Abstract: Fractal based digital image compression is a specific
technique in the field of color image. The method is best suited for
irregular shape of image like snow bobs, clouds, flame of fire; tree
leaves images, depending on the fact that parts of an image often
resemble with other parts of the same image. This technique has
drawn much attention in recent years because of very high
compression ratio that can be achieved. Hybrid scheme incorporating
fractal compression and speedup techniques have achieved high
compression ratio compared to pure fractal compression. Fractal
image compression is a lossy compression method in which selfsimilarity
nature of an image is used. This technique provides high
compression ratio, less encoding time and fart decoding process. In
this paper, fractal compression with quad tree and DCT is proposed
to compress the color image. The proposed hybrid schemes require
four phases to compress the color image. First: the image is
segmented and Discrete Cosine Transform is applied to each block of
the segmented image. Second: the block values are scanned in a
zigzag manner to prevent zero co-efficient. Third: the resulting image
is partitioned as fractals by quadtree approach. Fourth: the image is
compressed using Run length encoding technique.
Abstract: In order to retrieve images efficiently from a large
database, a unique method integrating color and texture features
using genetic programming has been proposed. Opponent color
histogram which gives shadow, shade, and light intensity invariant
property is employed in the proposed framework for extracting color
features. For texture feature extraction, fast discrete curvelet
transform which captures more orientation information at different
scales is incorporated to represent curved like edges. The recent
scenario in the issues of image retrieval is to reduce the semantic gap
between user’s preference and low level features. To address this
concern, genetic algorithm combined with relevance feedback is
embedded to reduce semantic gap and retrieve user’s preference
images. Extensive and comparative experiments have been conducted
to evaluate proposed framework for content based image retrieval on
two databases, i.e., COIL-100 and Corel-1000. Experimental results
clearly show that the proposed system surpassed other existing
systems in terms of precision and recall. The proposed work achieves
highest performance with average precision of 88.2% on COIL-100
and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3%
on Corel. Thus, the experimental results confirm that the proposed
content based image retrieval system architecture attains better
solution for image retrieval.
Abstract: In this paper, an approach for the liver tumor detection
in computed tomography (CT) images is represented. The detection
process is based on classifying the features of target liver cell to
either tumor or non-tumor. Fractional differential (FD) is applied for
enhancement of Liver CT images, with the aim of enhancing texture
and edge features. Later on, a fusion method is applied to merge
between the various enhanced images and produce a variety of
feature improvement, which will increase the accuracy of
classification. Each image is divided into NxN non-overlapping
blocks, to extract the desired features. Support vector machines
(SVM) classifier is trained later on a supplied dataset different from
the tested one. Finally, the block cells are identified whether they are
classified as tumor or not. Our approach is validated on a group of
patients’ CT liver tumor datasets. The experiment results
demonstrated the efficiency of detection in the proposed technique.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: Segmentation of left ventricle (LV) from cardiac
ultrasound images provides a quantitative functional analysis of the
heart to diagnose disease. Active Shape Model (ASM) is widely used
for LV segmentation, but it suffers from the drawback that
initialization of the shape model is not sufficiently close to the target,
especially when dealing with abnormal shapes in disease. In this work,
a two-step framework is improved to achieve a fast and efficient LV
segmentation. First, a robust and efficient detection based on Hough
forest localizes cardiac feature points. Such feature points are used to
predict the initial fitting of the LV shape model. Second, ASM is
applied to further fit the LV shape model to the cardiac ultrasound
image. With the robust initialization, ASM is able to achieve more
accurate segmentation. The performance of the proposed method is
evaluated on a dataset of 810 cardiac ultrasound images that are mostly
abnormal shapes. This proposed method is compared with several
combinations of ASM and existing initialization methods. Our
experiment results demonstrate that accuracy of the proposed method
for feature point detection for initialization was 40% higher than the
existing methods. Moreover, the proposed method significantly
reduces the number of necessary ASM fitting loops and thus speeds up
the whole segmentation process. Therefore, the proposed method is
able to achieve more accurate and efficient segmentation results and is
applicable to unusual shapes of heart with cardiac diseases, such as left
atrial enlargement.
Abstract: Digital images are widely used in computer
applications. To store or transmit the uncompressed images
requires considerable storage capacity and transmission bandwidth.
