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International conference on Computing and Networking Technology (ICCNT)

作者： G Kalyan Kumar Anita Titus R.S. Vijayashanthi M Shalini M Dinesh Department of CSE Rajalakshmi Institute of Technology Chennai Department of ECE Jeppiaar University Chennai Department of ECE S.A. Engineering College Chennai Department of CSE St. Joseph’s College of Engineering Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Histogram equalization is a method of contrast adjustment in image processing using the image’s histogram. However, as modern imaging systems become more complex, these traditional algorithms for histogram equalization are no longer efficient. In response to this problem, researchers have studied several strategies for improving the performance of histogram equalization in digital images. An option is to use parallel processing and multi-threading approaches to distribute the computational burden, thereby speeding up the execution of histogram equalization. Another methodology includes using machine learning algorithms to adapt histogram equalization parameters according to the input image. Furthermore, using advanced hardware architectures like Field Programmable Gate Arrays (FPGA), Graphic processing Units (GPU), or Application Specific Integrated Circuits can significantly enhance the speed and efficiency of a Histogram Equalization. The performance optimization techniques have provided encouraging results, which significantly refine image processing time and visual perception. Modern imaging systems may benefit tremendously from their use in the new age.

关键词： Histograms Machine learning algorithms Digital images Surveillance Graphics processing units Parallel processing Real-time systems Optimization Field programmable gate arrays Visual perception

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A Modern C++ Point of View of Programming in image processing 2022

A Modern C++ Point of View of Programming in Image Processin...

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21st ACM SIGPLAN International conference on Generative Programming: Concepts and Experiences, GPCE 2022, co-located with ACM SIGPLAN conference on systems, Programming, Languages, and Applications, SPLASH 2022

作者： Roynard, Michaël Carlinet, Edwin Géraud, Thierry EPITA Research Laboratory Le Kremlin-Bicetre France

ISBN: (纸本)9781450399203

C++ is a multi-paradigm language that enables the programmer to set up efficient image processing algorithms easily. This language strength comes from many aspects. C++ is high-level, so this enables developing powerful abstractions and mixing different programming styles to ease the development. At the same time, C++ is low-level and can fully take advantage of the hardware to deliver the best performance. It is also very portable and highly compatible which allows algorithms to be called from high-level, fast-prototyping languages such as Python or Matlab. One fundamental aspects where C++ shines is generic programming. Generic programming makes it possible to develop and reuse bricks of software on objects (images) of different natures (types) without performance loss. Nevertheless, conciliating genericity, efficiency, and simplicity at the same time is not trivial. Modern C++ (post-2011) has brought new features that made it simpler and more powerful. In this paper, we focus on some C++20 aspects of generic programming: ranges, views, and concepts, and see how they extend to images to ease the development of generic image algorithms while lowering the computation time. © 2022 ACM.

关键词： MATLAB

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Low-Illumination Lane Detection by Fusion of Multi-light Information

Low-Illumination Lane Detection by Fusion of Multi-light Inf...

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IEEE International conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Zhao, Wenbo Tian, Wei Han, Yi Yu, Xianwang Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China

ISBN: (数字)9781665460262

ISBN: (纸本)9781665460262

Lane detection based on the visual sensor is of great significance for the environmental perception of the intelligent vehicle. Current mature lane detection algorithms are trained and implemented in good visual conditions. However, the low-light environment such as in the night is much more complex, easily causing misdetections and even perception failures, which are harmful to the downstream tasks such as behavior decision and control of ego-vehicle. To tackle this problem, we propose a new lane detection algorithm that introduces the multi-light information into lane detection task. The proposed algorithm adopts a multi-exposure image processing module, which generates and fuses multi-exposure information from the source image data. By integrating this module, mainstream lane detection models can jointly learn the extraction of lane features as well as the enhancement of low-exposed image, thus improving both the performance and robustness of lane detection in the night.

关键词： lane detection image enhancement multi-exposure fusion

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Integrating the dynamic optimization strategies of Adam and AdaGrad

Integrating the dynamic optimization strategies of Adam and ...

