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

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Implementation of dual-phase grating system phase contrast information extraction based on generative adversarial networks 4

Implementation of dual-phase grating system phase contrast i...

引用

4th International conference on Laser, Optics, and Optoelectronic Technology, LOPET 2024

作者： Zeng, Jiacheng Huang, Jianheng Zhu, Minghui Li, Jiaqi Lei, Yaohu Liu, xin Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781510681903

In the dual-phase grating system of X-ray phase-contrast imaging, the reduction in structural requirements for the absorption grating relaxes the constraints on its aspect ratio and period, thereby broadening the applicability of X-ray phase-contrast imaging techniques in a wider market. Traditionally, the extraction of phase-contrast information primarily relies on phase-stepping methods or Fourier transform algorithms, which often introduce artifacts and blurring into the images. To achieve higher quality image restoration, this study introduces generative adversarial networks (GANs) for high-quality image reconstruction. Our approach uses ideal images as labels and images containing object stripe information as inputs, utilizing GANs for feature learning to facilitate the transformation from object stripe images to high-quality phase-contrast images. The network also employs transfer learning to process previously unseen object stripe images and generate corresponding phase-contrast images. This technique not only significantly enhances image resolution but also substantially reduces artifacts and blurring in the image processing, paving the way for high-precision demands in medical diagnostics and industrial inspection. © 2024 SPIE.

关键词： image resolution

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Automatic Volume Control Using image processing and Deep Learning Techniques: A Review

Automatic Volume Control Using Image Processing and Deep Lea...

引用

Intelligent systems for Cybersecurity (ISCS), International conference on

作者： Kinshuk Theraja Sakshi Gupta Deepak Kumar Gupta Amity Institute of Information and Technology Amity University Noida

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

This paper presents an innovative approach to automatic volume control using image processing and deep learning techniques. The ability to automatically adjust volume levels based on environmental factors and user preferences has significant implications for various audio applications, including teleconferencing systems, smart devices, and public address systems. By combining image processing algorithms with deep learning models, this paper aims to develop a robust and adaptive volume control system capable of accurately adjusting audio levels in real-time. The paper discusses the theoretical foundations, technical implementation, experimental results, and potential applications of the proposed automatic volume control system.

关键词： Deep learning Visualization Teleconferencing Solid modeling image processing Software algorithms Process control

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I2P-Rec: Recognizing images on Large-scale Point Cloud Maps through Bird's Eye View Projections

I2P-Rec: Recognizing Images on Large-scale Point Cloud Maps ...

引用

IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Zheng, Shuhang Li, Yixuan Yu, Zhu Yu, Beinan Cao, Si-Yuan Wang, Minhang Xu, Jintao Ai, Rui Gu, Weihao Luo, Lun Shen, Hui-Liang Zhejiang Univ Coll Opt Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Ningbo Res Inst Ningbo 315100 Peoples R China Homo Technol Co Ltd Beijing 100192 Peoples R China

ISBN: (纸本)9781665491907

Place recognition is an important technique for autonomous cars to achieve full autonomy since it can provide an initial guess to online localization algorithms. Although current methods based on images or point clouds have achieved satisfactory performance, localizing the images on a large-scale point cloud map remains a fairly unexplored problem. This cross-modal matching task is challenging due to the difficulty in extracting consistent descriptors from images and point clouds. In this paper, we propose the I2P-Rec method to solve the problem by transforming the cross-modal data into the same modality. Specifically, we leverage on the recent success of depth estimation networks to recover point clouds from images. We then project the point clouds into Bird's Eye View (BEV) images. Using the BEV image as an intermediate representation, we extract global features with a Convolutional Neural Network followed by a NetVLAD layer to perform matching. The experimental results evaluated on the KITTI dataset show that, with only a small set of training data, I2P-Rec achieves recall rates at Top-1% over 80% and 90%, when localizing monocular and stereo images on point cloud maps, respectively. We further evaluate I2P-Rec on a 1 km trajectory dataset collected by an autonomous logistics car and show that I2P-Rec can generalize well to previously unseen environments.

