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

共 15613 条记录，以下是521-530 订阅

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Deep Learning-Based image processing for Defect Detection in Electronic Components Manufacturing

Deep Learning-Based Image Processing for Defect Detection in...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Mathivanan, B. Maruthupandi, J. Lakshmi, Namala vijaya Suvitha, S. Karthik, K. Swaroop, Anand Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore641022 India New Horizon College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh 522017 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9798331543617

In electronic factors manufacturing, icing product quality through disfigurement discovery is pivotal for maintaining trustability and performance norms. This exploration investigates the operation of deep literacy ways in image processing for disfigurement discovery. Deep literacy algorithms, particularly convolutional neural networks(CNNs), have shown remarkable success in automated image analysis tasks. This study explores their effectiveness in relating blights similar to face abnormalities, cracks, or indecorous assembly in electronic factors. By using annotated datasets and advanced neural network infrastructures, the proposed approach aims to enhance delicacy and effectiveness in disfigurement discovery compared to traditional styles. The methodology involves preprocessing raw images, training CNN models on labeled data, and validating their performance through rigorous testing and evaluation. Results demonstrate the eventuality of deep literacy to revise disfigurement discovery processes in manufacturing, offering more dependable examinations that contribute to bettered product quality and reduced product costs. This exploration contributes precious perceptivity to the integration of AI technologies for enhancing quality control in artificial settings. © 2024 IEEE.

关键词： Smart manufacturing

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An Alternative Nonlinear Diffusion Algorithm for image Denoising and Deblurring 1

引用

4th International conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Fatone, Lorella Funaro, Daniele Università di Camerino Via Madonna delle Carceri 9 Camerino62032 Italy Università di Modena e Reggio Emilia Via Campi 103 Modena41125 Italy

ISBN: (数字)9783031812446

ISBN: (纸本)9783031812439

We discuss some applications in image restoration and enhancement, such as denoising and deblurring. The treatment involves a processing algorithm where an image is represented in a continuous frame and is manipulated through suitable time-dependent nonlinear Partial Differential Equations (PDEs). For this purpose, an ad-hoc space discretization scheme is implemented. As in the classic PDE-based image processing methods, the idea is to smooth out the regions of almost uniform shade and emphasize the discontinuities. The proposed methodology is quite efficient as satisfactory results are obtained after only one step. We present some numerical results by examining examples corrupted by average blur and/or salt and pepper noise. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Salt and pepper noise

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Multiple image Augmentations for Enhanced YOLO-based Traffic Sign Detection 2

Multiple Image Augmentations for Enhanced YOLO-based Traffic...

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2nd International conference on image processing, Computer vision and Machine Learning, ICICML 2023

作者： Long, Pinzhuang Hu, Yichen University of Nottingham Ningbo China Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Mechanical Manufacturing and Material Engineering NottinghamNG7 2RD United Kingdom

ISBN: (纸本)9798350331417

The rapid growth of the automotive industry necessitates the implementation of robust passenger safety measures, especially in the domain of traffic sign recognition for autonomous driving. This study introduces an effective approach to enhance traffic sign detection, with a specific emphasis on the You Only Look Once (YOLO) architecture. The paper addresses the challenges associated with accurately localizing traffic signs by employing diverse image augmentation techniques, including flipping, color inversion, Gaussian blur, affine transformation, and brightness adjustment. Despite computational challenges, particularly in light of YOLOv5's superior accuracy and efficiency, there is still room for further refinement to meet the stringent requirements of autonomous driving research. This research underscores the potential of image augmentation in advancing traffic sign recognition, demonstrating its pivotal role in the development of intelligent transportation systems. In comparison to existing algorithms as YOLO-V3 and YOLO-V5, the proposed technique might successfully reach higher accuracy and less computational power and processing time. © 2023 IEEE.

关键词： Autonomous driving Data augment Traffic signs detection YOLO architecture Introduction

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A personal retrospective on the development of the DLP and related spinoffs 16

A personal retrospective on the development of the DLP and r...

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conference on Emerging Digital Micromirror Device Based systems and Applications Xvi

作者： Cohn, Robert W. Univ Louisville Elect Res Inst Louisville KY 40209 USA Univ Louisville Nanotechnol Ctr Louisville KY 40209 USA

ISBN: (纸本)9781510670617;9781510670600

In 1985 I joined Texas Instruments' (TI's) Deformable Mirror Device ( DMD) group to develop applications of the cantilever device in coherent optical signal processing. At that time I witnessed the "aha discovery" that led to the invention of the DLP. It is interesting to consider the many years of effort that led Larry Hornbeck to this commercially successful implementation, not just the technology, but the efforts to sustain the project through sponsored R&D. While TI viewed the only sustainable market as (incoherent) display applications, the DMD group sustained the effort with DoD funding for coherent and incoherent optical signal processing systems, including matched filter correlators, digital optical switches, optical crossbar switches and related neural network processors. For coherent signal processing the need for a 2p phase-only (piston-motion pixel) spatial light modulator (SLM) was readily apparent to the sponsors. While TI saw little commercial justification for the phase-only device, this need inspired me around 1991 to develop a new class of real-time computer-generated holography algorithms referred to a pseudorandom encoding, in which each phase-only pixel is encoded with a desired magnitude and phase. The optical Fourier transforms of the modulation enabled my developments of multi-spot object targeting and laser tweezer systems. Around 2005 I began using Digital Light processing (DLP) developer kits in place of scanners to time-share images with a small number of detectors. One system using a single, high sensitivity detector together with well- chosen DLP frames quickly forms a "partial image" of a point-like scene objects - arguably, an early version of compressive sensing. This paper concludes with recommendations on optimizing the performance and applications of, and potential markets for TI's recently demonstrated phase-only DLP.

