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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Patel, Krupa Misra, Indranil Patel, Narendra Moorthi, S. Manthira Dhar, Debajyoti Birla Vishvakarma Mahavidyalaya Engineering College Dept. of Computer Science and Engineering Anand India Signal and Image Processing Group Ahmedabad India

ISBN: (纸本)9798350302288

Remote sensing image fusion amalgamate information from panchromatic and multispectral remote sensing images to generate an optimum representative image. In this paper, we have proposed a novel method for image fusion known as Residual Deep Learning with Joint Bilateral Denoising Network (RJB-Net). The RJB-Net procedure is initiated with panchromatic image denoising using a joint bilateral filter to suppress noise and retain edge information. RJB-Net model is trained at image patches covering all major spectral classes. The denoised panchromatic image along with multispectral data is input to the residual deep learning network for inference and generates a fused image. The remote sensing datasets used for RJB-Net training and evaluation are Indian Cartosat-1 panchromatic and Resourcesat-2 multispectral imagery. The experiment section compares the RJB-Net denoising output with prominent spatial domain image filtering methods both qualitatively and quantitatively. It is found that RJB-Net fused images inject the spatial information from panchromatic images in a controlled manner while retaining the spectra characteristics of multispectral data. The merging performance of RJB-Net is compared with state-of-the-art remote sensing image fusion techniques that range from classical to recent deep learning model. The proposed method RJB-Net generates superior results among all fusion methods at different feature targets and compared the performance both visually as well as with image quality metrics. © 2023 IEEE.

关键词： Remote sensing

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Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment 31

Siamese Residual Neural Network for Musical Shape Evaluation...

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31st European Signal processing Conference, EUSIPCO 2023

作者： Li, Xiaoquan Weiss, Stephan Yan, Yijun Li, Yinhe Ren, Jinchang Soraghan, John Gong, Ming Centre for Signal and Image Processing Dept. Electronic & Electrical Engineering University of Strathclyde Glasgow United Kingdom National Subsea Centre Robert Gordon University Aberdeen United Kingdom

ISBN: (纸本)9789464593600

Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, namely MSED-4k, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score. © 2023 European Signal processing Conference, EUSIPCO. All rights reserved.

关键词： Music

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MB-CapsNet: An Efficient Fusion of MobileNetV3 and Matrix Capsule Networks

MB-CapsNet: An Efficient Fusion of MobileNetV3 and Matrix Ca...

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2022 Chinese Automation Congress, CAC 2022

作者： Han, Wenxing Zou, An-Min Shantou University Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Dept. of Electronic and Information Engineering Shantou China

ISBN: (纸本)9781665465335

The capsule network constructs the relationship between the part and the whole in an image through feature coding, which shows excellent performance in the image classification. However, the original capsule network is weak in extracting useful feature information, because it tends to explain all the information in a graph and the advantages of the capsule network cannot be reflected in the face of complex background datasets. By using MobileNetV3 as the backbone to replace the simple primary convolutional network layer, an improved capsule network named MB-CapsNet is proposed in this paper. The advanced network reducing the interference of invalid features to focus on effective features. At the same time, a variety of parameter sets are employed and extensive experiments are proceeded on four kinds of datasets. After that, an optimal network configuration is obtained which is suitable for different datasets. The experimental results illustrate that the advanced MB-CapsNet is more effectively than the original capsule network in the feature extraction, showing its advantages and potential in complex datasets. © 2022 IEEE.

关键词： image classification

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Exploring Jaw Morphology and Patient-specific Dynamic Rotation Trajectory Using Cone Beam Computed Tomography

Exploring Jaw Morphology and Patient-specific Dynamic Rotati...

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International Conference on Communication engineering and Technology (ICCET)

作者： P S Anusree Bikram Keshari Parida Wonsang You Dept. of Information and Communication Engineering Artificial Intelligence and Image Processing Laboratory (AIIP Lab) Sun Moon University Asan South Korea

ISBN: (数字)9798350373271

ISBN: (纸本)9798350373288

Cone Beam Computed Tomography (CBCT) is a widely adopted technology in dental healthcare, particularly for surgical planning. However, effective visualization methods are crucial for formatting and analyzing volumetric data. Two primary formatting options - Curved Multi-planar Reformatting and Direct Volume Rendering - are used widely for evaluating jaw shape and creating panoramic views. Existing software methods frequently require the manual selection of several nodes along the jaw on an axial image that roughly represents the shape of the dental arch. Our work intends to introduce a novel and automatic algorithm for detecting the patient’s jaw from CBCT, suitable for defining a focal trough and evaluating an optimal patient-specific dynamic rotation trajectory applicable to rotational panoramic radiography based only on the CBCT volume data without the need to delineate a dental arch. Our findings showed that this approach works well even for patients without teeth or having heavy metal implants.

