This paper identifies two novel techniques for face features extraction based on two different multi-resolution analysis tools; the first called curvelet transform while the second is waveatom transform. The resultant features are trained and tested via three improved hidden Markov Model (HMM) classifiers, such as: Structural HMM (SHMM), Deviance Information CriterionInverse Weighted Average K-...

Face Biometrics is a science of automatically identifying individuals based on their unique facial features. The paper presents neural network classifier(Radial Basis Function Network) to detect frontal views of faces. The curvelet transform, Linear Discriminant Analysis(LDA) are used to extract features from facial images first, and Radial Basis Function Network(RBFN) is used to classify the f...

We outline digital implementations of two newly developed multiscale representation systems, namely, the ridgelet and curvelet transforms. We apply these digital transforms to the problem of restoring an image from noisy data and compare our results with those obtained via well established methods based on the thresholding of wavelet coefficients. We show that the curvelet transform allows us a...

In this paper, we analyze and compare the performance of fusion methods based on four different transforms: i) wavelet transform, ii) curvelet transform, iii) contourlet transform and iv) nonsubsampled contourlet transform. Fusion framework and scheme are explained in detail, and two different sets of images are used in our experiments. Furthermore, eight different performancemetrics are adopte...

Breast cancer is one of the most important causes of increased women death rate in the world. Mammography is the most efficient approach for the early identification of breast diseases. The major objective of mammography is to identify small, non-palpable cancers during its premature stage. On the other hand, mammograms are extremely complicated to interpret being the fact that the pathological...