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

共 16347 条记录，以下是461-470 订阅

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Comparative Study of image processing Neural Networks using Face Mask Dataset 1

Comparative Study of Image Processing Neural Networks using ...

引用

1st IEEE International conference on Advances in Electronics, Communication, Computing and Intelligent Information systems, ICAECIS 2023

作者： Sunny Jindal, Rajni Delhi Technological University Department of Computer Science & Engineering Delhi India

ISBN: (纸本)9798350348057

Over the years there has been huge improvements in the performance of image processing algorithms due to increase in computation power of Devices as well as use of Neural Networks. This paper focuses on comparison of size and performance of different image Classification networks used widely. Each one of the machine learning models studied presents a new technique to process the input images that might be useful in different scenarios. Our main focus is to identify the framework with low latency, high accuracy as they can be used practically in surveillance equipment's that need to be fast and accurate. © 2023 IEEE.

关键词： Deep neural networks

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A SCIL-based Transformer for PolSAR image Classification

A SCIL-based Transformer for PolSAR Image Classification

引用

IET International Radar conference 2023, IRC 2023

作者： xin, xinyue Li, Ming Wu, Yan Zhang, Peng Xu, Dazhi Zheng, Jia The National Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an China The Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781839539954

Transformer has been applied for polarimetric synthetic aperture radar (PolSAR) image processing due to its ability to construct long-range dependency. However, Transformer lacks the learning of local spatial information within samples and correlation information among samples, which limits its performance in practical tasks. To address these issues, this paper proposes a novel Transformer structure based on spatial information learning and correlation information learning (SCIL-based Transformer) for PolSAR image classification. Firstly, a global-local spatial information learning (GL-SIL) module is constructed to fully extract the global-local spatial information within samples. Secondly, a correlation information learning (CIL) module is designed to achieve the learning of correlation information among samples by modeling the dependency across samples. Compared to the self-attention network in Transformer, the above two modules not only add the learning of local spatial information within samples and the correlation information among samples, but also greatly reduce the network complexity. Experiments on two real PolSAR datasets have shown that the proposed SCIL-based Transformer has better performance than other state-of-the-art algorithms. © The Institution of Engineering & Technology 2023.

关键词： Contrastive Learning

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Research on Machine Vision Intelligent Detection System Based on Faster R-CNN 4

Research on Machine Vision Intelligent Detection System Base...

引用

4th IEEE International conference on Data Science and Computer Application, ICDSCA 2024

作者： Wang, Xueting Zeng, Fanshuo Song, Weizhao Zhang, Zhipeng He, Huitong Zhou, Zheyuan Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368239

With the rapid development of artificial intelligence technology, visual inspection and image processing algorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based on machine vision have been widely used in product quality control, defect detection and process monitoring. In this paper, a machine vision intelligent inspection system based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed, which aims to improve the detection accuracy and efficiency. In this paper, the overall architecture of the system is introduced, including the image acquisition module, the data preprocessing module and the detection module based on Faster R-CNN. In the data preprocessing module, the Deep Convolution Generative Adversarial Networks (DCGAN) algorithm was proposed for image expansion generation, and high-resolution image samples that were basically consistent with the collected image datasets were obtained. Then, the Faster R-CNN model was optimized and adjusted for specific application scenarios, so that the average accuracy (mAP) of the model reached 78.23% and the FPS value reached 11.0. ©2024 IEEE.

关键词： Faster R-CNN image processing image recognition Machine vision intelligent inspection system

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Realtime Scene Enhancement of Low Light image/Video 5

Realtime Scene Enhancement of Low Light Image/Video

引用

5th International conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Zeng, Huixia xiao, Ping School of Electronic and Information Engineering Guangzhou City University of Technology Guangdong Guangzhou510800 China

ISBN: (纸本)9798350368284

The quality of image and videos plays a vital role in case of real-Time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The processing ideas and networks of several mainstream low-light image enhancement neural network algorithms such as DRBN, DSLR, ZeroDCE, EnlightenGAN, as well as the key technologies used are compared and analyzed. In this paper, the EnglightenGAN neural network is used as the core image enhancement algorithm, and NVIDIA AGX Xavier equipment and video stream processing technology are used to achieve the entire process of video acquisition, video frame encoding, image enhancement processing, frame decoding, and video stream display. It can adapt to most low-light and dark scenes which has a good enhancement effect on video streams collected in low-light scenes. The real-Time performance and image enhancement effect of the system achieved good results, with FPS reaching 10, SSIM reaching 0.51753, and PSNR reaching 27.89973. © 2024 IEEE.

