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11th EAI International conference on Context-Aware Systems and applications, ICCASA 2022

11th EAI International Conference on Context-Aware Systems a...

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11th EAI International conference on Context-Aware Systems and applications, ICCASA 2022

ISBN: (纸本)9783031288159

The proceedings contain 14 papers. The special focus in this conference is on Context-Aware Systems and applications. The topics include: Prediction of Chaotic Time Series Based on LSTM, Autoencoder and Chaos Theory;an Approach to Selecting Students Taking Provincial and National Excellent Student Exams;safe Interaction Between Human and Robot Using vision Technique;application of the image processing Technique for Powerline Robot;collaborative Recommendation with Energy Distance Correlation;blockchain Model in Industrial Pangasius Farming;multiple-Criteria Rating Recommendation with Ordered Weighted Averaging Aggregation Operators;a Survey of On-Chip Hybrid Interconnect for Multicore Architectures;a Framework for Brain-Computer Interfaces Closed-Loop Communication Systems;identification of Abnormal Cucumber Leaves image Based on Recurrent Residual U-Net and Support Vector machine Techniques;lung Lesion images Classification Based on Deep Learning Model and Adaboost Techniques;balltree Similarity: A Novel Space Partition Approach for Collaborative Recommender Systems.

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Research on the Application of machine Learning in Visual image Design

Research on the Application of Machine Learning in Visual Im...

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

作者： Minnan Cang Meng Zhang Wei Zhou Xi’an University of Technology Xi’an China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

With the rapid development of artificial intelligence, machine learning applications in visual design have become increasingly widespread, particularly in the field of image processing. This study presents an intelligent image generation platform based on Generative Adversarial Networks (GANs) aimed at improving design efficiency and creative expression. The platform integrates Deep Convolutional GANs (DCGANs) with style transfer techniques to efficiently generate high-quality images with artistic and visual appeal. Experimental results demonstrate that the model outperforms traditional methods in terms of clarity (PSNR of 28.4 dB), realism (Inception Score of 7.5), and style consistency (Style Loss of 0.0025). User experience evaluations indicate that designers rate the platform highly for ease of use and image generation quality. This model not only enhances design efficiency but also serves as a powerful tool for creative visual tasks, advancing the automation and intelligence of the design process.

关键词： Visualization Computer vision Automation image synthesis Computational modeling machine learning Generative adversarial networks User experience

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High Dynamic Range image processing Techniques for Automation and machine vision

High Dynamic Range Image Processing Techniques for Automatio...

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Automation, Robotics and Computer Engineering (ICARCE), International conference on

作者： Chunhao Li Zhejiang University of Science and Technology Hangzhou China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

High Dynamic Range (HDR) imaging has become a significant technological advancement in visual data processing, allowing for the capture of a wider dynamic range of luminance levels in images. This paper explores various HDR processing techniques and their potential applications in automation and machine vision. By using methods such as multiple image fusion, image registration, and tone mapping, the paper demonstrates how HDR processing can enhance visual data in automated systems, improving accuracy in environments requiring complex lighting conditions. This work applies HDR algorithms to real-world scenarios, showcasing their potential in industrial automation and robotics, where accurate visual data plays a crucial role.

关键词： Visualization image registration Automation Accuracy Service robots machine vision Lighting Real-time systems High dynamic range image fusion

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Gesture Recognition of Filipino Sign Language Using Convolutional and Long-Short Term Memory Neural Network 1

引用

Intelligent Systems conference (IntelliSys)

作者： Cayme, Karl Jensen F. Retutal, Vince Andrei B. Salubre, Miguel Edwin P. Canete, Luis Gerardo S. Astillo, Philip Virgil B. Univ San Carlos Cebu Philippines

ISBN: (数字)9783031477249

ISBN: (纸本)9783031477232;9783031477249

Sign language is a form of communication prominently used by the deaf-mute community to convey their ideas and thoughts. In the Philippines, local signers use Filipino Sign Language (FSL) derived from the well-known American Sign Language (ASL). Despite the recent formalization of FSL as the country's official sign language, there is still minimal familiarity among the public. That said, Sign Language Recognition (SLR) systems integrated with machine learning applications have been developed to understand FSL better. However, the prevalent limitations of most of these systems are that it only involves static signs and asynchronous recognition. This study aimed to take this solution further and overcome existing limitations by developing a model capable of recognizing FSL gestures in real-time usable for applications such as in government service centers. To this end, the study proposes the deep learning algorithm Convolutional and Long Short-Term Memory Neural Networks in system capturing of real-time signs from a signer. The proponents considered 15 signs related to common greetings and business transactions. A total of 450 video recordings were collected for the signs with each having an equal number of samples. The collected data underwent cleaning, preprocessing, and augmentation before training. The proposed model's performance was analyzed with the following classification metrics: Accuracy, Precision, Recall, and F1-Score, and was able to achieve 95% accuracy and a macro-average of 0.95 precision, 0.95 Recall, and 0.95 F1-Score. Furthermore, the model had a comparable accuracy and loss between validation and test data-a 95.18% accuracy and 0.13629 loss on validation while 95.93% accuracy and loss of 0.1478 on the test. With that said, the proposed model was well-fit for classifying the 15 signs that involve upper body movements.