关键词： Stereo image processing

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Chaos Theory Based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Signal to noise ratio

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UAV to Cadastral Parcel Boundary Translation and Synthetic UAV image Generation Using Conditional-Generative Adversarial Network 1

引用

2nd International conference on Unmanned Aerial systems in Geomatics (UASG)

作者： Khadanga, Ganesh Jain, Kamal Indian Inst Technol Roorkee Roorkee Uttarakhand India

ISBN: (数字)9783031193095

ISBN: (纸本)9783031193118;9783031193095;9783031193088

The precise boundaries of the cadastral parcels from the Unmanned Aerial Vehicle (UAV) data are essential for any eGovernance application. The pix2pix, image-to-image translation using the conditional Generative Adversarial Network (cGAN) models, has emerged as an alternative to the traditional machine learning and image processing algorithms. It has been used and demonstrated for productive purposes in different domains without any change in the pix2pix network model and loss functions. The pix2pix model is implemented in this research for extracting the cadastral parcel boundaries using the existing UAV data set, and the corresponding digitised data. The input data set is prepared using the python modules. The model is also used to predict the synthetic UAV data from the map data. The predicted boundary of the model is very useful. The proposed model can reduce the manual labour and human interventions in outlining the parcel boundary from UAV data.

关键词： Pix2Pix image-to-image translation c-GAN CNN UAV Deep learning Parcel

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image-Based Pothole Detection System Using YoloV8 Algorithm

Image-Based Pothole Detection System Using YoloV8 Algorithm

引用

2024 International conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Tripathy, Asutosh Rajalakshmi, T. Santhosh Kumar, R. Vasudevan Suryakala, S. SRM Institute of science and Technology College of Enginerring and Technology Department of Electronics and Communication Engineering Kattankulathur603203 India SRM Institute of Science & Technology College of Enginerring and Technology Dept of Electronics and Communication Engineering Kattankulathur603203 India

ISBN: (纸本)9798350354539

A pothole is a significant disadvantage in the roadways, resulting from depressions formed due to the improper quality of road materials and external conditions, such as extreme weather forces and the heavy vehicles passing over them. This leads to soil erosion beneath the road surface. Potholes are responsible for various damages including punctures, wheel damages, dents on vehicles, damage to the vehicle floor, collisions, and even severe accidents. The number of deaths and injuries stands at 1481 and 3103 respectively. Therefore, accurate and fast pothole detection is crucial for improving ITS (Intelligent Transport systems). Hence, this proposed study aims to overcome this major issue by implementing the YOLOv8 algorithm to detect the potholes present on the roads, which has the highest accuracy level of 98.18 percent, compared to existing algorithms. The study under consideration includes a comparison with the YOLOv5 algorithm, and the results demonstrate that the proposed approach outperforms the current method. © 2024 IEEE.

关键词： image processing

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Building surface damage recognition based on synthetic data 9

Building surface damage recognition based on synthetic data

引用

9th IEEE International conference on Information Technology and Nanotechnology, ITNT 2023

作者： Zherdeva, L.A. Minaev, E. Yu. Firsov, N.A. Samara National Research University Samara Russia Image Processing Systems Institute Samara Russia

ISBN: (纸本)9798350397338

To detect surface damage to buildings, it is necessary to involve workers who are at risk of industrial injuries when inspecting hard-to-reach areas of industrial premises. Attraction of special means, such as aerial platforms, safety systems, etc. increase the financial costs with this approach. The use of unmanned aerial vehicles, coupled with neural network algorithms, can simplify this procedure. Due to the inaccessibility, the problem of obtaining training data for neural networks arises, which can be solved by synthesizing them in a virtual environment. © 2023 IEEE.

关键词： Convolutional neural networks

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Survey on One-Shot Learning for image Recognition using Machine Learning and Deep Learning Techniques 5

Survey on One-Shot Learning for Image Recognition using Mach...

