Abstract: In current common research reports, salient regions
are usually defined as those regions that could present the main
meaningful or semantic contents. However, there are no uniform
saliency metrics that could describe the saliency of implicit image
regions. Most common metrics take those regions as salient regions,
which have many abrupt changes or some unpredictable
characteristics. But, this metric will fail to detect those salient useful
regions with flat textures. In fact, according to human semantic
perceptions, color and texture distinctions are the main characteristics
that could distinct different regions. Thus, we present a novel saliency
metric coupled with color and texture features, and its corresponding
salient region extraction methods. In order to evaluate the
corresponding saliency values of implicit regions in one image, three
main colors and multi-resolution Gabor features are respectively used
for color and texture features. For each region, its saliency value is
actually to evaluate the total sum of its Euclidean distances for other
regions in the color and texture spaces. A special synthesized image
and several practical images with main salient regions are used to
evaluate the performance of the proposed saliency metric and other
several common metrics, i.e., scale saliency, wavelet transform
modulus maxima point density, and important index based metrics.
Experiment results verified that the proposed saliency metric could
achieve more robust performance than those common saliency
metrics.
Abstract: Wavelet transform provides several important
characteristics which can be used in a texture analysis and
classification. In this work, an efficient texture classification method,
which combines concepts from wavelet and co-occurrence matrices,
is presented. An Euclidian distance classifier is used to evaluate the
various methods of classification. A comparative study is essential to
determine the ideal method. Using this conjecture, we developed a
novel feature set for texture classification and demonstrate its
effectiveness
Abstract: In this paper we present a new method for coin
identification. The proposed method adopts a hybrid scheme using
Eigenvalues of covariance matrix, Circular Hough Transform (CHT)
and Bresenham-s circle algorithm. The statistical and geometrical
properties of the small and large Eigenvalues of the covariance
matrix of a set of edge pixels over a connected region of support are
explored for the purpose of circular object detection. Sparse matrix
technique is used to perform CHT. Since sparse matrices squeeze
zero elements and contain only a small number of non-zero elements,
they provide an advantage of matrix storage space and computational
time. Neighborhood suppression scheme is used to find the valid
Hough peaks. The accurate position of the circumference pixels is
identified using Raster scan algorithm which uses geometrical
symmetry property. After finding circular objects, the proposed
method uses the texture on the surface of the coins called texton,
which are unique properties of coins, refers to the fundamental micro
structure in generic natural images. This method has been tested on
several real world images including coin and non-coin images. The
performance is also evaluated based on the noise withstanding
capability.
Abstract: An unsupervised classification algorithm is derived
by modeling observed data as a mixture of several mutually
exclusive classes that are each described by linear combinations of
independent non-Gaussian densities. The algorithm estimates the
data density in each class by using parametric nonlinear functions
that fit to the non-Gaussian structure of the data. This improves
classification accuracy compared with standard Gaussian mixture
models. When applied to textures, the algorithm can learn basis
functions for images that capture the statistically significant structure
intrinsic in the images. We apply this technique to the problem of
unsupervised texture classification and segmentation.
Abstract: For the communication between human and computer
in an interactive computing environment, the gesture recognition is
studied vigorously. Therefore, a lot of studies have proposed efficient
methods about the recognition algorithm using 2D camera captured
images. However, there is a limitation to these methods, such as the
extracted features cannot fully represent the object in real world.
Although many studies used 3D features instead of 2D features for
more accurate gesture recognition, the problem, such as the processing
time to generate 3D objects, is still unsolved in related researches.
Therefore we propose a method to extract the 3D features combined
with the 3D object reconstruction. This method uses the modified
GPU-based visual hull generation algorithm which disables unnecessary
processes, such as the texture calculation to generate three kinds
of 3D projection maps as the 3D feature: a nearest boundary, a farthest
boundary, and a thickness of the object projected on the base-plane. In
the section of experimental results, we present results of proposed
method on eight human postures: T shape, both hands up, right hand
up, left hand up, hands front, stand, sit and bend, and compare the
computational time of the proposed method with that of the previous
methods.
