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image processing: machine vision applications VII

ISBN: (纸本)9780819499417

The proceedings contain 23 papers. The topics discussed include: high throughput imaging and analysis for biological interpretation of agricultural plants and environmental interaction;investigation of segmentation based pooling for image quantification;illumination invariant 3D change detection;illumination invariant pattern recognition using fringe-adjusted joint transform correlator and monogenic signal;on the use of MKL for cooking action recognition;efficient adaptive thresholding with image masks;hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates;improved wheal detection from skin prick test images;eye gaze tracking using correlation filters;image thresholding using standard deviation;object detection in MOUT: evaluation of a hybrid approach for confirmation and rejection of object detection hypotheses;and scoring recognizability of faces for security applications.

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3rd International Conference on machine vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd International Conference on machine vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic applications;shaft Rotation Monitoring Using Radar Signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus images Using Pyramid Real image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC 31

FAST CODING MODE PREDICTION FOR INTRA PREDICTION IN VVC SCC

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2024 International Conference on image processing

作者： Wang, Dayong Yu, Junyi Lu, Xin Dufaux, Frederic Guo, Hongwei Guo, Hui Zhu, Ce Chongqing Univ Posts & Telecommun Chongqing Key Lab Big Data Bio Intelligence Chongqing Peoples R China Wuzhou Univ Guangxi Key Lab Machine Vis & Intelligent Control Wuzhou Peoples R China Chongqing Univ Posts & Telecommun Chongqing Key Lab Image Cognit Chongqing Peoples R China De Montfort Univ Fac Comp Engn & Media CEM Leicester Leics England Univ Paris Saclay CNRS Cent Supelec Lab Signaux & Syst Gif Sur Yvette France Honghe Univ Sch Engn Mengzi Yunnan Peoples R China Univ Elect Sci & Technol China Chengdu Sichuan Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Currently, screen content video applications are increasingly widespread in our daily lives. The latest Screen Content Coding (SCC) standard, known as Versatile Video Coding (VVC) SCC, employs screen content Coding Modes (CMs) selection. While VVC SCC achieves high coding efficiency, its coding complexity poses a significant obstacle to the further widespread adoption of screen content video. Hence, it is crucial to enhance the coding speed of VVC SCC. In this paper, we propose a fast mode and splitting decision for Intra prediction in VVC SCC. Specifically, we initially exploit deep learning techniques to predict content types for all CUs. Subsequently, we examine CM distributions of different content types to predict candidate CMs for CUs. We then introduce early skip and early terminate CM decisions for different content types of CUs to further eliminate unlikely CMs. Finally, we develop Block-based Differential Pulse-Code Modulation (BDPCM) early termination to improve coding speed. Experimental results demonstrate that the proposed algorithm can improve coding speed by 34.95% on average while maintaining almost the same coding efficiency.

关键词： VVC SCC content type fast coding mode decision BDPCM

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GH-Feat: Learning Versatile Generative Hierarchical Features From GANs

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第6期45卷 7395-7411页

作者： Xu, Yinghao Shen, Yujun Zhu, Jiapeng Yang, Ceyuan Zhou, Bolei Chinese Univ Hong Kong Dept Informat Engn Hong Kong Peoples R China Ant Res Hangzhou 310000 Peoples R China Hong Kong Univ Sci & Technol Sch Comp Sci & Engn Hong Kong Peoples R China Univ Calif Los Angeles Comp Sci Dept Los Angeles CA 90095 USA

Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and reproduce the real data distribution. Through that, a hierarchical visual feature with multi-level semantics spontaneously emerges. In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones. We first train an encoder by considering the pre-trained StyleGAN generator as a learned loss function. The visual features produced by our encoder, termed as Generative Hierarchical Features (GH-Feat), highly align with the layer-wise GAN representations, and hence describe the input image adequately from the reconstruction perspective. Extensive experiments support the versatile transferability of GH-Feat across a range of applications, such as image editing, image processing, image harmonization, face verification, landmark detection, layout prediction, image retrieval, etc. We further show that, through a proper spatial expansion, our developed GH-Feat can also facilitate fine-grained semantic segmentation using only a few annotations. Both qualitative and quantitative results demonstrate the appealing performance of GH-Feat. Code and models are available at https://***/ghfeat/.

