image Classification is the basis of Computer vision. Classification with image data finds a variety of applications in various fields. A comparative study of Classifying the images in the compressed and uncompressed ...
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ISBN:
(数字)9798350350845
ISBN:
(纸本)9798350350852
image Classification is the basis of Computer vision. Classification with image data finds a variety of applications in various fields. A comparative study of Classifying the images in the compressed and uncompressed domain is exploited in this research. A garbage classification dataset with 6 different categories is employed for this purpose. Naitve Convolutional neural network is utilized as the classifier. Compressed classification is achieved by compressing the images using discrete wavelet transform of three levels and by feeding the compressed data to the model for classification. The accuracy achieved by classification in the uncompressed domain is about 82.1% with a loss of 0.892. The accuracy achieved in the compressed domain by the three levels of the discrete wavelet transform is 81.7%,81.2%, and 74.6% with losses of 0.793, 0.891, and 1.071 respectively. There is minimal difference in accuracy achieved between uncompressed and compressed domains. image compression by discrete wavelet transform overperforms the uncompressed domain by several factors like size, elapsed time and memory space.
wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide ...
wavelet-based totally records compression is a form of information compression used to method far flung sensing and picture processing. This technique makes use of wavelets, that are mathematical features that divide a signal into separate frequency components, and gift the signal as a sum of the components. wavelet-primarily based compression permits for compression of statistics into a smaller, extra efficient form without sizeable lack of best. furthermore, it could additionally produce precise reconstructions of the original sign or image, making it a feasible tool for remote sensing and imageprocessing. This approach is utilized in a selection of applications including medical imaging, television transmission, satellite tv for pc imagery and far flung sensing. via utilising wavelets, more efficient and special records may be received. moreover, due to its low time complexity, wavelet-based compression is ideal for processing big quantities of data speedy and efficaciously.
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 W...
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 signalprocessing 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.
Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computation...
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ISBN:
(纸本)9798350365474
Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net's architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https: //***/vedernikovphoto/TCCT_Net.
image enhancement is vital in computer vision and imageprocessingapplications. This study introduces an innovative approach combining HSV color space, Discrete wavelet Transforms (DWT), and Bi Histogram Equalization...
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The work is aimed on the development of advanced algorithms for analyzing the photoplethysmogram signal, which characterizes cardiac activity. We have designed an algorithm that separates individual pulse waveforms fr...
The work is aimed on the development of advanced algorithms for analyzing the photoplethysmogram signal, which characterizes cardiac activity. We have designed an algorithm that separates individual pulse waveforms from the photoplethysmogram, even in the presence of artifacts caused by pathological changes in zebrafish cardiac activity, due to cadmium exposure. The proposed algorithm is based on digital imageprocessing and discrete wavelet transform; it enables reliable separation of individual pulse waveforms. The data obtained can then be processed using conventional techniques, providing accurate pulse wave characteristics for various applications, including toxicology studies, drug development and developmental biology research.
wavelet neural networks are a unique combination of wavelet analysis and neural network architectures, thus providing the benefits of advanced signalprocessing. Unlike traditional methods, WNNs offer a multi-resoluti...
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ISBN:
(数字)9798350369106
ISBN:
(纸本)9798350369113
wavelet neural networks are a unique combination of wavelet analysis and neural network architectures, thus providing the benefits of advanced signalprocessing. Unlike traditional methods, WNNs offer a multi-resolution approach that outshines for nonstationary signals; therefore, these are very useful in applications related to areas such as biomedical signal analysis, imageprocessing, and time-series forecasting. Architecture of WNNs enables the replacement of activation functions with wavelet functions for the extraction of localized features, making both processing and interpretation of data much more efficient. Some of the recent improvements in designing WNNs improve its computational efficiency to suit the requirements of real-time applications and also to incorporate it into deep learning frameworks, but still, one of the main challenges faced in WNNs such as computational complexity and scalability need to be addressed. As the historical development, core contributions, and evolving architecture of WNNs is done, this paper represents those along with filling up certain existing research gaps. This research paper addresses future directions regarding hybrid models that combine synergies of CNNs with RNNs as well as WNNs and therefore posits opening its way for enhanced performance across various domains. Researching such an area ensures and guarantees further expansion of applications in WNNs besides strengthening their importance in signalprocessing in the modern arena.
image de-noising is an essential field in imageprocessing, encompassing a wide range of applications. This is pre-processing task in which unwanted noise signals are removed using different techniques. Noise are unwa...
image de-noising is an essential field in imageprocessing, encompassing a wide range of applications. This is pre-processing task in which unwanted noise signals are removed using different techniques. Noise are unwanted signals which deteriorate the useful information from the image. These information may be edges, ridges, contours are other fine structures. For different applications these details are important. Noise signals may contaminate the image partially or completely. It depends upon the type of noise and its level. Noise may be categories according to its characteristics. The most frequent types of noise signals encountered in imageprocessing include Additive White Gaussian Noise, Speckle noise, salt and pepper noise, Rician noise, random noise, and more. Noise signals introduced in the images during data acquisition, transmission or due to faulty location. Additive white Gaussian noise is one of the most common noise signal which affect almost all the images in a certain extent. In this chapter we apply de-noising technique which is based on wavelet thresholding. wavelet transform is widely recognized as one of the most popular transforms in signal and imageprocessing. It is used in various imageprocessingapplications. Thresholding is an essential component in wavelet transform, and it is commonly classified into two types: hard thresholding and soft thresholding. In the chapter we apply soft thresholding technique which outperforms hard thresholding technique.
This study presents a feature-driven fusion method for combining thermal and visible images in facial analysis, leveraging morphological and gradient operations to enhance image quality and information content. By int...
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ISBN:
(数字)9798350350654
ISBN:
(纸本)9798350350661
This study presents a feature-driven fusion method for combining thermal and visible images in facial analysis, leveraging morphological and gradient operations to enhance image quality and information content. By integrating diverse features, including structural components and edge details, the proposed fusion technique offers a comprehensive representation of input images, surpassing traditional methods like Discrete wavelet Transform (DWT) based image fusion. Performance evaluation using metrics such as Peak signal-to-Noise Ratio (PSNR) and Spatial Frequency (SF) demonstrates the superior quality and enhanced texture details achieved through the feature fusion approach. This proposed fusion technique not only enhances visual quality but also enriches the fused images with detailed information, highlighting its potential for various applications in imageprocessing and analysis.
A digital watermarking scheme based on dual-tree complex wavelet transform (DTCWT), and the quadtree (QR) is suggested in this paper, in this technique the DTCWT is exerted on the host image as a first step in the pro...
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