Abstract: Echocardiography imaging is one of the most common diagnostic tests that are widely used for assessing the abnormalities of the regional heart ventricle function. The main goal of the image enhancement task in 2D-echocardiography (2DE) is to solve two major anatomical structure problems; speckle noise and low quality. Therefore, speckle noise reduction is one of the important steps that used as a pre-processing to reduce the distortion effects in 2DE image segmentation. In this paper, we present the common filters that based on some form of low-pass spatial smoothing filters such as Mean, Gaussian, and Median. The Laplacian filter was used as a high-pass sharpening filter. A comparative analysis was presented to test the effectiveness of these filters after being applied to original 2DE images of 4-chamber and 2-chamber views. Three statistical quantity measures: root mean square error (RMSE), peak signal-to-ratio (PSNR) and signal-tonoise ratio (SNR) are used to evaluate the filter performance quantitatively on the output enhanced image.
Abstract: Using a texture database, a statistical estimation of
spring-back was conducted in this study on the basis of statistical
analysis. Both spring-back in bending deformation and experimental
data related to the crystal orientation show significant dispersion.
Therefore, a probabilistic statistical approach was established for the
proper quantification of these values. Correlation was examined
among the parameters F(x) of spring-back, F(x) of the buildup fraction
to three orientations after 92° bending, and F(x) at an as-received part
on the basis of the three-parameter Weibull distribution. Consequent
spring-back estimation using a texture database yielded excellent
estimates compared with experimental values.
Abstract: Most of the image watermarking methods, using the properties of the human visual system (HVS), have been proposed in literature. The component of the visual threshold is usually related to either the spatial contrast sensitivity function (CSF) or the visual masking. Especially on the contrast masking, most methods have not mention to the effect near to the edge region. Since the HVS is sensitive what happens on the edge area. This paper proposes ultrasound image watermarking using the visual threshold corresponding to the HVS in which the coefficients in a DCT-block have been classified based on the texture, edge, and plain area. This classification method enables not only useful for imperceptibility when the watermark is insert into an image but also achievable a robustness of watermark detection. A comparison of the proposed method with other methods has been carried out which shown that the proposed method robusts to blockwise memoryless manipulations, and also robust against noise addition.
Abstract: In this paper spatial variability of some chemical and
physical soil properties were investigated in mountain rangelands of
Nesho, Mazandaran province, Iran. 110 soil samples from 0-30 cm
depth were taken with systematic method on grid 30×30 m2 in
regions with different vegetation cover and transported to laboratory.
Then soil chemical and physical parameters including Acidity (pH),
Electrical conductivity, Caco3, Bulk density, Particle density, total
phosphorus, total Nitrogen, available potassium, Organic matter,
Saturation moisture, Soil texture (percentage of sand, silt and clay),
Sodium, Calcium, magnesium were measured in laboratory. Data
normalization was performed then was done statistical analysis for
description of soil properties and geostatistical analysis for indication
spatial correlation between these properties and were perpetrated
maps of spatial distribution of soil properties using Kriging method.
Results indicated that in the study area Saturation moisture and
percentage of Sand had highest and lowest spatial correlation
respectively.
Abstract: The paper proposes a novel technique for iris
recognition using texture and phase features. Texture features are
extracted on the normalized iris strip using Haar Wavelet while phase
features are obtained using LOG Gabor Wavelet. The matching
scores generated from individual modules are combined using sum of
score technique. The system is tested on database obtained from Bath
University and Indian Institute of Technology Kanpur and is giving
an accuracy of 95.62% and 97.66% respectively. The FAR and FRR
of the combined system is also reduced comparatively.
Abstract: Attempts to add fibre and polyphenols (PPs) into
popular beverages present challenges related to the properties of
finished products such as smoothies. Consumer acceptability,
viscosity and phenolic composition of smoothies containing high
levels of fruit fibre (2.5-7.5 g per 300 mL serve) and PPs (250-750
mg per 300 mL serve) were examined. The changes in total
extractable PP, vitamin C content, and colour of selected smoothies
over a storage stability trial (4°C, 14 days) were compared. A set of
acidic aqueous model beverages were prepared to further examine
the effect of two different heat treatments on the stability and
extractability of PPs. Results show that overall consumer
acceptability of high fibre and PP smoothies was low, with average
hedonic scores ranging from 3.9 to 6.4 (on a 1-9 scale). Flavour,
texture and overall acceptability decreased as fibre and polyphenol
contents increased, with fibre content exerting a stronger effect.
