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检索条件"任意字段=Conference on Applications of Digital Image Processing XIX"

共 10246 条记录，以下是41-50 订阅

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Compact and Efficient Neural Networks for image Recognition Based on Learned 2D Separable Transform

Compact and Efficient Neural Networks for Image Recognition ...

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Maxim Vashkevich Egor Krivalcevich Department of Computer Engineering Belarusian State University of Informatics and Radioelectronics Minsk Belarus

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper presents a learned two-dimensional separable transform (LST) that can be considered as a new type of computational layer for constructing neural network (NN) architecture for image recognition tasks. The LST based on the idea of sharing the weights of one fullyconnected (FC) layer to process all rows of an image. After that, a second shared FC layer is used to process all columns of image representation obtained from the first layer. The use of LST layers in a NN architecture significantly reduces the number of model parameters compared to models that use stacked FC layers. We show that a NN-classifier based on a single LST layer followed by an FC layer achieves 98.02% accuracy on the MNIST dataset, while having only 9.5k parameters. We also implemented a LST-based classifier for handwritten digit recognition on the FPGA platform to demonstrate the efficiency of the suggested approach for designing a compact and high-performance implementation of NN models.

关键词： Accuracy image recognition Quantization (signal) Two-dimensional displays Transforms Artificial neural networks Computer architecture image representation Field programmable gate arrays image classification

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Wavelet Analysis of Cardiograms in EDF Format

Wavelet Analysis of Cardiograms in EDF Format

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Sinan V. Kurbanov Denis A. Andrikov Department of Mechanics and Control Processes Peoples' Friendship University of Russia (PFUR) Moscow Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The correlation of the wavelet shape and the characteristic ECG section has been confirmed, and wavelets for the identification of P-, R-, and T waves have been experimentally established. An algorithm for identifying R waves in an ECG has been developed and its operability has been verified. Using the algorithm developed, a graph of the dynamics of the heartbeat period is plotted as a function of time (period number). To identify the P and T waves, the wavelet image of the real ECG is constructed, and the presence of distortion of the shape of this image due to the influence of QRS complex and neighboring waves is established. Computer removal (filtering) of QRS complexes from the ECG is carried out, wavelet image of the remaining part is calculated. The possibility of determining the positions of the maximum P- and T-waves has been established from the wavelet image of the filtered ECG. The theoretical and practical value of this study consists in a new direction of wavelet analysis of cardiac signals, by successively applying several wavelets of a special form, to identify characteristic sections of the ECG in order to improve diagnostics. The proposed method also assumes intermediate filtering of the identified sections when moving from wavelet to wavelet. The development was carried out within the framework of computer generation of arrays of digital functions of the state of the heart, suitable for training artificial intelligence systems, and diagnostics using such systems. The practical results obtained can be applied in the development of a digital expert diagnostic system or a specific technical device.

关键词： Training Correlation Shape Heart beat Filtering Heuristic algorithms Signal processing algorithms Electrocardiography Wavelet analysis Distortion

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A Machine Learning Framework for Skin Cancer Detection using Transfer Learning

A Machine Learning Framework for Skin Cancer Detection using...

引用

International image processing, applications and Systems conference (IPAS)

作者： Mary Linda Panakal Augustine Sotirios Spanogianopoulos Mhd Saeed Sharif Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London London UK School of Electrical and Mechanical Engineering University of Portsmouth Portsmouth UK

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper uses deep learning algorithms including InceptionV2, InceptionV3, DenseNet, MobileNet, and VGG19 to improve skin cancer detection. This research aims to improve skin cancer diagnosis. This work aims to determine the most effective evolutionary metrics-based technique to recognizing skin cancer, which is comparable to other diseases. Ultimately, our paper aims to create a realistic skin cancer detection system that uses the best deep learning algorithm. This discovery might improve medical diagnostics, leading to earlier diagnosis and improved healthcare outcomes.

关键词： Deep learning Machine learning algorithms image processing Transfer learning Medical diagnosis Reliability Skin cancer image classification

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Deep Learning Technique for Computer Vision-Based Pose Estimation for Augmented Reality 3rd

Deep Learning Technique for Computer Vision-Based Pose Estim...

