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A Survey on Data Augmentation Techniques 7

A Survey on Data Augmentation Techniques

引用

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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MED-GPVS: A Deep Learning-Based Joint Biomedical image Classification and visual Question Answering System for Precision e-Health

MED-GPVS: A Deep Learning-Based Joint Biomedical Image Class...

引用

IEEE International conference on Communications (ICC)

作者： Haridas, Harishma T. Fouda, Mostafa M. Fadlullah, Zubair Md Mahmoud, Mohamed ElHalawany, Basem M. Guizani, Mohsen Lakehead Univ Dept Comp Sci Thunder Bay ON Canada Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Thunder Bay Reg Hlth Res Inst TBRHRI Thunder Bay ON Canada Tennessee Technol Univ Dept Elect & Comp Engn Cookeville TN USA Benha Univ Fac Engn Shoubra Banha Egypt Mohamed Bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

General Purpose vision System (GPVS) is a task-agnostic vision-language system that inputs an image and a question from which the system recognizes the tasks to be performed and outputs bounding boxes, confidence scores, and text outputs to answer the question. While much attention to GPVS has been recently given in the computer vision field, its medical field applications are still in their infancy. This paper presents MED-GPVS, a customized deep learning-based GPVS on biomedical images to perform various vision tasks, such as object detection and visual question answering, on medical images to facilitate precision medicine/e-health services. Our envisioned MED-GPVS takes an image and a natural language text as inputs, and then outputs bounding boxes, confidence scores, and generates a caption (i.e., the answer to the posed query). For example, if a medical image of a patient's abdomen is presented to MED-GPVS followed by the question: "does the picture contain stomach?", MED-GPVS should ideally provide the answer "yes" along with a prediction box and prediction score on the image. We utilize the multilingual SLAKE dataset, which was annotated by expert physicians with a full semantic label, to validate the performance of MED-GPVS under various scenarios involving different biomedical image-based diagnoses. For the visual question answering (VQA) task, MED-GPVS demonstrates encouraging performance with significantly high accuracy of 82.41%.

关键词： e-health precision medicine General Purpose vision System (GPVS) DEtection TRansformer (DETR) vision-and-Language Bidirectional Encoder Representations from Transformers (viLBERT) object detection Natural Language processing (NLP)

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Application of Computer 3D image vision Algorithm in Intelligent image Recognition System

Application of Computer 3D Image Vision Algorithm in Intelli...

引用

IEEE International conference on Artificial Intelligence and Computer applications (ICAICA)

作者： Yuan Li Xin Yu Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan Shandong China

In this paper, the 3D space imaging model of machine vision is constructed. Starting from the traditional machine vision image processing algorithm flow, the image denoising process and target tracking process are optimized. The method uses the camera to collect the image and video information of the measured object, and transmits it to the controller. The controller corrects the signal obtained by the wireless sensor in the database to reproduce the position of the measured object and the 3D image. A real-time tracking method of motion trajectory based on computer vision is presented. The object autonomous capture, 3D position and motion trajectory tracking. Simulation experiments show that this method is quite different from conventional image processing methods. This method has the advantages of small computation, fast running speed and good real-time performance. It meets the needs of embedded 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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DGNet: Effective image Rain Removal through Comprehensive Detection and Generation

DGNet: Effective Image Rain Removal through Comprehensive De...

引用

image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Xiaotian Wan Xuefeng Yan Computer Science and Technology Department Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Rain in real-world scenes is influenced by a multitude of environmental factors, presenting considerable challenges for single image deraining (SID) techniques. Current methodologies predominantly depend on intricate feature extraction modules to enhance visual quality, albeit on a limited subset of synthetic data. Nevertheless, due to the pronounced discrepancy between synthetic and real-world data, the efficacy of these methods in practical applications is diminished. To mitigate these limitations, we introduce a novel rain detection and generation network. In particular, we refocus the learning objective on rain mask identification, for which we develop a dedicated rain detection module. Subsequently, a pixel-wise filtering module is employed to utilize the derived mask information, thereby refining the image restoration process. Furthermore, we introduce a Rain Generation Module designed to bridge the gap between synthetic and real data during network training. The experimental results, derived from both synthetic and real-world datasets, substantiate the superior performance of our proposed approach.

关键词： Training Computer vision visualization Rain Filtering Refining machine learning image restoration Object recognition Synthetic data

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Performance analysis of saliency detection in images

Performance analysis of saliency detection in images

引用

3rd International conference on Advancement in Nanoelectronics and Communication Technologies (ICANCT)

作者： Reddy, Maram Sreerama Nithya, B. Natl Inst Technol Comp Sci & Engn Dept Trichy 620015 Tamil Nadu India

Detecting salient objects in an image has a vast number of applications across the web, and mostly it is done manually. Since the internet is growing faster than ever, it is not a feasible solution for high-scale dynamic applications. Hence, image processing automation with computer vision and machine learning has become a burning topic of research in the past few years. Saliency based image cropping is a task of identifying the notable segments in an image and be able to accommodate these parts in the crop ratio for any given specific viewport. It helps not only in image cropping but also with object recognition, visual tracking and visual restoration, etc. To address this, a novel approach for saliency detection is proposed based on heat map analysis and boundary prior. In this approach, the images go through heat map anal-ysis to identify salient objects based on machine learning model. After that, the images are segmented based on color and texture difference and then major contours are identified. The contours and the obtained saliency coordinates are accommodated inside the crop for each viewport requirement. The crop coordinates then go through two more processes, firstly a shift center process where the crop center is moved towards the important but lesser salient object, and then an inclusivity rule checks that the image is not cropped at coordinates without any salient objects. The simulation results reveal that the proposed algorithm attains better results than the cutting-edge algorithm of Twitter with similarity index of 89.89%.(c) 2022 Elsevier Ltd. All rights *** and peer-review under responsibility of the scientific committee of the 3rd International Con-ference on "Advancement in Nanoelectronics and Communication Technologies".

