A Robust Color Image Watermarking Scheme Using Entropy and QR Decomposition

Internet has affected our everyday life drastically. Expansive volumes of information are exchanged over the Internet consistently which causes numerous security concerns. Issues like content identification, document and image security, audience measurement, ownership, copyrights and others can be settled by using digital watermarking. In this work, robust and imperceptible non-blind color image watermarking algorithm is proposed, which benefit from the fact that watermark can be hidden in different color channel which results into further robustness of the proposed technique to attacks. Given method uses some algorithms such as entropy, discrete wavelet transform, Chirp z-transform, orthogonal-triangular decomposition and Singular value decomposition in order to embed the watermark in a color image. Many experiments are performed using well-known signal processing attacks such as histogram equalization, adding noise and compression. Experimental results show that proposed scheme is imperceptible and robust against common signal processing attacks

Illumination Enhancement: Image and Video

This book is a complete guide book for researchers in the field of image processing and computer vision who are dealing with illumination enhancement. Many conventional and the state-of-the-art techniques are studied in details within this book. Also in this book the new metric for measuring illumination state of an image is presented and detailed formulas and their proofs are given. The book will give also a good visual representation results of many of the techniques used for illumination enhancement

Image resolution enhancement by using interpolation followed by iterative back projection

In this paper, we propose a new super resolution technique based on the interpolation followed by registering them using iterative back projection (IBP). Low resolution images are being interpolated and then the interpolated images are being registered in order to generate a sharper high resolution image. The proposed technique has been tested on Lena, Elaine, Pepper, and Baboon. The quantitative peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) results as well as the visual results show the superiority of the proposed technique over the conventional and state-of-art image super resolution techniques. For Lena\u27s image, the PSNR is 6.52 dB higher than the bicubic interpolation

Probability distribution function based iris recognition boosted by the mean rule

In this work, a new iris recognition algorithm based on tonal distribution of iris images is introduced. During the process of identification probability distribution functions of colored irises are generated in HSI and YCbCr color spaces. The discrimination between classes is obtained by using Kullback-Leibler divergence. In order to obtain the final decision on recognition, the multi decision on various color channels has been combined by employing mean rule. The decisions of H, S, Y, Cb and Cr color channels have been combined. The proposed technique overcome the conventional principle component analysis technique and achieved a recognition rate of 100% using the UPOL database. The major advantage is the fact that it is computationally less complex than the Daugman\u27s algorithm and it is suitable for using visible light camera as opposed to the one proposed by Daugman where NIR cameras are used for obtaining the irises

Speech-based emotion recognition and next reaction prediction

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703Communication through voice is one of the main components of affective computing in human-computer interaction. In this type of interaction, properly comprehending the meanings of the words or the linguistic category and recognizing the emotion included in the speech is essential for enhancing the performance. In order to model the emotional state, the speech waves are utilized, which bear signals standing for emotions such as boredom, fear, joy and sadness. In the first step of the emotional reaction prediction system proposed in this paper, different emotions are recognized by means of different types of classifiers. The second step is the prediction of a sequence of the next emotional reactions using neural networks. The sequence is extracted based on the speech signals being digitized at tenths of a second, after concatenating the different speech signals of each subject. The prediction problem is solved as a nonlinear auto-regression time-series neural network with the assumption that the variables are defined as data-feedback time-series. the best average recognition rate is 86.25%, which is achieved by the Random Forest classifier, and the average prediction rate of reactions by using neural networks is 60.30%. © 2017 IEEE

A Robust Color Image Watermarking Scheme Using Entropy and QR Decomposition

Internet has affected our everyday life drastically. Expansive volumes of information are exchanged over the Internet consistently which causes numerous security concerns. Issues like content identification, document and image security, audience measurement, ownership, copyrights and others can be settled by using digital watermarking. In this work, robust and imperceptible non-blind color image watermarking algorithm is proposed, which benefit from the fact that watermark can be hidden in different color channel which results into further robustness of the proposed technique to attacks. Given method uses some algorithms such as entropy, discrete wavelet transform, Chirp z-transform, orthogonal-triangular decomposition and Singular value decomposition in order to embed the watermark in a color image. Many experiments are performed using well-known signal processing attacks such as histogram equalization, adding noise and compression. Experimental results show that proposed scheme is imperceptible and robust against common signal processing attacks

Medical image illumination enhancement and sharpening by using stationary wavelet transform [Kalici Dalgacik Dönüşümü Kullanarak Tibbi Imge Aydinlatma Pekiştirme ve Netleşme]

24th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- -- 122605Medical images captured by various devices have different illumination states based on chemicals used by patient prior to scanning. Consider a MRI image which has low contrast or is too bright, hence the experts cannot analysis that image due to poor representation of data in the image. In this paper we are proposing new medical image illumination enhancement and sharpening technique based on stationary wavelet transform which is addressing the aforementioned problem. The technique decomposes the input medical image into the four frequency subbands by using stationary wavelet transformation and enhances the illumination of the low-low subband image, and then it enhanced edges of image by adding the high frequency subbands to the image. The technique is compared with the conventional and state-of-art image illumination enhancement techniques such as histogram equalisation, local histogram equalisation, singular value equalisation, and discrete wavelet transform followed by singular value decomposition contrast enhancement techniques. The experimental results are showing the superiority of the proposed method over the conventional and the state-of-art techniques. © 2016 IEEE

Computational Modeling of Catecholamines Dysfunction in Alzheimer's Disease at Pre-Plaque Stage

Alzheimer's disease (AD) etiopathogenesis remains partially unexplained. The main conceptual framework used to study AD is the Amyloid Cascade Hypothesis, although the failure of recent clinical experimentation seems to reduce its potential in AD research. Objective: A possible explanation for the failure of clinical trials is that they are set too late in AD progression. Recent studies suggest that the ventral tegmental area (VTA) degeneration could be one of the first events occurring in AD progression (pre-plaque stage). Methods: Here we investigate this hypothesis through a computational model and computer simulations validated with behavioral and neural data from patients. Results: We show that VTA degeneration might lead to system-level adjustments of catecholamine release, triggering a sequence of events leading to relevant clinical and pathological signs of AD. These changes consist first in a midfrontal-driven compensatory hyperactivation of both VTA and locus coeruleus (norepinephrine) followed, with the progression of the VTA impairment, by a downregulation of catecholamine release. These processes could then trigger the neural degeneration at the cortical and hippocampal levels, due to the chronic loss of the neuroprotective role of norepinephrine. Conclusion: Our novel hypothesis might contribute to the formulation of a wider system-level view of AD which might help to devise early diagnostic and therapeutic interventions