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Resampled importance sampled stochastic lightcuts 7

Resampled importance sampled stochastic lightcuts

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2022 7th international conference on Electronic technology and information Science, ICETIS 2022

作者： Chen, Jianwei Lin, Gang Li, Zhengze College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9783800757794

Scenes with many dynamic lights exist in various industries, and rendering which with ray tracing in real time remains a challenging problem. In this paper, we extend the real-time stochastic lightcuts method to achieve better performance. the key idea is to sample lights by resampling instead of traversing light tree. To make a good balance between rendering quality and performance when resampling, we also propose a metric to measure lighting contribution. Experiments show that our approach generates comparable effects to the real-time stochastic lightcuts method and achieves significant performance improvement at the cost of modest rendering quality loss. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Stochastic systems

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An Efficient Deep Transfer Learning Approach for Classification of Skin Cancer Images 7th

An Efficient Deep Transfer Learning Approach for Classificat...

引用

7th international conference on computer Vision and Image processing, CVIP 2022

作者： Naik, Prithviraj Purushottam Annappa, B. Dodia, Shubham Department of Computer Science and Engineering National Institute of Technology Surathkal Karnataka India

ISBN: (纸本)9783031314063

Prolonged exposure to the sun for an extended period can likely cause skin cancer, which is an abnormal proliferation of skin cells. the early detection of this illness necessitates the classification of der-matoscopic images, making it an enticing study problem. Deep learning is playing a crucial role in efficient dermoscopic analysis. Modified version of MobileNetV2 is proposed for the classification of skin cancer images in seven classes. the proposed deep learning model employs transfer learning and various data augmentation techniques to more accurately classify skin lesions compared to existing models. To improve the per¬formance of the classifier, data augmentation techniques are performed on "HAM10000" (Human Against Machine) dataset to classify seven dif¬ferent kinds of skin cancer. the proposed model obtained a training accuracy of 96.56% and testing accuracy of 93.11%. Also, it has a lower number of parameters in comparison to existing methods. the aim of the study is to aid dermatologists in the clinic to make more accurate diagnoses of skin lesions and in the early detection of skin cancer. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Automating Temperature Recording at Ferrocast Industry 7th

Automating Temperature Recording at Ferrocast Industry

引用

7th international conference on Advanced Computing and Intelligent Engineering, ICACIE 2022

作者： Goudar, Swetha Indudhar Mannasaheb, Furkhan Chatterjee, Aditya Naik, Saiesh Suchet, C. Department of Master of Computer Applications KLS Gogte Institute of Technology Karnataka Belagavi590008 India

ISBN: (纸本)9789819950140

Metal casting is a must for major household’s today. Optimization of the process in ferrocast industries in heating the raw materials to cast the mould is crucial and need of the hour. the right monitoring of the average casting temperature at the right context would be very much vital. Also, the electric power required is very huge for this process. the digitization of temperature recording for particular mix and alarming/alerting the team of workers about the attainment of threshold would enhance the process and save energy. this article proposes a process to automate the temperature recording and transfer the data to the desktop application. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Liquid metals

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Design and Implementation of Sorting System based on Machine Vision 7

Design and Implementation of Sorting System based on Machine...

引用

7th international conference on Big Data Analytics, ICBDA 2022

作者： Mao, Yanghong Chen, Chuixin Jiang, Huiyong Guangzhou Institute of Science and Technology Department of Computer Science and Engineering Guangzhou China Guangzhou Institute of Science and Technology Department of Intelligent Manufacturing and Electrical Engineering Guangzhou China Software Engineering Institute of Guangzhou Department of Software Engineering Guangzhou China

ISBN: (纸本)9781665479387

In order to achieve the goal that the manipulator can automatically obtain any position within its working range, and can adjust the end actuator for grasping, a machine vision sorting system is designed. the system passes through image processing, target recognition and positioning, and finally realizes the sorting effect through end control. the collected image information is processed by MATLAB. the image preprocessing adopts median filtering, image enhancement, binarization and edge detection. For the detection of workpiece surface defects, the improved segmentation network method of convolution neural network is adopted. In the algorithm of identifying workpiece type, the template matching method of log polar coordinate and Fourier transform is adopted. In the algorithm of workpiece position, the centroid of the workpiece is extracted as the positioning reference coordinate, combined with the three degree manipulator, the kinematics model of the manipulator is established. In the algorithm of workpiece grasping, the joint angle of each manipulator is obtained by inverse operation according to the position coordinates of the workpiece, and sent to STM32 through the serial port. then the STM32 completes the control command to grasp and place the workpiece and complete the sorting task. the experimental results show that the system meets the design requirements. © 2022 IEEE.