引用

5th International conference on Smart Electronics and Communication, ICOSEC 2024

作者： Kadam, Ganesh Vitthal Dubey, Ghanshyam Prasad Computer Science Amity School of Engineering & Technology Gwalior Maharajpura Madhya Pradesh Gwalior474005 India Amity School of Engineering & Technology ASET-CSE Maharajpura Madhya Pradesh Gwalior474005 India

ISBN: (纸本)9798331504403

Despite their success, the traditional deep neural networks are unable to rapidly generalize from a limited number of samples. To address the issue of large training data requirements with deep neural networks, one-shot learning restricting the number of data in each category is developed to attain enhanced results. Specifically, the traditional neural networks lead to over-fitting problems in training with a limited number of samples. In contrast, Machine Learning algorithms are capable of one-shot learning from limited samples like humans have the ability of oneshot learning. Hence, it is necessary to analyze one-shot object recognition techniques prevalent in image processing systems. This study will explore different one-shot learning techniques and methods from the state-of-the-art models and examine the merits and challenges of the approach with 25 literature papers. The effectiveness of the existing approaches will be evaluated by various techniques, datasets, and performance metrics are discussed in detail. At last, the paper discusses the potential future studies and challenges in achieving better detection accuracy. © 2024 IEEE.

关键词： Zero-shot learning

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Improving Multispectral image Classification with Convolutional Networks and Hyperspectral Band 4

Improving Multispectral Image Classification with Convolutio...

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4th International conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Nagarajan, S. Nagaraju, G. Chandra, K. Ramesh Sharmila, K. Soni Santhosh, K.N.S.K. Vanaja, Koppada Shri Vishnu Engineering College for Women Department of Cse Bhimavaram India Department of Ece Bhimavaram India Vishnu Institute of Technology Department of Ece Bhimavaram India Aditya University Department of Csm Surampalem India Dnr college of engineering and technology Department of Ece Bhimavaram India

ISBN: (纸本)9798331529635

This research work proposes an efficient framework to improve multispectral image classification through convolutional neural networks (CNNs) with optimized hyperspectral band selection. Hyperspectral images contain extensive spectral information, but the high dimensionality introduces redundant data, impacting processing efficiency and model performance. The proposed approach employs a hybrid band selection technique that combines mutual information and clustering algorithms to identify the most discriminative spectral bands before feeding them into a CNN model. This process enhances classification accuracy while significantly reducing computational load. Experimental results on the Indian Pines and Salinas hyperspectral datasets demonstrate the effectiveness of the proposed method. With our optimized band selection, the CNN achieved an overall classification accuracy of 95.7% on the Indian Pines dataset, an increase of 6.4 percentage points over the full-band CNN baseline accuracy of 89.3%, and reduced training time by 43%. Similarly, on the Salinas dataset, classification accuracy improved from 90.8% to 96.1%, with a 47% reduction in computation time. These results highlight the potential of hyperspectral band optimization in CNN-based classification, providing a robust and efficient approach for high-accuracy, real-time multispectral imaging applications. © 2024 IEEE.

关键词： Convolutional neural networks

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Moving Target Detection and Tracking Technology Based on Genetic Algorithm 12th

Moving Target Detection and Tracking Technology Based on Gen...

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12th International conference on Frontier Computing, FC 2022

作者： Fan, Dandan Liu, Tao Haojing College of Shaanxi University of Science and Technology Shaanxi Xi’an China

ISBN: (纸本)9789819914272

Detecting and tracking moving targets is a hot topic in current computer vision research. It has integrated cutting-edge science and technology in many fields such as image processing, pattern recognition, artificial intelligence, morphology, and automatic control technology. The use of fields such as visual navigation is also becoming more common. This paper aims to study the moving target detection and tracking technology based on genetic algorithm. On the basis of analyzing the image preprocessing technology, the classical moving target detection method and the operation steps of the genetic algorithm, the genetic algorithm is used as the best search strategy to obtain the target. In order to verify the tracking accuracy of the proposed algorithm, this paper selects 5 data sets and conducts experimental tests on them. The results show that all targets in test set 4 and test set 5 are moving, and the targets always move in the same direction, and the field of view Wide and unobstructed, the tracking accuracy is high. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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