关键词： Computer vision CNN-LSTM Deep learning Filipino sign language image processing Sign language recognition system machine learning

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Research on Key Technologies of target location based on Intelligent Robot 3

Research on Key Technologies of target location based on Int...

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3rd International conference on Computer vision, image and Deep Learning and International conference on Computer Engineering and applications, CVIDL and ICCEA 2022

作者： Li, Zhong Guo, Caihui Guangzhou City Construction College Guangdong China Citi University Ulaanbaatar Mongolia

ISBN: (纸本)9781665459112

In recent years, the country has proposed the strategic development goal of 'Made in China 2025', and the intelligent manufacturing industry has gradually received national attention. Intelligent robots rely on the advantages of high efficiency and strong adaptability to the working environment to promote the development of intelligent manufacturing industry. machine vision technology makes robots more intelligent, and the robot system integrating vision has also developed rapidly. Compared with monocular vision, binocular vision can obtain target stereo information, and it has become a key research topic in related fields. Under this background, this paper designs an intelligent robot target recognition and positioning system based on binocular vision according to the actual industrial demand. This paper mainly studies the recognition and localization algorithm of circular object workpiece by intelligent robot based on binocular vision system, and designs and realizes the whole experimental system according to the research content. Based on the system experiment, this paper introduces the calibration of the binocular camera system and the robot vision system, the target image pretreatment algorithm, the recognition and positioning algorithm of the target workpiece and so on. Finally, the visual interface is designed to control the robot to complete the simulated grasping action and realize the whole system. The object image is acquired by binocular camera, the object identification and location are completed according to the image processing algorithm, and the robot is controlled to complete the simulation grasping experiment, which has certain reference significance in practical application. © 2022 IEEE.

关键词： Binocular vision

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A Parallel Sea-Land Segmentation Method Based on Improved Unet Model 3

A Parallel Sea-Land Segmentation Method Based on Improved Un...

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3rd International conference on Computer vision and Pattern Analysis, ICCPA 2023

作者： Zhang, Xuebo Jiang, Min Li, Jinli School of Space Information Space Engineering University Beijing China 96944 troops Chinese People's Liberation Army Beijing China

ISBN: (纸本)9781510667563

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved Unet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on high-resolution remote sensing images. © 2023 SPIE.

关键词： image segmentation

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Feature-Guided machine-Centric image Coding for Downstream Tasks

Feature-Guided Machine-Centric Image Coding for Downstream T...

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2023 IEEE International conference on Multimedia and Expo Workshops, ICMEW 2023

作者： Kwak, Sangwoon Yun, Joungil Choo, Hyon-Gon Kim, Munchurl Electronics and Telecommunications Research Institute Korea Republic of Korea Advanced Institute of Science and Technology Korea Republic of

ISBN: (纸本)9798350313154

Video coding, a process of compressing and decompressing digital video content, has traditionally been optimized for human visual systems by reducing its size while maintaining the human perceptual quality. However, with the remarkable progress of artificial intelligence (AI) technology, the need for machine-centric coding has rapidly been increasing in recent years. In response to these trends, international standardization organizations such as MPEG are actively working to develop and launch new standards on coding technologies for machines, called video coding for machines (VCM). In this paper, we present a novel feature-guided block-wise image blending method for image compression, which is suitable for machine applications such as object detection and segmentation. For this, we use a gradient map of the feature loss using the pretrained encoder part of a task-specific network as guide for input degradation, so that the degraded input images can be effectively compressed for machine-centric tasks. Our method is simple but effective because additional training is not required by utilizing the pretrained encoder parts of networks for targeted tasks. Experimental results show that BD-rate gains can be obtained by applying our proposed method with averages 11% and 8% for object detection and instance segmentation tasks, respectively, compared to the image anchor results of MPEG-VCM reference software v0.4. © 2023 IEEE.

关键词： Computer vision

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Comprehensive Weather Recognition Using ResNet-50 and a Novel Weather image Dataset 4

Comprehensive Weather Recognition Using ResNet-50 and a Nove...