引用

3rd International conference on Intelligent Computing Techniques for Smart Energy Systems, ICTSES 2023

作者： Palanimeera, J. Ponmozhi, K. Jyothi, Kanagaraj Dargar, Shashi Kant Birla, Shilpi Department of Computer Applications Sathyabama Institute of Science and Technology Chennai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Biotechnology Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnan Kovil Tamil Nadu Virudhunagar India Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9789819784288

Pose estimation approaches of open pose were suggested. Pose estimation in real-time is a difficult research subject with many computer vision applications. Pose estimation applications are Human Activity Estimation, Motion Transfer and Augmented Reality, Training Robots, Motion Tracking for Consoles, and Human Fall Detection. This paper describes the Augmented Reality (AR) of pose estimation. AR is an enhanced version of the real physical world that is achieved through the use of digital visual elements, sound, or other sensory stimuli delivered via technology. One interesting use for obtaining the Open-Pose discovered important locations in each pose is in the field of augmented reality. A technique for 3D model overlays on an image using pose key point information that is presented in this study. To extract crucial information from the data, the fully convolutional networks deep learning technique is used. The technique used in this work comprises the real-time intersection of the picture plane with the intended 3D model and accurate model orientation based on the key point data. The performance will greatly depend on the Graphics processing Unit specifications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Comparative Study of Autoencoders Used for Noisy images Restoration

Comparative Study of Autoencoders Used for Noisy Images Rest...

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Y.S. Bekhtin P.E. Zhgutov Department of automatics and information technologies in control Ryazan State Radio Engineering University named after V.F. Utkin Ryazan Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper is devoted to solving the problem of restoring noised images by convolutional autoencoders. It is provided a comparative research of three types of convolutional autoencoder architectures: the standard architecture based on convolution and deconvolution operations; the architecture based on Residual-blocks; the architecture based on Residual-blocks using a discriminator network. The effectiveness of the autoencoder architectures have been evaluated on synthetically noisy color images. Subjective and objective metrics as MSE, RMSE, PSNR, SSIM were used to evaluate the quality of the restored images. In the course of a comparative study, some recommendations were drawn on which of the explored autoencoders to choose providing the best effectiveness restoring the quality of distorted images.

关键词： Deconvolution Convolution Autoencoders Computer architecture digital signal processing image restoration Computational efficiency Noise measurement Convolutional neural networks Standards

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applications of Cloud Computing and Artificial Intelligence in the Oil and Gas Industry: A Review

Applications of Cloud Computing and Artificial Intelligence ...

引用

International image processing, applications and Systems conference (IPAS)

作者： Alfitouri Ibrahim Jellah Mohamed. M Moaad. I Oil and Gas Engineering Department Bani-Waleed University Bani-Waleed Libya

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

This paper reviews the latest machine learning and artificial intelligence technology, showcasing its potential to revolutionize the oil and gas industry. Despite the current limitations, the industry is on the brink of a digital transformation that promises to enhance operations and increase overall production. With the help of AI and cloud computing, the oil and gas industry's future is one of optimism and potential. The review showed a significant increase in production when applying AI technology, particularly with EOR. The paper summarized the limitations and future challenges of this technology.

关键词： Cloud computing Reviews Oils Storage management Production Machine learning Companies Gas industry Reservoirs Petroleum industry

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Method for Assessing the Efficiency of Space Debris image Formation and processing by Orbital Computer Vision Systems

Method for Assessing the Efficiency of Space Debris Image Fo...

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Ruslan S. Siryi Pavel S. Baranov Department of Television Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper proposes a method for assessing the efficiency of orbital computer vision systems (OCVS) for space debris (SD) monitoring, based on the metric of the total number of detected objects with an acceptable trajectory error for a specific OCVS as part of a spacecraft in a modeled space environment. The method incorporates models of the environment, objects, as well as the hardware and software components of the OCVS. Functional dependencies are presented, demonstrating the mutual influence of various modules included in the proposed method. The application of the method is demonstrated through the comparison of different system configurations, enabling quantitative evaluation of their efficiency and the justified optimization of system parameters.

关键词： Space vehicles Computer vision Space debris Computational modeling Orbits Software Hardware Trajectory Monitoring Signal to noise ratio

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Active Contour Method Modification for Diabetic Macular Edema Area Quantification in OCT images

Active Contour Method Modification for Diabetic Macular Edem...