关键词： Video streaming

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SSPS 2024 - 2024 6th International Symposium on Signal processing systems

SSPS 2024 - 2024 6th International Symposium on Signal Proce...

引用

6th International Symposium on Signal processing systems, SSPS 2024

ISBN: (纸本)9798400716171

The proceedings contain 11 papers. The topics discussed include: a dynamic dictionary-based sparse reconstruction method for DOA estimation;method of weak communication signal detection and signal quality assessment at low SNR;Non-coherent fusion detection method for distributed MIMO radar based on modified ordered statistics;wavelet based multiscale deep learning algorithms for arctic sea ice melting prediction;deception detection system with joint cross-attention;a transformer-based method for the registration of terahertz security images with visible light images;multi-resolution convolutional neural network for specific emitter identification;and EDV-HOP: enhanced distance vector hop localization for wireless sensor network.

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Research on image Sharpness Evaluation in Microstructure processing

Research on Image Sharpness Evaluation in Microstructure Pro...

引用

2024 IEEE International conference on Manipulation, Manufacturing and Measurement on the Nanoscale, 3M-NANO 2024

作者： Zhang, Jiajun Wang, Zhe Li, Yiquan Xu, Jinkai Changchun University of Science and Technology Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing College of Mechanical and Electrical Engineering China Changchun University of Science and Technology School of Life Science and Technology China

ISBN: (纸本)9798350362107

To address issues of insufficient sensitivity and weak anti-noise characteristics in existing image sharpness evaluation algorithms, we propose a method combining local variance and gradient analysis. Traditional methods like the Tenengrad function use horizontal and vertical gradient values, while multi-directional gradients can increase noise sensitivity and false detections. Our approach integrates the Tenengrad function with gray variance processing, analyzing the deviation of each pixel's brightness from the average to distinguish content from background. Normalizing these values helps reduce background and impurity pixel influence. Preprocessing with an anisotropic diffusion filter smooths the image while preserving edges. Experimental results show this method offers higher sensitivity and better noise resistance than traditional functions, suitable for autofocus systems. © 2024 IEEE.

关键词： Anisotropy

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Vision Model Pre-training on Interleaved image-Text Data via Latent Compression Learning 38

Vision Model Pre-training on Interleaved Image-Text Data via...

引用

38th conference on Neural Information processing systems, NeurIPS 2024

作者： Yang, Chenyu Zhu, xizhou Zhu, Jinguo Su, Weijie Wang, Junjie Dong, Xuan Wang, Wenhai Lu, Lewei Li, Bin Zhou, Jie Qiao, Yu Dai, Jifeng Tsinghua University China OpenGVLab Shanghai AI Laboratory China SenseTime Research Hong Kong The Chinese University of Hong Kong Hong Kong University of Science and Technology of China China Xi'an Jiaotong University China Beijing University of Posts and Telecommunications China

Recently, vision model pre-training has evolved from relying on manually annotated datasets to leveraging large-scale, web-crawled image-text data. Despite these advances, there is no pre-training method that effectively exploits the interleaved image-text data, which is very prevalent on the Internet. Inspired by the recent success of compression learning in natural language processing, we propose a novel vision model pre-training method called Latent Compression Learning (LCL) for interleaved image-text data. This method performs latent compression learning by maximizing the mutual information between the inputs and outputs of a causal attention model. The training objective can be decomposed into two basic tasks: 1) contrastive learning between visual representation and preceding context, and 2) generating subsequent text based on visual representation. Our experiments demonstrate that our method not only matches the performance of CLIP on paired pre-training datasets (e.g., LAION), but can also leverage interleaved pre-training data (e.g., MMC4) to learn robust visual representations from scratch, showcasing the potential of vision model pre-training with interleaved image-text data. Code is released at https://***/OpenGVLab/LCL. © 2024 Neural information processing systems foundation. All rights reserved.

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An Automatic Data Cleaning Method for Radar Emitter Signal Recognition 16

An Automatic Data Cleaning Method for Radar Emitter Signal R...