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International conference on Intelligent Control and Information processing (ICICIP)

作者： Zhixuan Huang Xiaonan Luo Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the rise of big data and deep learning, AI has advanced in applications like recommendation systems, image recognition, and machine translation, primarily through optimizing loss functions in deep neural networks to improve accuracy and reduce training *** descent is the core optimization method but faces challenges like slow convergence and local minima. To overcome these, algorithms like Momentum, AdaGrad, RMSProp, Adadelta, Adam, and Nadam have been developed, introducing momentum and adaptive learning rates to accelerate convergence. This paper presents a new optimization algorithm that combines the strengths of Adam and AdaGrad, offering better adaptability to different learning rates.

关键词： Training Heuristic algorithms Optimization methods Machine translation Logic Artificial intelligence Robots Recommender systems Convergence Intelligent control

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image processing-Based Presentation Control System Using Binary Logic Technique 1st

Image Processing-Based Presentation Control System Using Bin...

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1st International conference on Expert Clouds and Applications, ICOECA 2023

作者： Chinchmalatpure, Sheela Ingale, Harshal Jadhao, Rushikesh Ghule, Ojasvi Ingole, Madhura Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9789819917440

Providing a perfect presentation becomes a challenging job because of various factors like changing the slides and the correct keys to be used to change the slides while maintaining composure in front of the audience. There are a lot of interruptions during a presentation when using the keyboard for controlling the slides. Therefore, this paper proposes a smart presentation system using hand gestures which provides an easy way to change or operate the slides. The proposed system uses various algorithms to detect different hand gestures to perform several operations. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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Adaptive Rate Block Compressive Sensing Based on Algorithmic Reconstruction Error

Adaptive Rate Block Compressive Sensing Based on Algorithmic...

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International conference on Vision, image and Signal processing (ICVISP)

作者： Chonghuan Song Jianhua Chen School of Information Science and Engineering Yunnan University Kunming Yunnan China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Compressive sensing (CS) for a full-sized image requires a large sensing matrix, consuming significant storage space and computational resources. Block-based CS addresses this but allocates the same number of samples to each block, ignoring the sparsity differences among various blocks. Additionally, the original image's sparsity is unknown in practical systems. Therefore, it is necessary to accurately estimate the sparsity from some initial sampling information. This paper proposes an adaptive rate block CS model based on the difference of the results of two reconstruction algorithms. The image is initially sampled at the sampling end. Then a high-accuracy algorithm and a low-accuracy algorithm are used to reconstruct initial sampled signals respectively. The sparsity of each image block is estimated by the difference between the two initial reconstructed signals, allowing the appropriate allocation of the number of samples for a block. The main computations of this scheme are concentrated at the reconstruction end, which can effectively save resources at the sampling end. Experimental results show that the proposed scheme can effectively improve reconstruction quality while reducing the sampling rate.

关键词： Surveillance Measurement uncertainty Signal processing algorithms Reconstruction algorithms Signal processing Sensors Resource management image reconstruction Compressed sensing Videos

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Reliability Evaluation of LU Decomposition on GPU-Accelerated System-on-Chip Under Proton Irradiation

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2022年第7期69卷 1467-1474页

作者： Badia, Jose M. Leon, German Belloch, Jose A. Lindoso, Almudena Garcia-Valderas, Mario Morilla, Yolanda Entrena, Luis Univ Jaume 1 Dept Ingn & Ciencia Comp Castellon De La Plana 12071 Castellon Spain Univ Carlos III Madrid Dept Tecnol Elect Madrid 28911 Spain CSIC Ctr Nacl Aceleradores CNA JA Seville 41092 Spain