关键词： Task analysis Generators Visualization Generative adversarial networks Feature extraction Training Representation learning Feature learning generative adversarial network generative representation image editing

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Momentum Contrast Learning for Aerial image Segmentation and Precision Agriculture Analysis

Momentum Contrast Learning for Aerial Image Segmentation and...

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

作者： Liu, Meixuan Shanghai Jianping High Sch 517 Gushan Rd Shanghai 200135 Peoples R China

ISBN: (纸本)9781665464680

Computer vision in precision agriculture analysis has gained increasing attention as recent advancements in deep learning-based methods for various tasks were proven successful. As one of the primary problems in agriculture-vision applications, semantic segmentation from aerial agricultural images, differs from common object or aerial image segmentation tasks in various ways. Recently, there have been some efforts that aim to apply deep learning techniques to model multi-spectral aerial images and segment field anomaly pattern objects with extremely irregular shapes and scales. However, most existing methods fail to propose effective methods for model initialization and perform poorly in segmenting small objects. To address these challenges, we propose a deep learning framework that leverages momentum contrast learning with a PointRend-based model for aerial image analysis. Extensive experiments have demonstrated the effectiveness of our model for better aerial image semantic segmentation.

关键词： Contrast learning Deep learning Semantic segmentation Precision agriculture Aerial image analysis

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Cutting-Edge image Recognition Leveraging Deep Learning and machine Learning for Enhanced Accuracy

Cutting-Edge Image Recognition Leveraging Deep Learning and ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Shrivastava, Abhishek Kumar, Vinesh Maurya, Jay Prakash School of Mechanical Engineering VIT Bhopal University Sehore India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Sehore India School of Computing Science & Engineering VIT Bhopal University Sehore India

ISBN: (纸本)9798331517953

This paper investigates advanced techniques in image recognition and classification by integrating deep learning and machine learning approaches to achieve higher accuracy. Through the implementation of sophisticated training algorithms, the study demonstrates enhanced performance in recognizing and categorizing images across various data models. A major turning point in the development of image identification technology came in 2012 when deep neural networks were introduced. These networks surpassed earlier cutting-edge algorithms and completely changed the computer vision industry. This progress has brought us closer to achieving human-level accuracy in tasks such as identity verification. The role of large datasets like imageNet is crucial, as they provide the foundation for the success of deep learning. With continuous research pushing the limits of picture identification and producing major advances in human knowledge, deep learning has a huge influence on business, society, and technology. Additional research in this area might lead to creative uses that revolutionize our relationship with our surroundings. Key topics discussed include data pre-processing, post-processing, model optimization, and accuracy enhancement. The findings highlight the potential of cutting-edge technologies to advance image classification and recognition in various sectors, such as medical imaging and visual analysis. The approach emphasizes scalability and adaptability, ensuring that models can be effectively applied to real-world scenarios. Future research will focus on refining these models to handle even more complex image datasets, further enhancing their practical utility and reliability. © 2024 IEEE.

关键词： Convolutional neural networks

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Review of Industry Workpiece Classification and Defect Detection using Deep Learning

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND applications 2022年第4期13卷 329-340页

作者： Chen, Changxing Abdullah, Azween Kok, S. H. Tien, D. T. K. Taylors Univ Sch Comp Sci & Engn Subang Jaya Malaysia

Object detection and classification denotes one of the most extensively-utilized machine vision applications given the high requirements put forward for object classification and defect detection with the rise of object recognition scenes. Notwithstanding, conventional image recognition processing technology encounters specific drawbacks. Its benefits and limitations were duly compared upon selecting several typical conventional image recognition techniques. Resultantly, such recognition approaches required multiple manual participation elements and extensive manpower with restricted object identification. As a branch of machine learning, deep learning has attained more optimal results in the image recognition discipline. In the classification and defect detection of industrial workpieces, over 70 literature reviews of deep learning algorithms across multiple application scenarios for classical algorithm model and network structure assessment based on the deep learning theory. Relevant network model performance was compared and analyzed based on network intricacies parallel to natural image classification. Six research gaps were found based on the reviewed algorithm pros and cons. The corresponding six research proposal in workpiece image classification was highlighted with prospects on the workpiece image classification and defect detection direction development. It provides an empirical solution for the selection of workpiece classification and defect detection deep learning model in the future.

