Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Discrete Wavelet Transform

Fingerprints Identification Using Contourlet Transform

T.M. Salman; M.K.M. Al-Azawi

Engineering and Technology Journal, 2017, Volume 35, Issue 3, Pages 282-288

This paper suggests the use of contourlet transform for efficient feature extraction of fingerprints for identification purposes. Back propagated neural network is then used as a classifier. Two fingerprints databases are used to test the system. These include fingerprints images with different positions, rotations and scales to test the robustness of the system. Computer simulation results show that the proposed contourlet transform outperforms the classical wavelet method. Where an identification rate of 94.4% was obtained using contourlet transform compare with 87% using wavelet transform for standard FVC2002 database.

Hybrid Algorithm to Improve Robustness of Image Watermarking

Ammar Fakhri Mahdi

Engineering and Technology Journal, 2015, Volume 33, Issue 3, Pages 564-570

Watermarking is the process of embedding digital information into any multimedia data such as an image, audio or video file in such a way that intruder cannot be able to trace the signal to protect copyright of intellectual property of the owners. In this paper, a new hybrid watermarking algorithm is proposed by using discrete wavelet transform (DWT) and slant let transform (SLT) which are the most robust to attack rather than least significant bit (LSB) for the protection of digital Images. Embedding watermark is accomplished in still images (JPEG) true color in high frequency sub bands by combining the two transforms. The watermark is applied to many images, and the results showed that the proposed algorithm offers good performance and has good robustness against different types of attacks and it does not affect the transparency of the cover image.

Face Recognition using DWT with HMM

Eyad I. Abbas; Hameed R. Farhan

Engineering and Technology Journal, 2012, Volume 30, Issue 1, Pages 142-154

This paper presents an efficient face recognition system based on Hidden Markov
Model (HMM) and the simplest type “Haar” of the Discrete Wavelet Transform
(DWT). The one dimensional ergodic HMM with Gaussian outputs, which represent
the simplest and robust type of HMM, is used in the proposed work. A novel method
is introduced for selecting the training images implemented by choosing the images
that have the odd identifying numbers from the database. Some of these images are
replaced according to the trial-and-error results. The proposed work achieves the
maximum recognition rate (100%), where the experiments are carried out on the ORL
face database.

A Recursive Algorithm to Hide Three Secret Images In One Image Using Wavelet Transform

Yasmin Muwafaq kassim

Engineering and Technology Journal, 2012, Volume 30, Issue 2, Pages 238-260

This paper presents an algorithm based on wavelet transform to hide three
secret colored or gray-scale images with different sizes in one colored cover
image. The algorithm takes level1 wavelet transformation for the cover image and
level2 wavelet transformation for the coefficients resultant from level1. The
algorithm begins to divide and transpose the secret images into multiple sub bands,
then imbedding them into the coefficient parts resulting from level2. The
embedding depends upon a variable threshold which begins with a very small
value. Here the algorithm ensures the embedding of all the pixels values of the sub
band, if it is not, the operation will be repeated with a larger threshold value until
all the pixels are embedded. Also the pixel's value will not be embedded directly,
the difference between the cover and the secret pixel value will be embedded
instead of it after some manipulation (mathematical operations). All of these
factors (divide and transpose the secret images, the variable threshold for each sub
band and changes on the embedded pixels) increase the robustness and quality of
the algorithm. The resultant stego image and the extracted secret images are very
close to the original one with high PSNR, high Correlation, low Normal Absolute
Error and low Maximum Difference.