International Journal of Computer and Applications

Fast Fourier Transform

Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

https://www.spiedigitallibrary.org/journals/Journal-of-Electronic-Imaging/volume-23/issue-2/029901/Color-Appearance-Models/10.1117/1.JEI.23.2.029901.pdf

Color Appearance Models

Biometric Recognition - Security and Privacy Concerns.

Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.

Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

Security of Digital Images is utmost important. Nowadays not only data but also digital images are increasing in a huge number. Good encryption techniques are always in need. In this paper we have proposed a Novel Framework, which is a combination of Information Hiding and Image Scrambling. The proposed framework is one of its kind and doesn’t resemble to any existing frameworks in literature. For Image scrambling R-Prime Shuffle technique has been  used.

A Novel Framework for Information Hiding and Image Encryption using Least Significant Bit Techniques

The speech signal gives various levels of information. Firstly it conveys the words or message being spoken; also on a secondary level it gives us information about the identity of the speaker. The goal of speaker recognition is to extract the identity of the person speaking.

Speaker identification by using power distribution in frequency domain

This paper proposes multi-resolution based image compression method. Wavelets have ability to analyze the signal at different resolutions to give different levels of details in the image. This property has been used in image compression and performance is compared with hybrid transform and hybrid wavelets generated using respective component transforms. Kekre transform (DKT) is paired with Hartley transform, Discrete Sine Transform and Real Fourier Transform to obtain respective hybrid wavelet transforms. Experiments show that DKT-Real DFT gives better image compression than DKT-Hartley and DKT-DST pair. Performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Average Fractional Change in Pixel Value (AFCPV). It has been observed that hybrid wavelet transform proves better than its multi-resolution analysis and hybrid transform. DKT (32x32) -RealDFT (8x8) hybrid wavelet gives the best performance with lowest values of all error metrics.

Performance Comparison of Multi -resolution Wavelet Transforms to Hybrid Transforms for Image Compression

Software architecture has been proposed as a major discipline to manage the complexity of large-scale software systems from the high abstraction levels and systemwide perspectives. It has been expected to achieve “a breathtaking capability for reliably designing systems of unprecedented size and complexity verging on a true engineering discipline” [1].

Software architecture design for airport surveillance radar data processing system using decision oriented design approach

Content-based search and retrieval of video data has become a challenging and important issue. Different methods[1,2,3] have been proposed to develop video retrievals systems to achieve better performance in terms of accuracy. The proposed technique uses wavelettransforms that areCosine Wavelet Transform, Walsh, Kekre, Slant and Hartley Wavelet Transform. The benefit of energy compaction of fewer transformedcoefficients is taken to reduce the feature vector size by taking fractional coefficients[5]of transformed frames of video. Smaller feature vector size results in less time for comparison of feature vectors resulting in faster retrieval of images. The feature vectors are extracted and coefficients sets are considered as feature vectors (100%, 6.25%, 3.125%, 1.5625%, 0.7813%, 0.39%, 0.195%, 0.097%, 0.048%, 0.024%, 0.012%, 0.006% and 0.003% of complete transformed coefficients).The database consists of 500 videos spread across 10 categories.Performance using wavelets is compared with respect to their transforms. In all considered wavelet transform, experimental result show the performance improvement in fractional coefficient of wavelet transform in content based video retrieval as compared to 100% coefficient considered.

http://www.aicit.org/IJIIP/ppl/IJIIP132PPL.pdf

H. B. Kekre

Papers

A Novel Framework for Information Hiding and Image Encryption using Least Significant Bit Techniques

Speaker identification by using power distribution in frequency domain

Performance Comparison of Multi -resolution Wavelet Transforms to Hybrid Transforms for Image Compression

Software architecture design for airport surveillance radar data processing system using decision oriented design approach

Performance Comparison of Wavelet Transform with respect to Their Transforms of CBVR