Higher fibre content resulted in greater viscosity, with an elevated PP
content increasing viscosity only slightly. The presence of fibre also
aided the stability and extractability of PPs after heating. A reduction
of extractable PPs, vitamin C content and colour intensity of
smoothies was observed after a 14-day storage period at 4°C. Two
heat treatments (75°C for 45 min or 85°C for 1 min) that are
normally used for beverage production, did not cause significant
reduction of total extracted PPs. It is clear that high levels of added
fibre and PPs greatly influence the consumer appeal of smoothies,
suggesting the need to develop novel formulation and processing
methods if a satisfactory functional beverage is to be developed
incorporating these ingredients.
Abstract: In this paper, a new reversible watermarking method is presented that reduces the size of a stereoscopic image sequence while keeping its content visible. The proposed technique embeds the residuals of the right frames to the corresponding frames of the left sequence, halving the total capacity. The residual frames may result in after a disparity compensated procedure between the two video streams or by a joint motion and disparity compensation. The residuals are usually lossy compressed before embedding because of the limited embedding capacity of the left frames. The watermarked frames are visible at a high quality and at any instant the stereoscopic video may be recovered by an inverse process. In fact, the left frames may be exactly recovered whereas the right ones are slightly distorted as the residuals are not embedded intact. The employed embedding method reorders the left frame into an array of consecutive pixel pairs and embeds a number of bits according to their intensity difference. In this way, it hides a number of bits in intensity smooth areas and most of the data in textured areas where resulting distortions are less visible. The experimental evaluation demonstrates that the proposed scheme is quite effective.
Abstract: An important step in studying the statistics of
fingerprint minutia features is to reliably extract minutia features from
the fingerprint images. A new reliable method of computation for
minutiae feature extraction from fingerprint images is presented. A
fingerprint image is treated as a textured image. An orientation flow
field of the ridges is computed for the fingerprint image. To
accurately locate ridges, a new ridge orientation based computation
method is proposed. After ridge segmentation a new method of
computation is proposed for smoothing the ridges. The ridge skeleton
image is obtained and then smoothed using morphological operators
to detect the features. A post processing stage eliminates a large
number of false features from the detected set of minutiae features.
The detected features are observed to be reliable and accurate.
Abstract: In this paper the design of maximally flat linear phase
finite impulse response (FIR) filters is considered. The problem is
handled with totally two different approaches. The first one is
completely deterministic numerical approach where the problem is
formulated as a Linear Complementarity Problem (LCP). The other
one is based on a combination of Markov Random Fields (MRF's)
approach with messy genetic algorithm (MGA). Markov Random
Fields (MRFs) are a class of probabilistic models that have been
applied for many years to the analysis of visual patterns or textures.
Our objective is to establish MRFs as an interesting approach to
modeling messy genetic algorithms. We establish a theoretical result
that every genetic algorithm problem can be characterized in terms of
a MRF model. This allows us to construct an explicit probabilistic
model of the MGA fitness function and introduce the Ising MGA.
Experimentations done with Ising MGA are less costly than those
done with standard MGA since much less computations are involved.
The least computations of all is for the LCP. Results of the LCP,
random search, random seeded search, MGA, and Ising MGA are
discussed.
Abstract: Surface metrology with image processing is a challenging task having wide applications in industry. Surface roughness can be evaluated using texture classification approach. Important aspect here is appropriate selection of features that characterize the surface. We propose an effective combination of features for multi-scale and multi-directional analysis of engineering surfaces. The features include standard deviation, kurtosis and the Canny edge detector. We apply the method by analyzing the surfaces with Discrete Wavelet Transform (DWT) and Dual-Tree Complex Wavelet Transform (DT-CWT). We used Canberra distance metric for similarity comparison between the surface classes. Our database includes the surface textures manufactured by three machining processes namely Milling, Casting and Shaping. The comparative study shows that DT-CWT outperforms DWT giving correct classification performance of 91.27% with Canberra distance metric.