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4th International Signal processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Wang, Jun Zhou, Yunhan Fu, Min School of Electrical and Electronics Engineering UESTC Chengdu China Cyrinn Science & Technology Ltd. Guangzhou China

ISBN: (纸本)9798331528676

Weather conditions play a crucial role in both daily human activities and industrial operations. For example, recognizing different weather patterns is critical for outdoor automation systems. With the development of AI technology, scientific researchers have trained many models which receive weather images as inputs and output the weather in these images fast and accurately. However, many existing weather recognition models are limited in scope, typically identifying only four types of weather with varying degrees of accuracy. In this study, the two key contributions of this research are: first, the creation of a custom dataset designed specifically for recognizing seven distinct weather conditions, and second, the exploration of multiple machine learning techniques to identify the most effective model for this task. By developing our own dataset, we ensure a broader representation of weather types, allowing the models to train and be tested on more diverse and realistic scenarios. We evaluate five different algorithms: Support Vector machine (SVM), Random Forest, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and ResNet50. To enhance model generalization and reduce overfitting, we employ data augmentation techniques and add dropout layers in the CNN and ResNet50 architectures. Through extensive experimentation, ResNet50 emerges as the best-performing model, achieving an accuracy of approximately 87% on the custom dataset. This work demonstrates the importance of creating domain-specific datasets and using a range of machine learning methods to develop accurate and reliable weather recognition models. Our approach offers a robust solution for outdoor vision systems, with potential applications in autonomous vehicles, surveillance systems, and other environments where accurate weather detection is critical for safety and performance. ©2024 IEEE.

关键词： Convolutional neural networks

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Joint Demosaicing and Denoising with Frequency Domain Features 32nd

Joint Demosaicing and Denoising with Frequency Domain Featur...

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32nd International conference on Artificial Neural Networks (ICANN)

作者： Li, Feiyu Yang, Jun Zhejiang Sci Tech Univ Sch Informat Sci & Engn Hangzhou Peoples R China Jiaxing Univ Coll Informat Sci & Engn Jiaxing Peoples R China

ISBN: (纸本)9783031441974;9783031441981

image demosaicing and denoising play important roles in modern digital camera image processing and have been studied in recent decades. Due to the interference of independent demosaicing and denoising causes color errors and the loss of image details, therefore, the joint demosaicing and denoising problem has been extensively studied. Although the simple combination of existing demosaicing and denoising methods can obtain recovery images, there are still considerable room for improvement. In this paper, we propose an end-to-end frequency domain features network (FFNet) to solve the ill-posed joint problem. Different from previous methods based on spatial domain features, FFNet uses frequency domain information to learn global and local image features, and it is based on the vision Transformer architecture. Our model contains mainly two frequency domain feature blocks: a global Fourier block (GFB) that learns a global frequency domain parameter weight to achieve global attention and a local Fourier block (LFB) based on multi-layer perceptron to enhance local feature learning. These two modules and channel attention are combined into the frequency domain attention block (FAB), which constitutes the core module of FFNet. Extensive experiments demonstrate that FFNet outperforms previous approaches and achieves state-of-the-art performance.

关键词： image demosaicing and denoising joint problem frequency domain network

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3D Object Reconstruction with Deep Learning 13th

3D Object Reconstruction with Deep Learning

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13th IFIP TC 12 International conference on Intelligent Information processing, IIP 2024

作者： Aremu, Stephen S. Taherkhani, Aboozar Liu, Chang Yang, Shengxiang School of Computer Science and Informatics De Montfort University Leicester United Kingdom Digital Factory Department Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783031579189

Recent advancements and breakthroughs in deep learning have accelerated the rapid development in the field of computer vision. Having recorded a huge success in 2D object perception and detection, a lot of progress has also been made in 3D object reconstruction. Since humans can infer and relate better with 3D world images by just a single view 2D image of the object, it is necessary to train computers to think in 3D to achieve some key applications of computer vision. The use of deep learning in 3D object reconstruction of single-view images is rapidly evolving and recording significant results. In this research, we explore the Facebook well-known hybrid approach called Mesh R-CNN that combines voxel generation and triangular mesh reconstruction to generate 3D mesh structure of an object from a 2D single-view image. Although the reconstruction of objects with varying geometry and topology was achieved by Mesh R-CNN, the mesh quality was affected due to topological errors like self-intersection, causing non-smooth and rough mesh generation. In this research, Mesh R-CNN with Laplacian Smoothing (Mesh R-CNN-LS) was proposed to use the Laplacian smoothing and regularization algorithm to refine the non-smooth and rough mesh. The proposed Mesh R-CNN-LS helps to constrain the triangular deformation and generate a better and smoother 3D mesh. The proposed Mesh R-CNN-LS was compared with the original Mesh R-CNN on the Pix3D dataset and it showed better performance in terms of the loss and average precision score. © IFIP International Federation for Information processing 2024.

关键词： Computer vision

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