Graphic processing units (GPUs) have become a basic accelerator both in high-performance nodes and low-power system-on-chip (SoC). They provide massive data parallelism and very high performance per watt. However, their reliability in harsh environments is an important issue to take into account, especially for safety-critical applications. In this article, we evaluate the influence of the parallelization strategy on the reliability of lower-upper (LU) decomposition on a GPU-accelerated SoC under proton irradiation. Specifically, we compare a memory bound and a compute bound implementation of the decomposition on a K20A GPU embedded on a Tegra K1 (TK1) SoC. We leverage the GPU and CPU clock frequencies both to highlight the radiation sensitivity of the GPU where we are running the benchmark and also to apply both algorithms to solve problems with the same size when exposed to the same radiation dose. Results show that more intensive use of the resources of the GPU increases the cross section. We also observed that most of the radiation-induced errors hang the operating system and even the rebooting process. Finally, we present a preliminary study of the error propagation of the LU decomposition algorithms.

关键词： Graphics processing units Protons Sensitivity System-on-chip Performance evaluation Benchmark testing Kernel Fault tolerance graphic processing unit (GPU) lower-upper (LU) decomposition proton irradiation system-on-chip (SoC)

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Classification and detection of skin diseases based on CNN-powered image segmentation 3

Classification and detection of skin diseases based on CNN-p...

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3rd IEEE International conference on Intelligent Technologies, CONIT 2023

作者： Vasudeva Reddy, T. Anirudh Reddy, R. Bhanu Teja, C.S. Sai Charan Reddy, N. Chandra Sekhar Rao, N. Hima B V Raju Institute of Technology Department of ECE Telangana India

ISBN: (纸本)9798350338607

Skin diseases have become more prevalent in recent years, posing a significant burden on healthcare systems globally. These conditions can range from noncancerous problems like acne, eczema, and vitiligo to more severe diseases such as melanoma, basal and squamous cell carcinoma. Skin ailments not only have an impact on physical health but can also affect the patient's psychological well-being, especially when the face is disfigured or damaged. Early detection and diagnosis of dermatological conditions can significantly improve survival rates as their severity can worsen over time. This article covers various methods used to identify and diagnose skin diseases, including natural diagnostic techniques and image pre-processing mechanisms and classification algorithms used globally. By understanding these techniques, healthcare professionals can detect skin diseases accurately, leading to better patient outcomes. Advancements in technology and research in this field continue to progress, providing hope to those who suffer from skin diseases. © 2023 IEEE.

关键词： Python

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Deep Learning-Based Depth Map Defect Removal for Industrial Applications 10

Deep Learning-Based Depth Map Defect Removal for Industrial ...

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conference on Optical Metrology and Inspection for Industrial Applications X

作者： Voronin, V. Gapon, N. Zhdanova, M. Tokareva, O. Khamidullin, I. Semenishchev, E. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Rostov Na Donu Russia

ISBN: (纸本)9781510667877;9781510667884

A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.

关键词： depth map defect detection image reconstruction manufacturing deep learning U-Net

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Recognition and Analysis of Glass Subsurface Defects by Integrating Machine Vision and image processing Technology

Recognition and Analysis of Glass Subsurface Defects by Inte...

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Computers, Information processing and Advanced Education (CIPAE), 2020 International conference on

作者： Chen Lin Xiong Ling Guangming Luo Wuhan University of Science and Technology Wuhan China Changli Glass Honghu Co. Ltd Wuhan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Machine vision and computer image processing technologies are widely used in the metallurgical industry, especially in recognizing and analyzing defects in glass. High surfaces of planer surface and quality in the glass and metal manufacturing sector primarily require automated and performance visual detection, and inspection systems and algorithms are constantly improving. This article, therefore, seeks to comprehensively recognize and analyze glass subsurface defects through the integration of image processing technologies and machine vision. According to the algorithm's properties and the image features typified in the process, the defects of identified glass components are primarily classified based on determining the cause and the optical features of characteristics exhibited. Given the nature of varied and detected effects determined mainly by the classifiers, an improved accuracy level was notable, and a reduction of the computational complexity was evident following the use of machine vision and image processing technologies.

关键词： Visualization image recognition Machine vision Metals Glass Optical imaging Classification algorithms Manufacturing Surface treatment Optical devices

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