Abstract: In this paper, a robust digital image watermarking
scheme for copyright protection applications using the singular value
decomposition (SVD) is proposed. In this scheme, an entropy
masking model has been applied on the host image for the texture
segmentation. Moreover, the local luminance and textures of the host
image are considered for watermark embedding procedure to
increase the robustness of the watermarking scheme. In contrast to all
existing SVD-based watermarking systems that have been designed
to embed visual watermarks, our system uses a pseudo-random
sequence as a watermark. We have tested the performance of our
method using a wide variety of image processing attacks on different
test images. A comparison is made between the results of our
proposed algorithm with those of a wavelet-based method to
demonstrate the superior performance of our algorithm.
Abstract: In the area where the high quality water is not
available, unconventional water sources are used to irrigate.
Household leachate is one of the sources which are used in dry and
semi dry areas in order to water the barer trees and plants. It meets
the plants needs and also has some effects on the soil, but at the same
time it might cause some problems as well. This study in order to
evaluate the effect of using Compost leachate on the density of soil
iron in form of a statistical pattern called ''Split Plot'' by using two
main treatments, one subsidiary treatment and three repetitions of the
pattern in a three month period. The main N treatments include:
irrigation using well water as a blank treatments and the main I
treatments include: irrigation using leachate and well water
concurrently. Some subsidiary treatments were DI (Drop Irrigation)
and SDI (Sub Drop Irrigation). Then in the established plots, 36
biannual pine and cypress shrubs were randomly grown. Two months
later the treatment begins. The results revealed that there was a
significant variation between the main treatment and the instance
regarding pH decline in the soil which was related to the amount of
leachate injected into the soil. After some time and using leachate the
pH level fell, as much as 0.46 and also increased due to the great
amounts of leachate. The underneath drop irrigation ends in better
results than sub drop irrigation since it keeps the soil texture fixed.
Abstract: Kepsut-Dursunbey volcanic field (KDVF) is located
in NW Turkey and contains various products of the post-collisional
Neogene magmatic activity. Two distinct volcanic suites have been
recognized; the Kepsut volcanic suite (KVS) and the Dursunbey
volcanic suite (DVS). The KVS includes basaltic trachyandesitebasaltic
andesite-andesite lavas and associated pyroclastic rocks. The
DVS consists of dacite-rhyodacite lavas and extensive pumice-ash
fall and flow deposits. Petrographical features (i.e. existence of
xenocrysts, glomerocrysts, and mixing-compatible textures) and
mineral chemistry of phenocryst assemblages of both suites provide
evidence for magma mixing/AFC. Calculated crystallization
pressures and temperatures give values of 5.7–7.0 kbar and 927–982
°C for the KVS and 3.7–5.3 kbar and 783-787°C for the DVS,
indicating separate magma reservoirs and crystallization in magma
chambers at deep and mid crustal levels, respectively. These
observations support the establishment and evolution of KDVF
magma system promoted by episodic basaltic inputs which may
generate and mix with crustal melts.
Abstract: The proposed system identifies the species of the wood
using the textural features present in its barks. Each species of a wood
has its own unique patterns in its bark, which enabled the proposed
system to identify it accurately. Automatic wood recognition system
has not yet been well established mainly due to lack of research in this
area and the difficulty in obtaining the wood database. In our work, a
wood recognition system has been designed based on pre-processing
techniques, feature extraction and by correlating the features of those
wood species for their classification. Texture classification is a problem
that has been studied and tested using different methods due to its
valuable usage in various pattern recognition problems, such as wood
recognition, rock classification. The most popular technique used
for the textural classification is Gray-level Co-occurrence Matrices
(GLCM). The features from the enhanced images are thus extracted
using the GLCM is correlated, which determines the classification
between the various wood species. The result thus obtained shows a
high rate of recognition accuracy proving that the techniques used in
suitable to be implemented for commercial purposes.
Abstract: This paper describes an optimal approach for feature
subset selection to classify the leaves based on Genetic Algorithm
(GA) and Kernel Based Principle Component Analysis (KPCA). Due
to high complexity in the selection of the optimal features, the
classification has become a critical task to analyse the leaf image
data. Initially the shape, texture and colour features are extracted
from the leaf images. These extracted features are optimized through
the separate functioning of GA and KPCA. This approach performs
an intersection operation over the subsets obtained from the
optimization process. Finally, the most common matching subset is
forwarded to train the Support Vector Machine (SVM). Our
experimental results successfully prove that the application of GA
and KPCA for feature subset selection using SVM as a classifier is
computationally effective and improves the accuracy of the classifier.