引用

digital Signal processing and its applications (DSPA), International conference on

作者： Egor Volkov Anastasia Linich Alexey Averkin Intellegent Systems Department FRC CSC RAS Moscow Russia Faculty of Applied Mathematics and Informatics MIPT Moscow Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

A new method for automated quantification of diabetic macular edema on optical coherence tomograms based on modification of the active contour model is proposed. Traditional active contour models require manual initialization of the contour and are computationally expensive. This paper presents a modification of the IAC that uses a fast Fourier transform to accelerate the iterative segmentation process and automatic loop initialization. The model is reformulated using a threshold function that defines the contour as a region boundary, and includes a Fourier function of the pressure force with sign to account for global image features and a gradient force for local adaptation. A spectral residual approach based on the analysis of the frequency spectrum of the image is applied for automatic initialization. The proposed method combines the advantages of the frequency approach and regional segmentation, providing automatic initialization, robustness to background inhomogeneities, and high-speed performance comparable to traditional active contour models despite processing the entire image. This modification of the active contour model allows efficient and automatic extraction of diabetic macular edema boundaries on optical coherence tomograms, eliminating the need for manual initialization and ensuring the practical applicability of the method for quantitative assessment of the diabetic macular edema area.

关键词： image segmentation Accuracy Computational modeling Force Manuals Coherence Optical imaging Diabetes Active contours High-speed optical techniques

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Low-Data Classification of Historical Music Manuscripts: A Few-Shot Learning Approach

Low-Data Classification of Historical Music Manuscripts: A F...

引用

International image processing, applications and Systems conference (IPAS)

作者： Elona Shatri Daniel Raymond György Fazekas Centre for Digital Music Queen Mary University of London London UK Big Data Science Queen Mary University of London London UK

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

In this paper, we explore the intersection of technology and cultural preservation by developing a self-supervised learning framework for the classification of musical symbols in historical manuscripts. Optical Music Recognition (OMR) plays a vital role in digitising and preserving musical heritage, but historical documents often lack the labelled data required by traditional methods. We overcome this challenge by training a neural-based feature extractor on unlabelled data, enabling effective classification with minimal samples. Key contributions include optimising crop preprocessing for a self-supervised Convolutional Neural Network and evaluating classification methods, including SVM, multilayer perceptrons, and prototypical networks. Our experiments yield an accuracy of 87.66%, showcasing the potential of AI-driven methods to ensure the survival of historical music for future generations through advanced digital archiving techniques.

关键词： Training Support vector machines Scalability Symbols Music Self-supervised learning Multilayer perceptrons Feature extraction Cultural differences Few shot learning

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Flattening images Manifold at the Nishimori Point: Exploring Linearity in Polysemantic Embedding for Human-Aware Bayesian CNN image Classification

Flattening Images Manifold at the Nishimori Point: Exploring...

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digital Signal processing and its applications (DSPA), International conference on

作者： Vasiliy S. Usatyuk Denis A. Sapozhnikov Sergey I. Egorov R&D Department T8 LLC Moscow Russia Department of Computer Science South-West State University Kursk Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

Efficient image classification often requires a balance between accuracy and computational complexity. In this paper, we propose a graph-based spectral embedding method that leverages improved Nishimori temperature estimation to achieve low-dimensional feature representations while maintaining high classification performance. Our approach reduces image features extracted by MobileNetV2 to a 32-dimensional manifold using graph embeddings, significantly decreasing the parameter count compared to conventional methods like bottleneck ResNet-18. On the imageNet10 dataset, the proposed method achieved 98% accuracy with only 32 dimensions, while on the imageNet-100 dataset, it matched the performance of a 64-dimensional bottleneck layer using half the dimensionality. Furthermore, with polysemantic multigraph embeddings exceeding 64 dimensions, the method demonstrated a 1.5% accuracy improvement. The computational efficiency, achieved by reducing parameters from 51200 to 3200, highlights the potential of this method for resource-constrained applications. These results demonstrate that the proposed graph-based embedding framework is a scalable and effective solution for image classification tasks, offering a compelling alternative to conventional deep learning architectures. Future work will extend this approach to coarsely quantized feature extracting, improved discriminators, Diffusions and Transformers neural networks.

关键词： Manifolds Temperature distribution Accuracy image coding Estimation Feature extraction Transformers Parity check codes Computational efficiency image classification

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