A robust blind color image watermarking based on Fourier transform domain

Aerial imagery pile burn detection using deep learning: The FLAME dataset

This chapter contains sections titled: Introduction Principles of QAM-OFDM Modulation by DFT Transmission via Bandlimited Channels Generalised Nyquist Criterion Basic OFDM Modem Implementations Cyclic OFDM Symbol Extension Reducing MDI by Compensation [15] Decision-Directed Adaptive Channel Equalisation OFDM Bandwidth Efficiency Chapter Summary and Conclusion

Introduction to OFDMOFDM and MCCDMA for Broadband Multiuser Communications WLANS and Broadcasting. L.Hanzo, Mnster, T. Keller and B.J. Choi,

Unmanned aerial vehicles (UAVs) or Drones technology has a huge potential for supporting different efficient solutions for the smart applications in our world. The applications include sma...

A survey on recent optimal techniques for securing unmanned aerial vehicles applications

Digital image watermarking techniques have been developed widely in recent years to maintain the broadcasting media and content authentication, broadcast monitoring, copy control, and many other applications. Therefore, many studies have used digital image watermarking to solve these problem. This paper highlights digital image watermarking. It starts with a basic model of digital image watermarking, it discusses the main requirements and applications. Moreover, it reviews some of the techniques and algorithm used in image watermarking. In addition, digital image watermarking attacks are discussed. Lastly, Watermarking evaluation system is described.

Properties of digital image watermarking

Digital watermarking is a technique which allows an individual to add hidden copyright notices or other verification messages to digital audio, video, or image signals and documents. Watermarking can be done by using least significant bit (LSB), singular value decomposition (SVD), Discrete Fourier Transform (DFT), Discrete cosine transform (DCT) and Discrete wavelet transform (DWT) techniques. In this paper Discrete cosine transform (DCT) and Discrete wavelet transform (DWT) are used for embedding and extraction of watermark. DWT and DCT are compared with respect to peak signal to noise ratio (PSNR) at a different threshold values. DWT gives better Image quality then DCT.

Digital Image Watermarking by Using Discrete Wavelet Transform and Discrete Cosine Transform and Comparison Based on PSNR

This paper presents performance comparison of three leading face recognition techniques. In the first method, the face recognition is done using principle component analysis (PCA). In the second method we combine K-nearest neighbor (KNN) classification method with PCA. The face recognition using histogram is also carried out. The above methods are compared on the basis of accuracy and time taken in an ORL database and YALE database.

Comparative Study of Different Face Recognition Techniques

Physical fading radio channels encountered in wireless mobile communication are often modeled as a complex Gaussian process whose envelope is statistically described by Rayleigh or Rician probability distribution function (PDF). In most of the literature, the accuracy of the simulation model is estimated by comparing the simulated autocorrelation function (ACF) of inphase (or quadrature phase) component of the fading process and ACF of squared envelope with the analytical ones. In this paper, we examine the performance of a sum of sinusoid (SOS) based Rician fading channel simulator on the basis of the ACF and power spectral density (PSD) of the fading envelope. We obtained simplified approximate expressions for the autocorrelation and mean value of the fading envelope which become more accurate as the value of Rice factor increases. In the simulation, the line-of-sight (LOS) component is modeled as a zero-mean random variable with pre-chosen angle of arrival (AOA) and random initial phase. We showed that the AOA of the LOS component significantly affects the level crossing rate (LCR) and average fade duration (AFD) of the fading envelope. All simulation results are compared with the analytical results and a very good agreement between them is found.

On the Analysis of Envelope Correlation and Spectra for Rician Fading Channel

Every day, a huge figure of medical face images are created by dermatologist and medical imaging center for research, surgical, and acne diagnostics. All of such images are kept for future remark and citation of the patients consequently, such medical images are required to endure the compression process prior to its storage. Acne face images have several parts which are having more information than the supplementary part of face called the region of interest (ROI). The lossless compression approach is essential in archival and communique of remedial images and it is found that discrete wavelet transform is used in this rationale. In this paper, a new algorithm is anticipated to choose automatically a part or patch of an image from the whole image which contains more information and is to be sent uncompressed.

DWT Based Compression Algorithm on Acne Face Images

For saving human life and wildlife, methods of fire safety need to be implemented in different areas like house hold, industries, forests etc. Algorithms of image processing have been developed over the years for fire detection. Such algorithms have been discussed here. Although these algorithms are able to detect fire but unable to differentiate between fire and orange color. In our proposed method, we developed a discrimination algorithm to differentiate between fire and orange color. This algorithm is applied on the detected fire pixels to check whether they are fire pixels or orange color pixels.

Rekha Gupta

Papers

Digital Image Watermarking by Using Discrete Wavelet Transform and Discrete Cosine Transform and Comparison Based on PSNR

Comparative Study of Different Face Recognition Techniques

On the Analysis of Envelope Correlation and Spectra for Rician Fading Channel

DWT Based Compression Algorithm on Acne Face Images

A Highly Accurate Fire Detection Method Using Discriminate Method