关键词： Deformable mirror device digital light processing optical information processing compressive sensing

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Refining Boundary Sample Space for Differentiable Rendering 16

Refining Boundary Sample Space for Differentiable Rendering

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2024 16th International conference on Graphics and image processing, ICGIP 2024

作者： Gao, Ruicheng Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Jingdezhen Research Institute Beihang University Jingdezhen333000 China Qingdao Research Institute Beihang University Qingdao266104 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The objective of physics-based differentiable rendering (PBDR) is to propagate gradients between scene parameters and the intensities of image pixels in a manner that is physically correct. The gradients obtained can be applied in optimization algorithms for the reconstruction of 3D geometry or materials, or they can be further propagated into neural network to learn neural representations of the scene. However, applying automatic differentiation techniques directly to the primary rendering process will result in biased gradients, as the rendering integral contains moving high-dimensional discontinuities. Based on how these discontinuities are managed-either implicitly or explicitly-existing PBDR methods can be categorized into two groups: reparameterization methods and boundary sampling methods. Boundary sampling methods need to construct paths that have one segment tangent to the geometry being differentiated in order to estimate a boundary integral to address the discontinuities explicitly. Such paths are usually constructed by sampling the tangent segment first and then extending it to complete the paths for subsequent processing. Fortunately, the number of dimensions in the space composed of such tangent segments is only three. In scenes comprised solely of triangle meshes, the first dimension is used to parameterize all the edges on the mesh, which determines a point on the tangent segment. The remaining two dimensions are used to parameterize the direction of the tangent segment. However, state-of-the-art boundary sampling methods parameterize the first dimension uniformly, which is inefficient because only a small portion of the edges contributes to the boundary integral, resulting in wasted parameter space. In this paper, we parameterize the first dimension by considering both edge length and contributions, thereby allocating more parameter space to important edges. Experiments demonstrate that our methods achieve lower variance gradients in the forward dif

关键词： Mesh generation

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Det-Recon-Reg: An Intelligent Framework Towards Automated Large-Scale Infrastructure Inspection

Det-Recon-Reg: An Intelligent Framework Towards Automated La...

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2024 International conference on Intelligent Robots and systems

作者： Yang, Guidong Zhang, Jihan Zhao, Benyun Gao, Chuanxiang Huang, Yijun Wen, Junjie Li, Qingxiang Tang, Jerry Chen, Xi Chen, Ben M. Chinese Univ Hong Kong CUHK Dept Mech & Automat Engn Hong Kong Peoples R China Univ Calif Berkeley Dept Mech Engn High Performance Robot Lab Berkeley CA 94720 USA

ISBN: (纸本)9798350377712;9798350377705

visual inspection plays a predominant role in inspecting infrastructure surface. However, the generalization of existing visual inspection systems to large-scale real-world scenes remains challenging. In this paper, we introduce Det-Recon-Reg, an intelligent framework separating the complex inspection procedure into three stages: Detect, Reconstruct, and Register. (1) For defect detection (Detect), we present the first high-resolution defect dataset tailored for large-scale defect detection. Based on the dataset, we evaluate the most effective real-time object detection algorithms and push the boundary by proposing CUBIT-Net for real-world defect inspection. (2) For infrastructure reconstruction (Reconstruct), we propose a learning-based multi-view stereo (MVS) network to adapt to large-scale scenes, taking as input the multi-view images and outputting the point cloud reconstruction, where its performance has been validated on the standard MVS datasets, including BlendedMVS, DTU, and Tanks and Temples datasets. (3) For defect localization (Register), we propose an effective registration method based on the geographic information system that registers the detected defects onto the reconstructed infrastructure model to establish a global reference for maintenance measures. The real-world experiments further verify the effectiveness and efficiency of our proposed framework. More details about our proposed dataset, code, and appendix are available on our project page: https://***/large-scale-inspect-framework/.

关键词： Stereo image processing

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Research on Infrared Touch Screen Multi-Touch Recognition Algorithm and Simulation 7

Research on Infrared Touch Screen Multi-Touch Recognition Al...

