Robust Optimized Discrete Wavelet Transform-Singular Value Decomposition Based Video Watermarking

Kuraparthi, Swaraja; Kollati, Meenakshi; Kora, Padmavathi

doi:10.18280/ts.360612

Cited by 34 publications

(18 citation statements)

References 24 publications

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“…The recommended approach for detecting normal and abnormal instances in thermograms is designed in four key phases. These stages are as follows [10][11][12][13][14][15][16]…”

Section: Methodsmentioning

confidence: 99%

Breast Thermograms Asymmetry Analysis using Gabor filters

Yadlapalli¹,

Reddy²,

Sunitha³

et al. 2021

E3S Web Conf.

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Women are far more likely than males to acquire breast cancer, and current research indicates that this is entirely avoidable. It is also to blame for higher death rates among younger women compared to older women in nearly all developing nations. Medical imaging modalities are continuously in need of development. A variety of medical techniques have been employed to detect breast cancer in women. The most recent studies support mammography for breast cancer screening, although its sensitivity and specificity remain suboptimal, particularly in individuals with thick breast tissue, such as young women. As a result, alternative modalities, such as thermography, are required. Digital Infrared Thermal Imaging (DITI), as it is known, detects and records temperature changes on the skin’s surface. Thermography is well-known for its non-invasive, painless, cost-effective, and high recovery rates, as well as its potential to identify breast cancer at an early stage. Gabor filters are used to extract the textural characteristics of the left and right breasts. Using a support vector machine, the thermograms are then classified as normal or malignant based on textural asymmetry between the breasts (SVM). The accuracy achieved by combining Gabor features with an SVM classifier is around 84.5 percent. The early diagnosis of cancer with thermography enhances the patient’s chances of survival significantly since it may detect the disease in its early stages.

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“…The recommended approach for detecting normal and abnormal instances in thermograms is designed in four key phases. These stages are as follows [10][11][12][13][14][15][16]…”

Section: Methodsmentioning

confidence: 99%

Breast Thermograms Asymmetry Analysis using Gabor filters

Yadlapalli¹,

Reddy²,

Sunitha³

et al. 2021

E3S Web Conf.

Self Cite

View full text Add to dashboard Cite

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“…The publicly available MIVIA data set is used and it consists of 200 Car crashes, 200 Tire Skidding audio segments. Mutual information algorithm used as feature selection method and SVM is used for detecting events (Swaraja, 2018;Padmavathi and Krishna, 2014;Kuraparthi et al, 2019). From results, it is inferred that the proposed approach had an improvement by 7% in accuracy rate when compared to existing methods (Almaadeed et al, 2018) Smartly connected vehicles that improve the passengers safety if a car accidents occur is a low cost, car crash detection system called Crashzam.…”

Section: Related Workmentioning

confidence: 99%

Soft computing based audio signal analysis for accident prediction

Valiveti¹,

Kumar²,

Duggineni³

et al. 2021

IJPCC

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Purpose Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence. Design/methodology/approach The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques. Findings Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested. Practical implications Denoising of the audio samples for perfect feature extraction was a tedious chore. Originality/value The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

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“…To obtain ground truth depth values and obtain high performance, we employed a CNN deep learning model and a supervised deep learning-based technique. Monocular depth estimation relies on image processing techniques [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] and also helpful during communication [29][30][31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Monocular Depth Estimation using Transfer learning-An Overview

Swaraja¹,

Akshitha²,

B³

et al. 2021

E3S Web Conf.

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Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated.

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Robust Optimized Discrete Wavelet Transform-Singular Value Decomposition Based Video Watermarking

Cited by 34 publications

References 24 publications

Breast Thermograms Asymmetry Analysis using Gabor filters

Breast Thermograms Asymmetry Analysis using Gabor filters

Soft computing based audio signal analysis for accident prediction

Monocular Depth Estimation using Transfer learning-An Overview

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