关键词： Saliency detection image processing viewport Heat map Inclusivity rule Similarity index

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Embedded vision System Controlled by Dual Multi-frequency Tones 16th

Embedded Vision System Controlled by Dual Multi-frequency To...

引用

16th International conference on Information Technology and applications, ICITA 2022

作者： Orlando Guerrero, I.J. Ruiz, Ulises Corte, Loeza Hernadez Paxtian, Z.J. Universidad de la Cañada Teotitlán de Flores Magón Oax Mexico Instituto nacional de astrofísica óptica y electrónica. Sta María Tonantzintla San Andrés Cholula Pue Mexico

ISBN: (纸本)9789811993305

An embedded vision system, based on the conjunction of a mobile, a DTMF (dual-tone multi-frequency) module, and a four-bit relay module, is presented in this paper. The mobile camera is employed to distinguish color characteristics of analyzed objects by means of digital processing. Each time a feature is distinguished, the mobile generates a different tone through the audio port, which is sent to the DTMF module to generate one of four available digital outputs. The booster module allows to amplify this digital signal, which can be used for a power amplifier. In this research, the linear velocity of the system was evaluated, from the moment the image is acquired until the power signal is activated, for this purpose, an oscilloscope was used to perform a timing analysis. The results show that the system cannot distinguish color characteristics of objects at a speed greater than 48 cm/s. This system is intended to be used in the food industry, as an alternative vision machine, object selector. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Power amplifiers

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Research on Helmet Wearing Detection for Dense Cycling Crowd Based on Improved YOLOv8n

Research on Helmet Wearing Detection for Dense Cycling Crowd...

引用

image processing, Computer vision and machine Learning (ICICML), International conference on

作者： Kaihua Wang Zuoshi Liu School of Mechanical and Electrical Engineering Jiangxi University of Science and Technology Ganzhou China Jiangxi University of Science and Technology Ganzhou China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

An improved YOLOv8n algorithm is proposed to address the issue of low helmet detection accuracy caused by the high density of electric bike riders in complex scenarios. Introducing Channel Prior Convolutional Attention (CPCA) mechanism to enhance the feature extraction capability of the model; Introducing Symmetric Positive Definite Convolution (SPDConv) to enhance ordinary convolutions; Replace the bounding box loss function with a Weighted Intersection over Union (Wise IoU) function to improve the model’s ability to locate small targets. The experimental results showed that the improved model improved the mAP accuracy detected on the self-made dataset by 4.4%, with only a slight increase in the number of model parameters, and has the potential to be used as a solution in practical applications.

关键词： Computer vision Head Accuracy machine learning algorithms Attention mechanisms Convolution Feature extraction Electric vehicles Safety

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Model Pruning for Infrared-visible image Fusion

Model Pruning for Infrared-Visible Image Fusion

引用

machine vision, image processing and Imaging Technology (MviPIT), International conference on

作者： Qi Chen Rui Feng State Grid Beijing Electric Power Company Beijing China

ISBN: (数字)9798331543037

ISBN: (纸本)9798331543044

Infrared-visible image fusion combines complementary information from both modalities, enhancing scene perception in applications such as surveillance and autonomous driving. However, existing deep learning-based methods are often computationally expensive. Our approach involves training an over-parameterized fusion network, applying structured pruning to reduce model complexity, and fine-tuning the pruned model to maintain performance. The pruning process leverages the L2-norm of the Restormer blocks, ensuring that less critical components are removed while preserving essential fusion quality. Experiments on the benchmark datasets demonstrate that our approach achieves high fusion quality with significantly reduced computational costs. Ablation studies further validate the effectiveness of our pruning strategy.

关键词： Training Learning systems Adaptation models Computational modeling Surveillance Streaming media Real-time systems Complexity theory image restoration image fusion

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Research on Badminton Movement machine Learning Model Based on Computer vision Technology

Research on Badminton Movement Machine Learning Model Based ...

引用

image processing and Computer applications (ICIPCA), IEEE International conference on

作者： Cheng Zong Hohhot Vocational College Hohhot China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper aims to explore an innovative method combining computer vision and machine learning to accurately identify and analyze various movements in badminton. This paper first summarizes the application prospect of computer vision in the field of sports analysis, and introduces its specific application scenarios in badminton in detail. By constructing a complete technical framework of image preprocessing module, feature extraction algorithm and deep learning model, the complex movements of badminton players such as swing, stroke and moving pace are captured and analyzed. In the research process, we used multi-view image fusion and key point detection technology to accurately extract action features in badminton, combined with convolutional neural network (CNN), recurrent neural network (RNN), long term memory network (LSTM) and other deep learning models to efficiently learn and model these features. Thus, the automatic classification and recognition of badminton movement can be realized. The experimental results show that the model has significant accuracy in badminton action recognition, good generalization ability and practicability, and can be effectively applied in the badminton teaching and training process of athlete performance evaluation, competition data analysis and other aspects. This research result not only expands the practical application of computer vision technology in the field of badminton, but also provides new ideas and tools for further promoting the development of sports intelligence and digitalization.

关键词： Training Deep learning Computer vision Analytical models Recurrent neural networks Computational modeling Feature extraction Data models Convolutional neural networks Sports

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