引用

7th International conference on Communication and Information systems, ICCIS 2023

作者： Ji, Zhangyuan Sun, Jingguo Li, Hewei Chang, Yuanpei Xue, Ying Zhang, Yu Peng, Yue Zuo, Jiancun School of Computer and Information Engineering Institute for Artifical Intelligence Shanghai Polytechnic University Shanghai China

ISBN: (纸本)9798350330540

With the continuous development of human computer interaction technology, large-size infrared touch screens have become an important interactive tool, and improving their recognition accuracy has become one of the important research directions. In order to understand the research status and trend of multi-touch recognition algorithm for large size infrared touch screen, based on extensive research of existing literature and latest achievements, two types of infrared multi-touch technologies based on different principles are first introduced, and then discusses three types of infrared multi-touch recognition algorithms: coordinate positioning algorithm, image processing algorithm and hybrid recognition algorithm. Through comparative analysis, it is found that the hybrid recognition algorithm has higher accuracy in multi-touch recognition, and the algorithm can be applied to large size infrared touch screen to improve the recognition accuracy. Finally, this paper summarizes the commonly used simulation platforms and functional and performance testing methods for infrared touch screens. This article provides a complete research framework of multi-touch recognition algorithms of infrared touch screens for future related research. © 2023 IEEE.

关键词： Algorithm simulation image processing Infrared touch screen Multi-touch recognition

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image Interpolation with Edges Preserving and Implementation on the Real ADAS Platform 5

Image Interpolation with Edges Preserving and Implementation...

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5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Kelava, Bozidar Vranjes, Mario Vranjes, Denis Lukac, Zeljko TTTechAuto CEE Osijek Croatia Fac Elect Engn Comp Sci & Informat Technol Osijek Croatia Fac Tech Sci Novi Sad Serbia

ISBN: (纸本)9781665462198

To save transmission, processing and memory resources in Advanced Driver Assistance systems (ADAS), it is often necessary to reduce the image resolution. Sometimes it is necessary to increase it after the transmission. Both resolution changes involve an image interpolation process. This paper describes implementation for three well-known interpolation methods, nearest neighbour interpolation (NN), bilinear interpolation (BL) and bicubic interpolation (BC), on a real automotive AMV ALPHA platform, using multiple processors on the same System on Chip (SoC). Implementation was done using C programming language and vision Software Development Kit (VSDK). Specific attention is given to the optimal distribution of tasks to the certain processor. The results have shown that, on the real automotive AMV ALPHA platform, BL interpolation achieves the best trade-off between the quality of interpolated image for the usage in automotive image-processing based algorithms and execution time, especially for the algorithms where the lower frame rate is acceptable (like surround-view, park assist, etc.).

关键词： image interpolation ADAS real-time

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Research on image Signal Enhancement and Denoising algorithms for Intelligent Communication systems

Research on Image Signal Enhancement and Denoising Algorithm...

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Information Technology, Communication Ecosystem and Management (ITCEM), International conference on

作者： Shenghan Luo Xinyan Wang Southeast University Nanjing China Xuzhou Pharmaceutical Vocational College Xuzhou China

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

The quality of image signals directly affects the performance of intelligent communication systems. This paper proposes a set of image enhancement and denoising algorithms to address image quality degradation in intelligent communication systems. For image enhancement, we designed an adaptive histogram equalization algorithm based on blocks and a contrast adaptive optimization method, and implemented a detail enhancement algorithm combining multi-scale edge detection and enhancement. In terms of image denoising, we proposed adaptive median filtering, improved soft-threshold wavelet domain denoising, and non-local means algorithms to effectively suppress various types of noise. The system is developed using a hybrid $\mathrm{C}++$ and Python framework with parallel processing achieved through multithreading technology. Experimental results show that the proposed algorithms significantly improve image quality, processing efficiency, and system stability, providing reliable image signal processing support for intelligent communication systems.

关键词： image quality Wavelet transforms Histograms Wavelet domain Communication systems Noise reduction Signal processing algorithms image enhancement Streams image denoising

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Fired:An image-Based Fire Detector for Smart Homes using Machine Learning algorithms

Fired:An Image-Based Fire Detector for Smart Homes using Mac...

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2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Srinath, G.M. Yashaswini, K.M. Sahana Shree, N. Supraja, B. Varshitha, V. Arfa Thareen, K. Visvesvaraya Technological University Department of Computer Science and Engineering Chickballapur India

ISBN: (纸本)9798350359688

Smart homes are to be protected from fire hazards which is a crucial safety concern. Existing ways of detecting fire is time consuming, hence causing maximum injuries and financial loss, so we have come up with an effective solution for detecting fire in smart homes using image processing and Machine Learning techniques. FireD extracts the features from photos and captured video frames using Convolutional Neural Network (CNN) and in turn sends them to Neural Network which implements clustering algorithm known as Yolov5 to ensure the ability to classify images as Fire and Non-Fire. Once the fire is detected, model will send the images to Heroku cloud which serves as container based cloud Platform as a Service (PaaS) to access, deploy and manage the images. On the detection of fire Notification is sent to Telegram through telegram bot. Overall performance data from model indicates that FireD is likely to perform well in real-world scenarios. Therefore, FireD is more reliable than other existing systems since FireD application is pushed to the docker container with all the required libraries and files. The application can be accessed and implemented easily. © 2024 IEEE.

关键词： Platform as a Service (PaaS)

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