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2022 International Conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2022

作者： Yuan, Haidi Anhui Sanlian College Basic Experiment Teaching Center Anhui Hefei China

ISBN: (数字)9781665490092

ISBN: (纸本)9781665490092

With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the most important research contents of computer vision. It combines many advanced technologies in the field of computing, such as image processing, pattern recognition, automatic control and artificial intelligence, and is widely used in intelligent surveillance. In various fields such as traffic control, machine intelligence and medical diagnosis, visual effects are obtained through image or image processing. Record videos from the computer and perform specific mechanical tasks. In terms of intelligent tracking, as the demand for applications in various complex environments continues to grow, how to improve the robustness and accuracy of moving target tracking and tracking algorithms has become the focus of ongoing target tracking research. This paper studies the image target detection algorithm based on computer vision technology. Firstly, the literature research method is used to summarize the existing problems of image target detection based on computer vision technology and the existing algorithms. The experiment is used to analyze the image target based on computer vision technology. The detection algorithm is verified, and the error rate of image target detection of the algorithm proposed in this paper is compared. According to the experimental results, it can be seen from Figure 1 that in experiment 1, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The maximum false detection rate is only 2.3%, and the other algorithms have 5.4%-11.1% false detection rate The GMM-STMRF algorithm increases the multi-frame calculation in the time dimension, so the calculation time has increased. Algorithms such as GMM and MeanShift need to estimate the multi-frame parameters, an

关键词： Target tracking

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A Survey on Data Augmentation Techniques 7

A Survey on Data Augmentation Techniques

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Nanthini, K. Sivabalaselvamani, D. Chitra, K. Gokul, P. KavinKumar, S. Kishore, S. Department of Computer Applications Kongu Engineering College Perundurai Tamil Nadu Erode India

ISBN: (纸本)9781665464086

Data augmentation is a technique used to generate new data sets from existing ones and artificially increase the size of a dataset. By providing the model with more training data, it is possible to enhance the performance of the proposed model. Various methods, such as image rotating, cropping, and flipping, and adding noise to audio signals can be used to enhance the data. When the original dataset is limited or unbalanced, the use of data augmentation can result in better generalization and increased performance for unknown data. This also reduces the over-fitting by increasing the number of training data included in the machine learning model. However, it is crucial to select the data augmentation methods that are used to be aware of how these methods will affect the models' performance on actual data. To deal with data scarcity and lack of diversity, computer vision and natural language processing (NLP) models employ data augmentation strategies. Accuracy of machine learning models can be improved further by employing AI/ML approaches like data augmentation. According to an experiment, deep learning model performs better by reducing the training loss and validation loss than a model without augmentation for image classification. This study has briefly discussed about the data augmentation techniques that are used to process the image, text and signal data by increasing the volume and variety of training data and set it as the primary goal for a machine learning model to perform better when it comes to generalization. By employing these techniques, over-fitting can be eliminated and the robustness and accuracy of the model can also be improved. The main objective of this study is to implement data augmentation as a solution for the problem of data scarcity. Data augmentation refers to a group of methods that are used to improve the amount and quality of training datasets so that more effective machine learning models may be constructed to process the data. The ap

关键词： Deep learning

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Generative Visual Compression: A Review

Generative Visual Compression: A Review

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IEEE International Conference on image processing

作者： Bolin Chen Shanzhi Yin Peilin Chen Shiqi Wang Yan Ye City University of Hong Kong Alibaba Group

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and diverse functionalities over traditional codecs. This paper provides a thorough review on the recent advances of generative visual compression, illustrating great potentials and promising applications in ultra-low bitrate communication, user-specified reconstruction/filtering, and intelligent machine analysis. In particular, we review the visual data compression methodologies with deep generative models, and summarize how compact representation and high-quality reconstruction could be actualized via generative techniques. In addition, we generalize related generative compression technologies for machine vision with different-domain analysis. Finally, we discuss the fundamental challenges on generative visual compression techniques and envision their future research directions.

