The purpose of this article is to shed light on the mechanism
and the procedures of a program that classifies an input face into
any of the six basic facial expressions, which are Anger, Disgust,
Fear, Happiness, Sadness and Surprise, in addition
to normal face.
This program works by apply PCA- principal component
analysis algorithm, which is applied of one side of the face, and
depends, on contrast to the traditional studies which rely on the
whole face, on three components: Eyebrows, Eyes and Mouth.
Those out-value are used to determine the facial feature array as
an input to the neural network, and the neural network is trained by
using the back-propagation algorithm. Note that the faces used in
this study belong to people from different ages and races.
A simple and low cost thin layer chromatographic (TLC)-image analysis method was developed for rapid determination and quantification of monosodium glutamate (MSG) in some food samples. Chromatographic separation of MSG was achieved on silica gel TLC
plates, using n-butanol:glacial acetic acid:water (5:3:1, v/v/v) as the mobile phase and ninhydrin for spot detection. Image analysis of the scanned TLC plate was performed to quantify the amount of MSG, the method was validated and found to be accurate specific reliable and convenient for the analysis of MSG in some food sample.
The purpose of this article is to shed light on the mechanism
and the procedures of a neuro-fuzzy controller that classifies an
input face into any of the four facial expressions, which are
Happiness, Sadness, Anger and Fear. This program works
a
ccording to the facial characteristic points-FCP which is taken
from one side of the face, and depends, in contrast with some
traditional studies which rely on the whole face, on three
components: Eyebrows, Eyes and Mouth.
The study suggests designing a weighting model for iris features and selection of the
best ones to show the effect of weighting and selection process on system performance.
The search introduces a new weighting and fusion algorithm depends on the i
nter and intra
class differences and the fuzzy logic. The output of the algorithm is the feature’s weight of
the selected features. The designed system consists of four stages which are iris
segmentation, feature extraction, feature weighting_selection_fusion model
implementation and recognition. System suggests using region descriptors for defining the
center and radius of iris region, then the iris is cropped and transformed into the polar
coordinates via rotation and selection of radius-size pixels of fixed window from center to
circumference. Feature extraction stage is done by wavelet vertical details and the
statistical metrics of 1st and 2nd derivative of normalized iris image. At weighting and
fusion step the best features are selected and fused for classification stage which is done by
distance classifier. The algorithm is applied on CASIA database which consists of iris
images related to 250 persons. It achieved 100% segmentation precision and 98.7%
recognition rate. The results show that segmentation algorithm is robust against
illumination and rotation variations and occlusion by eye lash and lid, and the
weighting_selection_fusion algorithm enhances the system performance.
There has been a clear and rapid development in signal processing systems,
this development comes as a result of the availability of modern techniques
in electronic systems and also as a result of achieving mathematical
algorithms which were effec
tive and perfect for signal processing.
One of the most important application in signal processing is the digital
image processing techniques. Sampling process is regarded as one of the
basic and important operations in signal processing, from which we obtain
samples that can represent the original image in perfect way.
We present in this essay an affective algorithm which helps to arrange onedimensional
samples from two- dimensional samples image. This enables to
obtain a series of samples which has an ability of representing images with
concern of their general structure. Also the neighborhood correlation of
image points is respected, in addition to carrying out the subsequent
treatments with less mathematical cost.
This paper proposes a new approach for the segmentation of the retina images to obtain the optic nerve and blood vessels regions. We used retinal images from DRIVE and STARE databases which include different situations like illumination variations, d
ifferent optic nerve positions (left, right and center). Illumination problem has been solved by preprocessing stage including image histogram-based illumination correction. Next, some morphological operations were used to filter the preprocessed image to obtain the ROI region, then, the center and radius of optic nerve were determined, and the optic nerve region was extracted from the original image. In blood vessels segmentation, we applied the illumination correction and median filtering.Then the closing, subtraction and morphological operations were done to get the blood vessels image which was thresholded and thinned to get the final blood vessels image.
This research aims to developing new method for breast tumors extraction and
features detection in breast magnetic resonance images by depending on clusteringand
image processing algorithms. At the beginning, one of clustering algorithms was used f
or
image segmentation and grouping pixels by their gray scale values. Then morphological
operations were implemented in order to remove noise and undesired regions, after that
suspected areas were extracted. Finally some shape features for extracted area were
detected, this features could be very useful for tumors diagnosis. A database consisted of
96breast magnetic resonance images were used and proposed approach was appliedby
MATLAB program, and we obtainedbreast tumors extraction and its features and
compared them with the doctor's opinion .
Considered the diagnosis of diseases using image processing is one of the most
important areas of image processing techniques used in the medical field, Where is the
digital data in the field of ophthalmology focus of researchers for automatic dete
ction of
some important diseases such as diabetic retinopathy (DR).
And is defined as damage to the retina of the eye comes as serious complications and
on the human body complications resulting from diabetes in the long term and is
considered one of the most important causes of blindness in the world and cause serious
damage to the retina.
The research aims to Assess the performance of some of the methods used in the
diagnosis of diabetic retinopathy by revealing one of the most important accompanying
pests him in the retina of the eye and is the exudates and through diagnosed in images
digital fundus through image processing techniques where this detection process
contributes in helping to early detection.
Personal identification based on handprint has been gaining more attention with the
increasing needs of high level of security. In this study a novel approach for human
recognition based on handprint is proposed. Wavelet transform was used to extra
ct features
presented in the palm image based on wavelet zero-crossing method. Firstly the wavelet
transform of the whole palm image at the fourth level was worked out, which results in
four matrices; three of them are detail matrices (i.e., horizontal, vertical and diagonal) as
well as one approximation matrix. Throughout this study, only the detail matrices were
used because the required information (i.e., hand lines and curves) is included in those
matrices. Sixteen features were extracted from each detail matrix, and then arranged in one
vector. Consequently, for each palm sample a feature vector consisting of 48 input features
of the used neural network was obtained. For this purpose, a database consisting of 400
palm images belonging to 40 people at the rate of 10 images per person was built. Practical
tests outcome showed that the designed system successfully indentified 91.36% of the
tested images.
The topic of this research aims at minimizing carpet image color number
(acquired by a scanner) from 16 million colors to 5 colors, in order to restore
the shapes of the original image automatically without distortions.
For this purpose an algorit
hm was developed for the application called CDS
(Carpet Design System)- which is an application developed locally for use in
sewing carpets in General Institution of Texture Factory in Syria-, but the
results are still not acceptable, because the resulting images have a lot of
distortions, and they need some processing using an image processing
application (ex. Photoshop). This task needs more than tow weeks.
In this research work, we study the CDS algorithm, and other color
quantification algorithms, developed in research laboratories specialized in
image processing. We apply this algorithms on carpet images, after doing the
necessary modifications for tuning and adapting to our special problem. Finally
we compare the results, and suggest the best solution for the problem.