引用

16th International conference on Signal processing systems, ICSPS 2024

作者： Jing, Zehuan Mu, Hao Li, Peng Yin, Xuefeng Zhang, Jiamiao Li, Jingyi Wang, Zhao School of Electronic Engineering Xidian University Xi’an710071 China National Key Laboratory of Electromagnetic Space Security Chengdu610036 China

ISBN: (纸本)9781510689251

With the rapid advancement of modern electronic warfare, the volume of signals intercepted by electronic reconnaissance equipment is increasing exponentially, yet the quality of these signals remains inconsistent. Low-quality signals hinder radar signal recognition tasks, particularly those employing deep learning algorithms. Despite this challenge, there has been limited research focused on cleaning radar emitter signal data to enhance the quality of radar signal datasets. To address this issue, we propose a data cleaning method that automatically detects and removes low-quality signal samples from radar emitter signal datasets. By analyzing the characteristics of the received radar signals, we extract resemblance coefficients from their envelopes, effectively distinguishing noise signals from pulse signals in the feature space. We then apply the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster different types of signals. This approach enables the automatic detection and removal of noise data from the dataset. Extensive experiments on both simulated and experimental data demonstrate that the proposed method significantly improves data quality and enhances the recognition rate of deep learning networks for emitter recognition. © 2025 SPIE.

关键词： Radar signal processing

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A study on advancing Edge Detection and Geometric Analysis through image processing 24

A study on advancing Edge Detection and Geometric Analysis t...

引用

8th International conference on Graphics and Signal processing

作者： Hamza, Syed Amir Jesser, Alexander Heilbronn Univ Heilbronn Germany Heilbronn Univ Professorship Embedded Syst & Commun Engn Heilbronn Germany

ISBN: (纸本)9798400717024

In the realm of Printed Circuit Board (PCB) manufacturing, the alignment process is pivotal for ensuring the functional integrity of the final product. Traditional image measurement techniques, while foundational, often fall short of achieving the high degree of accuracy and precision necessary for today's complex PCB designs. This research presents a novel approach that significantly enhances measurement accuracy through the application of optimized image processing techniques. By leveraging advanced algorithms within the OpenCV library, we introduce a methodology that accurately transforms pixel coordinates into real-world measurements, crucial for precise PCB alignment. Our technique employs edge detection algorithms such as Canny, Sobel, and Prewit filters, combined with a machine learning model that adapts to variations in real-time imaging conditions. The study delineates the development of a user-friendly graphical interface that streamlines the measurement process, making it accessible for practical industrial application. Results from experimental validations indicate a substantial improvement in measurement precision, with a demonstrable reduction in alignment errors compared to conventional methods. This leap forward not only promises to elevate the standards of PCB manufacturing but also opens avenues for similar advancements in other domains where image measurement is essential. The implications of this work are far-reaching, with the potential to significantly boost the efficiency and reliability of electronic manufacturing processes globally.

关键词： image measurement techniques microns edge detection algorithms real-world coordinate transformation machine learning in imaging graphical user interface (GUI) Printed Circuit Board (PCB)

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High-Fidelity Audio Compression with Improved RVQGAN 37

High-Fidelity Audio Compression with Improved RVQGAN

引用

37th conference on Neural Information processing systems (NeurIPS)

作者： Kumar, Rithesh Seetharaman, Prem Luebs, Alejandro Kumar, Ishaan Kumar, Kundan Descript Inc San Francisco CA 94118 USA

ISBN: (纸本)9781713899921

Language models have been successfully used to model natural signals, such as images, speech, and music. A key component of these models is a high quality neural compression model that can compress high-dimensional natural signals into lower dimensional discrete tokens. To that end, we introduce a high-fidelity universal neural audio compression algorithm that achieves 90x compression of 44.1 KHz audio into tokens at just 8kbps bandwidth. We achieve this by combining advances in high-fidelity audio generation with better vector quantization techniques from the image domain, along with improved adversarial and reconstruction losses. We compress all domains (speech, environment, music, etc.) with a single universal model, making it widely applicable to generative modeling of all audio. We compare with competing audio compression algorithms, and find our method outperforms them significantly. We provide thorough ablations for every design choice, as well as open-source code and trained model weights. We hope our work can lay the foundation for the next generation of high-fidelity audio modeling.

关键词： image enhancement

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