关键词： Shape Computed tomography Heuristic algorithms Software algorithms Surgery Teeth Rendering (computer graphics)

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Contents Scene Extraction Based on Distance Matrix

Contents Scene Extraction Based on Distance Matrix

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Hyok Song Minsoo Ko Jisang Yoo Intelligent Image Processing Research Center Korea Electronics Technology Institute Sungnam Korea Dept. Of Electronics Engineering Kwangwoon Univ. Seoul Korea

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

This paper shows the solution for automatic scene extraction from video footages. The purpose of this paper is development of automatic and semi-automatic video editing technology. This scene extraction algorithm has three staged cascade process. The first stage is scene analytic feature extraction using content deep network encoding information. The second stage is using temporal network for video feature extraction. And the last stage is clip video feature generation by maximum activation of convolution and normalization. A deep learning network is adapted for mixture with scene analysis feature and video feature information. This module showed 0.57 F1 score.

关键词： Deep learning image analysis Convolution Feature extraction Encoding

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Single Band NIR-to-RGB image Colorization using Attention Guided Conditional GAN

Single Band NIR-to-RGB Image Colorization using Attention Gu...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Moordhan Songade Sunita Arya S. Manthira Moorthi Dept. of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

NIR images, capturing light beyond visible spectrum, are useful in remote sensing, astronomy, etc. Converting them to RGB format makes them better to interpret for humans. This paper introduces a novel approach for NIR to RGB image colorization using GAN. Our primary contribution is a Conditional Generative Adversarial Network (cGAN) with an enhanced Attention UNet serving as its generator. With this model, we have achieved an Angular Error of 15.55, PSNR of 16.41 and SSIM of 0.69 which is comparable to the other prepared models’ like pix2pix & Attention UNet as well as more visually appealing than all of them.

关键词： Deep learning Visualization Statistical analysis image color analysis Generative adversarial networks Generators Remote sensing

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Mission Completion - image Fusion UAS Navigation 41

Mission Completion - Image Fusion UAS Navigation

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41st IEEE/AIAA Digital Avionics Systems Conference, DASC 2022

作者： Kramer, Kathleen A. Stubberud, Stephen C. University of San Diego Dept. of Electrical Engineering San DiegoCA United States Software Directorate Image Processing Controls Target Tracking Group General Atomics - Ems San DiegoCA United States

ISBN: (数字)9781665486071

ISBN: (纸本)9781665486071

An unmanned aerial system (UAS) mission of interest is to have multiple UASs coordinate the delivery of payloads. In this operation, one UAS flies over the area of interest and uses imagery to identify pinpoint areas where payload delivery should be made by other UASs. The mission operation uses imagery to pass along the location to the payload delivery UASs. Each payload UAS arrives to the area of interest using standard navigation techniques. The pinpoint navigation employs imagery *** this effort, the imagery navigation approach uses an image-correlation-based technique. The image correlator is invariant to a translation variation. The images must be mapped into a scale or rotational invariant representation. The image correlator will then provide a correlation peak based upon the representation of the navigation image. Once the target location is identified on the invariant image, the correlation peak can then be mapped back to the standard image coordinate system. To demonstrate the capabilities of the proposed image-navigation approach, the results of a two-drone experiment is presented. The technique tracks the delivery sites in the field of view while the drone maneuvers around. The resulting data shows the navigation tracking to identify that the system can control the navigation system. © 2022 IEEE.

关键词： Navigation systems

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Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment

arXiv

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arXiv 2024年

Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score. © 2024, CC BY.

关键词： Music

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Gender Detection using Facial Features with Support Vector Machine 2

Gender Detection using Facial Features with Support Vector M...

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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Hiremath, Jayaprada S. Hiremath, Shantala S. Kumar, Sujith Chincholi, Manjunath S. Patil, Shantakumar B. Hiremath, Mrutyunjaya S. Computer Science and Engineering Research Scholar at VTU Karnataka Belgaum590018 India Specialist in Image Processing Tata Elxsi Limited Karnataka Bangalore560048 India VVDN Technologies Pvt. Ltd. Karnataka Bangalore560066 India EMath Technology Pvt. Ltd. Dept. of Image Processing Karnataka Bangalore560072 India Computer Science and Engineering Sai Vidya Institute of Technology Karnataka Bengaluru560064 India

ISBN: (纸本)9781665491112

Innovative security technologies have increased the need for accurate identification. Gender identification has been widespread use of image analysis in recent decades. The face is one of the most popular biometric features in image processing. In image processing and video surveillance, systems that automatically discern gender from facial photos are gaining prominence. This study discusses face traits and gender categorization. In this study, we created a technique with good runtime and efficiency to determine human gender using face photos. It uses characteristics taken from pre-processed face photographs of various ages. Support Vector Machine (SVM) Classification was used to determine class thresholds. Our method classifies UTK-FACE gender with 91.63% accuracy. © 2022 IEEE.

关键词： Support vector machines

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Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment

Siamese Residual Neural Network for Musical Shape Evaluation...

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European Signal processing Conference (EUSIPCO)

作者： Xiaoquan Li Stephan Weiss Yijun Yan Yinhe Li Jinchang Ren John Soraghan Ming Gong Dept. Electronic & Electrical Engineering Centre for Signal and Image Processing University of Strathclyde Glasgow UK National Subsea Centre Robert Gordon University Aberdeen UK

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