Image compression is a means to perform transmission or storage of
visual data in the most economical way. This paper explains about
how images can be encoded to be transmitted in a multiplexing
time-frequency domain channel. Multiplexing involves packing
signals together whose representations are compact in the working
domain. In order to optimize transmission resources each 4 × 4
pixel block of the image is transformed by a suitable polynomial
approximation, into a minimal number of coefficients. Less than
4 × 4 coefficients in one block spares a significant amount of
transmitted information, but some information is lost. Different
approximations for image transformation have been evaluated as
polynomial representation (Vandermonde matrix), least squares +
gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev
polynomials or singular value decomposition (SVD). Results have
been compared in terms of nominal compression rate (NCR),
compression ratio (CR) and peak signal-to-noise ratio (PSNR)
in order to minimize the error function defined as the difference
between the original pixel gray levels and the approximated
polynomial output. Polynomial coefficients have been later encoded
and handled for generating chirps in a target rate of about two
chirps per 4 × 4 pixel block and then submitted to a transmission
multiplexing operation in the time-frequency domain.
Abstract: Environmental concerns about the scarcity of marine
resources are critical driving forces for firms aiming to prepare their
supply chains for sustainability. Building on previous work, this
paper highlights the implementation of good practices geared towards
sustainable operations in the seafood department, which were
pursued in an exploratory retailer case. Outcomes of the adopted
environmentally and socially acceptable fish retailing strategies,
ranged from traceability, to self-certification and eco-labelling. The
consequences for business were, as follows: stronger collaboration
and trust across the chain of custody, improvement of sponsors’
image and of consumers’ loyalty and, progress in the Greenpeace
retailers’ evaluation ranking.
Abstract: The aim of this paper is to propose a general
framework for storing, analyzing, and extracting knowledge from
two-dimensional echocardiographic images, color Doppler images,
non-medical images, and general data sets. A number of high
performance data mining algorithms have been used to carry out this
task. Our framework encompasses four layers namely physical
storage, object identification, knowledge discovery, user level.
Techniques such as active contour model to identify the cardiac
chambers, pixel classification to segment the color Doppler echo
image, universal model for image retrieval, Bayesian method for
classification, parallel algorithms for image segmentation, etc., were
employed. Using the feature vector database that have been
efficiently constructed, one can perform various data mining tasks
like clustering, classification, etc. with efficient algorithms along
with image mining given a query image. All these facilities are
included in the framework that is supported by state-of-the-art user
interface (UI). The algorithms were tested with actual patient data
and Coral image database and the results show that their performance
is better than the results reported already.
Abstract: In this paper, for an arbitrary multiplicative functional
f from the set of all upper triangular fuzzy matrices to the fuzzy
algebra, we prove that there exist a multiplicative functional F and a
functional G from the fuzzy algebra to the fuzzy algebra such that the
image of an upper triangular fuzzy matrix under f can be represented
as the product of all the images of its main diagonal elements under
F and other elements under G.
Abstract: The myocardium is composed of specialized muscle
which relies mainly on fatty acid and sugar metabolism and it is
widely contribute to the heart functioning. The changes of the cardiac
energy-producing system during heart failure have been proved using
autoradiography techniques. This study focused on evaluating sugar
and fatty acid metabolism in myocardium as cardiac energy getting
system using heart-accumulated radiopharmaceuticals. Two sets of
autoradiographs of heart cross sections of Lewis male rats were
analyzed and the time- accumulation curve obtained with use of the
MATLAB image processing software to evaluate fatty acid and sugar
metabolic functions.
Abstract: This paper introduces an effective method of
segmenting Korean text (place names in Korean) from a Korean road
sign image. A Korean advanced directional road sign is composed of
several types of visual information such as arrows, place names in
Korean and English, and route numbers. Automatic classification of
the visual information and extraction of Korean place names from the
road sign images make it possible to avoid a lot of manual inputs to a
database system for management of road signs nationwide. We
propose a series of problem-specific heuristics that correctly segments
Korean place names, which is the most crucial information, from the
other information by leaving out non-text information effectively. The
experimental results with a dataset of 368 road sign images show 96%
of the detection rate per Korean place name and 84% per road sign
image.
Abstract: We have studied the temporal characteristics of
bistable perception of the stimuli of two types: one involves
alterations in a perceived depth and another one has an ambiguous
content. We used the Necker lattice and lines of shadowed circles
ambiguously perceived either as spheres or holes as stimuli of the
first type. The Winson figure (the Eskimo/Indian picture) was a
stimulus of the second type. We have analyzed how often the
reversals occurred (reversal rate) and for how long each of the two
interpretations, or percepts, was observed during one presentation
(stability durations). For all three ambiguous images the reversal rate
and the stability durations had similar values, which provide another
evidence for a significant role of top-down processes in multistable
perception.