关键词： Convolutional neural network image processing image recognition defect detection deep learning

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Satellite Still image Classification Using CNN

Satellite Still Image Classification Using CNN

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Den, B.M. Elbialy, Samar Delta University for Science and Technology Fauclty of engineering Electronics and Communication Dep. Gamasa city Egypt

ISBN: (纸本)9798350351408

Satellite still image plays a crucial role in various domains, such as law enforcement, disaster response, and environmental monitoring. The ability to manually identify objects and facilities within these images is often crucial for these applications. However, given the extensive geographic areas that require coverage and the limited number of analysts available, automation has become a necessity. Traditional object detection and classification algorithms have proven to be insufficiently accurate and reliable for solving this problem. Fortunately, deep learning, a branch of machine learning, offers promising solutions for automating such tasks. One particular approach that has achieved success in image understanding is the use of convolutional neural networks (CNNs). Deep learning has experienced substantial advancements in diverse domains, including computer vision and natural language processing, Despite the progress made in this area, there remains a dearth of comprehensive evaluations concerning datasets and techniques tailored specifically for scene classification using satellite still image. This research paper seeks to bridge this gap by conducting a comprehensive analysis of deep learning, its development over time, and its notable applications within the domain of satellite still image. © 2024 IEEE.

关键词： Convolutional neural networks

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Defect Detection in Metal Surfaces Using Computer vision 1

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4th International Conference on Recent Trends in machine Learning, IoT, Smart Cities, and applications, ICMISC 2023

作者： Singh, Krishna Kumar Ghosh, Manish Symbiosis Centre for Information Technology Pune India

ISBN: (数字)9789819994427

ISBN: (纸本)9789819994410

Encoder models have shown remarkable success in various computer vision operations like object detection, image classification, and semantic segmentation. However, the results of one model showed that it was underfitting as it performed better on the validation set than on the training set. To gain insights into how the model was processing input images at different layers and which image features were being detected by different filters in the convolutional layers, an algorithm was used to visualize the activation maps for the output of convolutional layers in the model. To address the underfitting issue, the previous model was converted into a single feature-vector using global average pooling and used as a single layer in another deep learning model. As a result, the new model displayed a batch of images along with their corresponding segmentation masks and predicted segmentation masks generated by the encoder model. The output was a 3 × 1 image grid that included the original image, its ground truth segmentation mask, and the predicted segmentation mask. To optimize the model further, an algorithm was applied that trained an image encoder model on training data and evaluated it on validation data for a number of epochs. The algorithm then displayed a batch of samples with their ground truth and predicted segmentation masks. The results for this model were better than the previous model, as it was performing better with the train set as compared to the validation set. Overall, the usage of an encoder model and subsequent modifications allowed for improved performance in image segmentation tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Computer vision

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The Virtual Air Canvas Using image processing

The Virtual Air Canvas Using Image Processing

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Pavithra, K. Geetha, A. Chinnaiyan, R. Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350317060

One of the most interesting and challenging research focuses on pattern recognition and image processing has emerged in recent days is writing in the air. In many different applications, it can improve the interface between a machine and a human and offers a substantial contribution to the development of automated operations. In the field of computer vision, object tracking is considered a key challenge. The method of analyzing a video usually consists of three primary steps: recognizing the object, tracking its movement from frame to frame, and finally evaluating its behavior. Choosing an adequate object representation, selecting tracking features, identifying objects, and tracking them are the four problems taken into consideration for object tracking. Object tracking algorithms are widely used in many real-world applications, including autonomous surveillance, video indexing, and vehicle navigation. This work exploits this gap by developing a motion-to-text converter that may be used as software for wearable intelligent devices that allow writing in the air. The proposed work acts as a recorder of rare gestures. Computer vision will be utilized to track the finger's path. With the generated text, messages, emails, and other kinds of correspondence can all be sent. It will enable effective communication for the deaf. Keywords - object, emoji's, image color, camera © 2023 IEEE.

关键词： Computer vision

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