Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Max


Eye Diseases Classification Using Back Propagation Artificial Neural Network

Hanaa M. Ahmed; Shrooq R. Hameed

Engineering and Technology Journal, 2021, Volume 39, Issue 1B, Pages 11-20
DOI: 10.30684/etj.v39i1B.1363

A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases. Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification. The comparative study states that the features extracted from HMMD color space is better than other features like Histogram of Oriented Gradient (HOG) features and give the same accuracy as features extracted directly from medical expert recorded symptoms. The proposed method is applied on external eye diseases data set consisting of 416 images with an accuracy rate of 85.26315%, which is the major result that was achieved in this study.

Principal Component Analysis Based Wavelet Transform

Hana; a M. Salman

Engineering and Technology Journal, 2012, Volume 30, Issue 9, Pages 1538-1549

The principal component analysis (PCA) is a valuable statistical means,
implemented in time domain that has found application in many fields such as face
recognition and image compression, and is a common technique for finding patterns in
data of high dimension. This paper investigates the ability to implement PCA in
frequency domain, by using the wavelet transform (WT), and evaluate its effectiveness
based on face recognition as a means to find patterns in data. The basic idea of
frequency domain implementation of the PCA refers to the correlation
implementation using wavelet transform.
The Min-max is invoked to increase wavelet based eigenface robustness to
variations in facial geometry and illumination. Two face images are contrast in terms
of their correlation distance. A threshold is used to restrict the impostor face image
from being identified. Experimental results point up the effectiveness of a new method
in either using varying (noisy images, unknown images, face expressions, illumine,
and scales ).

Spectral Eigenface Representation for Human Identification

Hana; a M. Salman

Engineering and Technology Journal, 2010, Volume 28, Issue 19, Pages 5960-5972

Human identification based on face images, as physical biometric means, plays an
imperative role in many applications area. The methods for human identification using
face image uses either part of the face, all face, or mixture from these methods, in either
time domain or frequency domain. This paper investigate the ability to implement the
eigenface in frequency domain, the result spectral eigenface is utilize as a feature vector
means for human identification. The converting from eigenface implementation in time
domain, into spectral eigenface implementation in frequency domain, is based on
implemented the correlation by using FFT. The Min-max is invoked as normalization
techniques that increase spectral eigenface robustness to variations in facial geometry
and illumination. Two face images are contrast in terms of their correlation distance. A
threshold (10.50x107) is used to restrict the impostor face image from being identified.
The experimental results point up the effectiveness of a new method in either using
varying (noisy images, unknown image, face expressions, illumine, and scale s), with
identification value of 100%.