关键词： Template matching

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Research on Rapid Selection of University Funding Objects Based on Social Big Data Analysis 7th

Research on Rapid Selection of University Funding Objects Ba...

引用

7th EAI international conference on Advanced Hybrid information processing, ADHIP 2023

作者： Xu, Xiaoyan Zhang, Yuliang Data and Information Center Wuxi Vocational Institute of Commerce Wuxi214000 China

ISBN: (纸本)9783031505423

With the sharp increase in the number of college students, the number of students who need financial aid also *** to quickly and accurately select university funding objects has become the key to achieve the goal of funding education. therefore, this paper proposes a research on rapid selection methods of university funding objects based on social big data analysis. Based on the principles of systematicness, objectivity, scientificity and feasibility, we will build an index system for the selection of university funding objects, deeply mine the index data for the selection of university funding objects in the big data of social communications, build a pre-processing framework for the selection of index data, re sample the index data for the selection of university funding objects based on the SMOTE algorithm, and eliminate the adverse effects of unbalanced data. Set up a model for selecting university funding objects, formulate rules for selecting university funding objects, and realize rapid selection of university funding objects. the experimental results show that after the application of the proposed method, the corresponding maximum accuracy rate of the selection results of university funding objects is 98%, the maximum recall rate is 91%, and the maximum F value is 0.96, which fully confirms that the proposed method has better application performance. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Big data

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Semantic Metadata Integration Support Method for Editable Re-flowable Document OOXML and Fixed-layout Document PDF 23

Semantic Metadata Integration Support Method for Editable Re...

引用

7th international conference on computer Science and Application Engineering, CSAE 2023

作者： Wang, Yongxuan Li, Ning Tian, Yingai Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400700590

Document semantic annotation based on metadata lays the foundation for the automatic understanding and processing of document information. At present, most common documents can only support a small amount of preset metadata, and cannot support semantic metadata from different vocabularies. In addition, metadata cannot be shared among different types of documents. To solve this problem, this paper proposes a metadata support method for the mainstream editable document in OOXML format. this method satisfies that any semantic metadata can be edited along with editable documents, and the metadata in it can be converted into the fixed layout document in PDF format, thus realizing cross-document metadata support. In addition, this method does not need to change document format standards. thus documents with metadata can be supported by existing tools. the feasibility of the proposed method is verified by the implementation of the system. the result has a good application value for automatic understanding and intelligent processing of documents. © 2023 ACM.

关键词： Metadata

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Software Testing Model by Measuring the Level of Accuracy Fault Output Using Neural Network Algorithm 7

Software Testing Model by Measuring the Level of Accuracy Fa...

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7th IEEE international conference on information technology and Digital Applications, ICITDA 2022

作者： Zulkifli Gaol, Ford Lumban Trisetyarso, Agung Budiharto, Widodo Binus University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Binus University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

the testing stage is essential in software development because it determines the quality level, which is indicated by minimal errors. Meanwhile, the error that is discovered by the tester is called a fault. therefore, this research aims to create a Model-Based Testing (MBT) using the equivalence partitioning technique in the black-box method. the model is expected to log software errors such as Access, performance, initialization, a data structure or external database, incorrect or missing functions, to identify, categorize, and fix them methodically and appropriately. Based on the discoveries, the errors form a dataset that measures the accuracy of the fault output using a neural network algorithm. the result showed the formation of MBT can be developed using the equivalence partitioning technique by measuring the fault output accuracy level using a neural network algorithm, where the average accuracy rate is 80%. © 2022 IEEE.

关键词： Errors

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A Frame Work for the Tuberculosis Detection through CXR Using Deep Learning Networks 7

A Frame Work for the Tuberculosis Detection Through CXR Usin...