Abstract: The aim of this work is to detect geometrical shape
objects in an image. In this paper, the object is considered to be as a
circle shape. The identification requires find three characteristics,
which are number, size, and location of the object. To achieve the
goal of this work, this paper presents an algorithm that combines
from some of statistical approaches and image analysis techniques.
This algorithm has been implemented to arrive at the major
objectives in this paper. The algorithm has been evaluated by using
simulated data, and yields good results, and then it has been applied
to real data.
Abstract: This research aimed to investigate the relationship
between attitude towards marketing mix, brand image and consumer
behavior of the passengers of low-cost airlines service. This study
employed by quantitative research and the questionnaire was used to
collect the data from 400 sampled of the passengers who have ever
used the low-cost airline services based in Bangkok, Thailand. The
descriptive statistics and Pearson’s correlation analysis were used to
analyze data. The research results revealed that the attitude of the marketing mix
of the low-cost airline services including product, price, place,
promotion and process had related to the consumer behavior on the
aspects of duration of service and frequency of service. While, the
brand image of the low cost airline including the characteristics of
organization, service quality and company identity had related to the
consumer behavior on duration of service, frequency of service and
cost of service at the significant statistically acceptable levels.
Abstract: Rotary entrainment is a phenomenon in which the
interface of two immiscible fluids are subjected to external flux by
means of rotation. Present work reports the experimental study on
rotary motion of a horizontal cylinder between the interface of air and
water to observe the penetration of gas inside the liquid. Experiments
have been performed to establish entrainment of air mass in water
alongside the cylindrical surface. The movement of tracer and seeded
particles has been tracked to calculate the speed and path of the
entrained air inside water. Simplified particle image velocimetry
technique has been used to trace the movement of particles/tracers at
the moment they are injected inside the entrainment zone and
suspended beads have been used to replicate the particle movement
with respect to time in order to determine the flow dynamics of the
fluid along the cylinder. Present paper establishes a thorough experimental analysis of the
rotary entrainment phenomenon between air and water keeping in
interest the extent to which we can intermix the two and also to study
its entrainment trajectories.
Abstract: Liver segmentation from medical images poses more
challenges than analogous segmentations of other organs. This
contribution introduces a liver segmentation method from a series of
computer tomography images. Overall, we present a novel method for
segmenting liver by coupling density matching with shape priors.
Density matching signifies a tracking method which operates via
maximizing the Bhattacharyya similarity measure between the
photometric distribution from an estimated image region and a model
photometric distribution. Density matching controls the direction of
the evolution process and slows down the evolving contour in regions
with weak edges. The shape prior improves the robustness of density
matching and discourages the evolving contour from exceeding liver’s
boundaries at regions with weak boundaries. The model is
implemented using a modified distance regularized level set (DRLS)
model. The experimental results show that the method achieves a
satisfactory result. By comparing with the original DRLS model, it is
evident that the proposed model herein is more effective in addressing
the over segmentation problem. Finally, we gauge our performance of
our model against matrices comprising of accuracy, sensitivity, and
specificity.
Abstract: This study was aimed to measure effective transverse
relaxation rates (R2*) in the liver and muscle of normal New Zealand
White (NZW) rabbits. R2* relaxation rate has been widely used in
various hepatic diseases for iron overload by quantifying iron contents
in liver. R2* relaxation rate is defined as the reciprocal of T2*
relaxation time and mainly depends on the constituents of tissue.
Different tissues would have different R2* relaxation rates. The signal
intensity decay in Magnetic resonance imaging (MRI) may be
characterized by R2* relaxation rates. In this study, a 1.5T GE Signa
HDxt whole body MR scanner equipped with an 8-channel high
resolution knee coil was used to observe R2* values in NZW rabbit’s
liver and muscle. Eight healthy NZW rabbits weighted 2 ~ 2.5 kg were
recruited. After anesthesia using Zoletil 50 and Rompun 2% mixture,
the abdomen of rabbit was landmarked at the center of knee coil to
perform 3-plane localizer scan using fast spoiled gradient echo
(FSPGR) pulse sequence. Afterwards, multi-planar fast gradient echo
(MFGR) scans were performed with 8 various echo times (TEs) to
acquire images for R2* measurements. Regions of interest (ROIs) at
liver and muscle were measured using Advantage workstation.