关键词： Visualization image coding Reviews machine vision Data compression Performance gain Data models

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Systematic review of image segmentation using complex networks

arXiv

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arXiv 2024年

作者： Rezaei, Amin Asadi, Fateme Yazd Iran Yazd University Fars Iran

This review presents various image segmentation methods using complex networks. image segmentation is one of the important steps in image analysis as it helps analyze and understand complex images. At first, it has been tried to classify complex networks based on how it being used in image segmentation. In computer vision and image processing applications, image segmentation is essential for analyzing complex images with irregular shapes, textures, or overlapping boundaries. Advanced algorithms make use of machine learning, clustering, edge detection, and region-growing techniques. Graph theory principles combined with community detection-based methods allow for more precise analysis and interpretation of complex images. Hybrid approaches combine multiple techniques for comprehensive, robust segmentation, improving results in computer vision and image processing tasks. © 2024, CC BY.

关键词： Complex networks

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Design of a Lightweight Convolutional Neural Network Accelerated by FPGA

Design of a Lightweight Convolutional Neural Network Acceler...

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Algorithm, image processing and machine vision (AIPMV), International Conference on

作者： Yingjun Xu Kun Wang Shanxi University College of Physics and Electronic Engineering Taiyuan China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

image classification has widespread applications in various fields such as medical diagnosis, autonomous driving, security surveillance, and manufacturing. The existing architecture has a complex computation workload, leading to increased latency. This paper designs and implements a convolutional neural network architecture accelerated by FPGA, quantizing the parameters to int14 fixed-point integers, achieving an effective recognition rate of 98.2 % for handwritten digits. By reducing the number of parameters and arithmetic operations, the difficulty of deploying this CNN architecture on hardware devices is lowered, it also reduces the usage of storage resources, the combination of parallel computation of convolutions with pipelining operations improves the real-time performance of the overall architecture. Utilizing the resources on the ZYNQ-7020 FPGA development board to build the CNN architecture, achieving a prediction time of 86.02 μs for a single image under a clock frequency of 50 MHz, compared to existing architectures, it has significant advantages in terms of real-time performance.

关键词： Handwriting recognition Time-frequency analysis image coding Surveillance Computer architecture Real-time systems Convolutional neural networks Security Field programmable gate arrays Pipeline processing

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Automatic Sown Field Detection Using machine vision and Contour Analysis 21st

Automatic Sown Field Detection Using Machine Vision and Cont...

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21st International Conference on Computational Science and Its applications (ICCSA)

作者： Shirobokov, Mikhail Grishkin, Valeriy Kayumova, Diana St Petersburg State Univ Univ Embankment 7-9 St Petersburg 199034 Russia

ISBN: (纸本)9783030869601;9783030869595

The paper proposes a prototype of an algorithm based on the use of machine vision methods, which allows automatic identification and selection of fields sown with agricultural crops on images. The algorithm works with satellite images and consists of two stages. At the first stage, the image undergoes initial processing, after which edge detection and contour finding algorithms are applied to it. At the second stage, the obtained image areas enclosed within the contours are represented as a set of numerical and logical parameters which are used for filtering and classification of the areas.

关键词： image processing machine vision Contour analysis

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vision-based size classification of iron ore pellets using ensembled convolutional neural network

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NEURAL COMPUTING & applications 2022年第21期34卷 18629-18641页

作者： Deo, Arya Jyoti Sahoo, Animesh Behera, Santosh Kumar Das, Debi Prasad CSIR Inst Minerals & Mat Technol Proc Engn & Instrumentat Dept Bhubaneswar 751013 India Acad Sci & Innovat Res AcSIR Ghaziabad 201002 India Birla Inst Technol & Sci KK Birla Goa Campus Sancoale 403726 Goa India

In an iron ore pelletization plant, pellets are produced inside a rotating disc pelletizer. Online pellet size distribution is an important performance indicator of the pelletization process. image processing-based system is an effective solution for online size analysis of iron ore pellets. This paper proposes a machine learning algorithm for estimating the size class of the pellets during their production by imaging from an area inside the disc pelletizer. Instead of computing the size of each individual pellets in the acquired image, this method proposes a qualitative approach to get the overall size estimate of the pellets in production. The key idea of this paper is to find out whether the disc is producing VERY SMALL, SMALL, MEDIUM, or BIG-sized pellets. A weighted average ensemble of different convolutional neural networks such as VGG16, Mobilenet, and Resnet50 is used to achieve this objective. Furthermore, batch normalization is applied to improve the estimation performance of the proposed model. A novel data augmentation method is applied to the in situ captured images to create the data set used to train and evaluate the proposed ensemble of CNN models. Results of experiments indicate that it is possible to detect the operating state of the pelletization disc by acquiring images from the inside area of the disc with sufficient accuracy.