引用

7th international conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Sudhakar, S. Kolanchinathan, V.P. Babu, S. Sivasaravana Aakash, M. Arunkumar, B. Arunkumar, V.B. SSM Institute of Engineering and Technology Department of Computer Science and Engineering India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350372816

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), continues to be a serious worldwide health concern, killing about 1.3 million people in 2022, predominantly in low- and middle-income countries. the urgent need for automation in healthcare has spurred the development of supplementary tools. We have created a method for detecting TB in chest X-rays using advanced techniques such as pre-processing, picture segmentation, augmenting the data, along with Deep Learning model for classification. Our approach involves segmenting the lung region from X-rays using a U-Net model, followed by classification with a convolutional neural network (CNN). this optimized design enhances accuracy and efficiency, making it suitable for mobile deployment and providing a practical solution for TB detection in resource-limited settings. © 2024 IEEE.

关键词： Convolutional neural networks

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Terrain Image Classification Based on Vision Transformer Deep Learning Algorithm 7

Terrain Image Classification Based on Vision Transformer Dee...

引用

7th international conference on Algorithms, Computing and Artificial Intelligence, ACAI 2024

作者： Ren, Shiqi Zhang, Yu College of information engineering College of artificial intelligence Yangzhou university Jiangsu Yangzhou225000 China College of Computing Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9798331529314

Terrain image classification is an important research direction in the field of remote sensing and computer vision, aiming to realize automatic recognition and classification of different landform features through the analysis and processing of terrain images. In this paper, a deep learning algorithm based on Vision Transformer (ViT) is used to classify terrain images, and the performance of the algorithm in this task is systematically evaluated. In the process of model construction, we first imported the Vision Transformer model and made corresponding parameter Settings to ensure its adaptability and effectiveness. After training, it is observed that the loss function of the training set decreases from the initial value of 2.84 to 0.35, a decrease of 2.49, indicating that the model tends to converge in continuous optimization. At the same time, the accuracy is also significantly improved, from 55.9% to 86.8%, an increase of 30.9%, showing the enhancement of the model's learning ability. For the validation set, loss also decreased from 0.78 to 0.47, a decrease of 0.31, while accuracy increased from 60.1% to 83.5%, an increase of 23.4%. these results further prove the good performance of the model on different data sets and its convergence trend. In addition, through the evaluation of the test set, we get more specific performance indicators: the accuracy of the terrain image classification model based on Vision Transformer on the test set reaches 89.9%, the Precision is 0.9615, the Recall is 0.9494, and the F1-score is 0.9554. these indicators show that the model not only has high classification accuracy, but also performs well in generalization ability. To sum up, this research demonstrates the effectiveness of Vision Transformer deep learning algorithm in terrain image classification, and provides a new solution idea and method for related fields. through continuous optimization and adjustment, the algorithm is expected to achieve more extensive promotion in pract

关键词： Machine vision

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Adaptive Learning for Leather Image Pre-processing Using GAN 7th

Adaptive Learning for Leather Image Pre-processing Using GAN

引用

7th international conference on computer Vision and Image processing, CVIP 2022

作者： Varghese, Anjli Jawahar, Malathy Gupta, Prajjwal Prince, A. Amalin Birla Institute of Technology and Science Pilani K K Birla Goa Campus Zuarinagar Goa 403726 India Central Leather Research Institute Chennai600020 India

ISBN: (纸本)9783031314063

computer vision and image processing advancements contribute to an automated leather species identification technique. In the novel leather image data, hair pores are the key to distinguishing among species. However, non-pore regions append the uncertainty and complexity in determining the distinct pore patterns. Hence, this paper proposes a generative adversarial network (GAN) as an automatic leather image pre-processing tool. It is chosen to adaptively learn the leather-image-specific pore and non-pore regions in a supervised manner. It adversarially updates to synthesize an image with more plausible pore pixels. thus, the proposed method effectively pre-processed the leather images by removing the undesirable non-pore regions and enhancing the pore pixels. the experimental results with NIQE, entropy, SSIM, and PSNR objectively prove the effect of GAN-based leather image pre-processing. the adaptive and supervised learning with an adversarial update makes the proposed GAN unbounded. thus, unlike the traditional methods, the proposed method promotes effective leather image pre-processing. thereby facilitating further pore segmentation and classification. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Generative adversarial networks

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