Finally, the R2* was obtained by a linear regression of ln(sı) on TE.
The results showed that the longer the echo time, the smaller the signal
intensity. The R2* values of liver and muscle were 44.8 ± 10.9 s-1 and
37.4 ± 9.5 s-1, respectively. It implies that the iron concentration of
liver is higher than that of muscle. In conclusion, the more the iron
contents in tissue, the higher the R2*. The correlations between R2*
and iron content in NZW rabbits might be valuable for further
exploration.
Abstract: Magnetic Resonance Imaging (MRI) is one of the
most important medical imaging modality. Subjective assessment of
the image quality is regarded as the gold standard to evaluate MR
images. In this study, a database of 210 MR images which contains
ten reference images and 200 distorted images is presented. The
reference images were distorted with four types of distortions: Rician
Noise, Gaussian White Noise, Gaussian Blur and DCT compression.
The 210 images were assessed by ten subjects. The subjective scores
were presented in Difference Mean Opinion Score (DMOS). The
DMOS values were compared with four FR-IQA metrics. We have
used Pearson Linear Coefficient (PLCC) and Spearman Rank Order
Correlation Coefficient (SROCC) to validate the DMOS values. The
high correlation values of PLCC and SROCC shows that the DMOS
values are close to the objective FR-IQA metrics.
Abstract: The purposes of hydraulic gate are to maintain the
functions of storing and draining water. It bears long-term hydraulic
pressure and earthquake force and is very important for reservoir and
waterpower plant. The high tensile strength of steel plate is used as
constructional material of hydraulic gate. The cracks and rusts,
induced by the defects of material, bad construction and seismic
excitation and under water respectively, thus, the mechanics
phenomena of gate with crack are probing into the cause of stress
concentration, induced high crack increase rate, affect the safety and
usage of hydroelectric power plant. Stress distribution analysis is a
very important and essential surveying technique to analyze
bi-material and singular point problems. The finite difference
infinitely small element method has been demonstrated, suitable for
analyzing the buckling phenomena of welding seam and steel plate
with crack. Especially, this method can easily analyze the singularity
of kink crack. Nevertheless, the construction form and deformation
shape of some gates are three-dimensional system. Therefore, the
three-dimensional Digital Image Correlation (DIC) has been
developed and applied to analyze the strain variation of steel plate with
crack at weld joint. The proposed Digital image correlation (DIC)
technique is an only non-contact method for measuring the variation of
test object. According to rapid development of digital camera, the cost
of this digital image correlation technique has been reduced.
Otherwise, this DIC method provides with the advantages of widely
practical application of indoor test and field test without the restriction
on the size of test object. Thus, the research purpose of this research is
to develop and apply this technique to monitor mechanics crack
variations of weld steel hydraulic gate and its conformation under
action of loading. The imagines can be picked from real time
monitoring process to analyze the strain change of each loading stage.
The proposed 3-Dimensional digital image correlation method,
developed in the study, is applied to analyze the post-buckling
phenomenon and buckling tendency of welded steel plate with crack.
Then, the stress intensity of 3-dimensional analysis of different
materials and enhanced materials in steel plate has been analyzed in
this paper. The test results show that this proposed three-dimensional
DIC method can precisely detect the crack variation of welded steel
plate under different loading stages. Especially, this proposed DIC
method can detect and identify the crack position and the other flaws
of the welded steel plate that the traditional test methods hardly detect
these kind phenomena. Therefore, this proposed three-dimensional
DIC method can apply to observe the mechanics phenomena of
composite materials subjected to loading and operating.
Abstract: In this glasshouse study, we developed a new imagebased
non-destructive technique for detecting leaf P status of
different crops such as cotton, tomato and lettuce. The plants were
grown on a nutrient solution containing different P concentrations,
e.g. 0%, 50% and 100% of recommended P concentration (P0 = no P,
L; P1 = 2.5 mL 10 L-1 of P and P2 = 5 mL 10 L-1 of P). After 7 weeks
of treatment, the plants were harvested and data on leaf P contents
were collected using the standard destructive laboratory method and
at the same time leaf images were collected by a handheld crop image
sensor. We calculated leaf area, leaf perimeter and RGB (red, green
and blue) values of these images. These data were further used in
linear discriminant analysis (LDA) to estimate leaf P contents, which
successfully classified these plants on the basis of leaf P contents.
The data indicated that P deficiency in crop plants can be predicted
using leaf image and morphological data. Our proposed nondestructive
imaging method is precise in estimating P requirements of
different crop species.