关键词： Convolutional neural network Deep learning image classification Iron ore pellet Size estimation

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Highly Sensitive Real-Time Detection of Surface Texture Changes 17

Highly Sensitive Real-Time Detection of Surface Texture Chan...

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17th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET 2024

作者： Yarmilko, Andrii Rozlomii, Inna Mysiura, Yuliia Bohdan Khmelnytsky National University of Cherkasy Department of Automated Systems Software Cherkasy Ukraine Cherkasy State Technological University Department of Information Security and Computer Engineering Cherkasy Ukraine Bohdan Khmelnytsky National University of Cherkasy Department of Information Technologies Cherkasy Ukraine

ISBN: (纸本)9798331520564

Real-time surface analysis is becoming increasingly important in various fields, including industry, medicine, and transportation. This article examines the problem of remote monitoring of surface texture in real time. The authors review the methods and techniques used for this task, focusing on high-precision technologies such as computer vision, laser scanning, and image processing techniques. The paper examines the importance of this real-time approach to support accurate and rapid decisions in industrial processes and medical diagnostics. The authors propose a method for detecting texture artifacts with machine learning and analyze the performance and quality of possible variations of its application. Based on the discussion, the authors draw conclusions regarding the effectiveness and prospects of the proposed approach in real-time remote monitoring of surface textures. They emphasize the importance of further research in this direction and the use of innovative technologies to improve the accuracy and speed of the monitoring process. This article will be a valuable source of information for scientists, engineers and professionals in the field of industrial control and automation. © 2024 IEEE.

关键词： Laser applications

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Benefits of Layered Software Architecture in machine Learning applications

Benefits of Layered Software Architecture in Machine Learnin...

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International Conference on image processing and vision Engineering (IMPROVE)

作者： Romhanyi, Armin Vamossy, Zoltan Obuda Univ John von Neumann Fac Informat Becsi St Budapest Hungary

ISBN: (纸本)9789897585111

The benefits of layering in software applications are well-known not only to authors and industry experts, but to software enthusiasts as well because the layering provides a testable and more error-proof framing for applications. Despite the benefits, however, the increasingly popular area of machine learning is yet to embrace the advantages of such a design. In the present paper, we aim to investigate if characteristic benefits of layered architecture can be applied to machine learning by designing and building a system that uses a layered machine learning approach. Then, the implemented system is compared to other already existing implementations in the literature targeting the field of facial recognition. Although we chose this field as our example for its literature being rich in both theoretical foundations and practical implementations, the principles and practices outlined by the present work are also applicable in a more general sense.

关键词： machine Learning Layered Architecture Software Engineering Facial Authentication Facial Landmark Points

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Deep learning as a tool for ecology and evolution

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METHODS IN ECOLOGY AND EVOLUTION 2022年第8期13卷 1640-1660页

作者： Borowiec, Marek L. Dikow, Rebecca B. Frandsen, Paul B. McKeeken, Alexander Valentini, Gabriele White, Alexander E. Univ Idaho Entomol Plant Pathol & Nematol Moscow ID 83843 USA Univ Idaho Inst Bioinformat & Evolutionary Studies IBEST Moscow ID 83843 USA Smithsonian Inst Data Sci Lab Off Chief Informat Officer Washington DC USA Brigham Young Univ Dept Plant & Wildlife Sci Provo UT USA Arizona State Univ Sch Life Sci Tempe AZ USA Smithsonian Inst Dept Bot Natl Museum Nat Hist Washington DC 20560 USA

Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modelling and excels at recognizing complex patterns. In this review we synthesize 818 studies using deep learning in the context of ecology and evolution to give a discipline-wide perspective necessary to promote a rethinking of inference approaches in the field. We provide an introduction to machine learning and contrast it with mechanistic inference, followed by a gentle primer on deep learning. We review the applications of deep learning in ecology and evolution and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications. We find that deep learning is being rapidly adopted in ecology and evolution, with 589 studies (64%) published since the beginning of 2019. Most use convolutional neural networks (496 studies) and supervised learning for image identification but also for tasks using molecular data, sounds, environmental data or video as input. More sophisticated uses of deep learning in biology are also beginning to appear. Operating within the machine learning paradigm, deep learning can be viewed as an alternative to mechanistic modelling. It has desirable properties of good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. We expect that rapid adoption of deep learning in ecology and evolution will continue, especially in automation of biodiversity monitori

关键词： artificial intelligence automation computer vision machine learning modelling neural networks statistics

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