing of images in ROI regions. The images are input into the U-net to obtain high-quality and clearly-outlined partitioned images of dangerous items. Finally, an experiment is conducted to verify the improvement of the detection accuracy of the proposed algorithm in comparison with traditional partitioning algorithms. This is conducive to improving the recognition rate of dangerous items by the passive terahertz imaging security inspection system. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Neurons

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Boosted Spectral Perception via Synergistic Vision for Cross-Domain Few-Shot Hyperspectral image Classification 2

Boosted Spectral Perception via Synergistic Vision for Cross...

引用

2nd IEEE International conference on Signal, Information and Data processing, ICSIDP 2024

作者： Yan, xinyi Li, Jiaojiao Zhang, Zhiyuan Xu, Haitao Zheng, Tie Du, Qian Xidian University Isn Xi'an China Nssc Key Laboratory of Eitss Beijing China Mississippi State University Department of Electrical and Computer Engineering Starkville United States

ISBN: (纸本)9798331515669

Cross-domain few-shot learning (CDFSL) hyper-spectral image classification (HSIC) techniques have significantly enhanced the performance of HSIC under a few annotations. Due to the scattered spatial distribution of HSI and limited labels, the spatial information of the HSI sample is restricted, which means the spectral information may contain more discriminative category features than spatial information. However, most existing CDFSL HSIC methods lack targeted spectral discriminative feature extraction and struggle to capture spectral subtleties. To address this, a Human Eye-like Dual-Branch Network (HEDB-Net) is proposed, comprising a central branch and a peripheral branch. The central branch focuses on extracting core features from complete spectral dimension HSI patches. Simultaneously, these patches are input into the peripheral branch via random sampling and reintegration of spectral bands, enabling the model to perceive initially dispersed spectral information. These two branches represent the model's central and peripheral vision for the spectrum, allowing it to focus on different levels of feature information and significantly boosting spectral perception capabilities. To fully leverage the advantages of synergistic vision, a consistency constraint is applied to align their category probability prediction distributions, which helps ensure consistency in classification decisions between the two branches. Experimental results on the PaviaU dataset demonstrate that HEDBNet outperforms other advanced algorithms in terms of overall accuracy(OA), average accuracy(AA), and Kappa coefficient. This proves that HEDBNet can simultaneously focus on primary and secondary spectral features, thus significantly boosting classification accuracy. © 2024 IEEE.

关键词： image annotation

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Tobacco Impurities Detection with Deep image Segmentation Method on Hyperspectral Imaging

Tobacco Impurities Detection with Deep Image Segmentation Me...

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2023 IEEE International conference on Signal processing, Communications and Computing, ICSPCC 2023

作者： Tang, Linruize Zhao, Min Shi, Shuaikai Chen, Jie Li, Jinwei Li, Qiang Li, Rengang School of Marine Science and Technology Center of Intelligent Acoustics and Immersive Communications Northwestern Polytechnical University Xi'an China Luxi Tobacco Redrying Factory Yunnan Tobacco Redrying Co Ltd. Yunnan China

ISBN: (纸本)9798350316728

The detection of impurities in tobacco raw materials is an important task, and the performance of detection algorithms directly affects the quality of tobacco products. The hyperspectral imaging system simultaneously captures the visible and near-infrared spectra containing rich spectral information that can accurately reflect material categories. In this paper, we propose a novel deep learning-based tobacco impurities detection method on hyperspectral data. It is a U-net like structure that progressively extracts multi-scale discriminant features and fuses them to find the impurities in the scene. Experiments conducted on the collected dataset show that the proposed segmentation model yields superior performance compared to other detection methods. © 2023 IEEE.

关键词： Tobacco

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