Abstract: In this paper we present the deep study about the Bio-
Medical Images and tag it with some basic extracting features (e.g.
color, pixel value etc). The classification is done by using a nearest
neighbor classifier with various distance measures as well as the
automatic combination of classifier results. This process selects a
subset of relevant features from a group of features of the image. It
also helps to acquire better understanding about the image by
describing which the important features are. The accuracy can be
improved by increasing the number of features selected. Various
types of classifications were evolved for the medical images like
Support Vector Machine (SVM) which is used for classifying the
Bacterial types. Ant Colony Optimization method is used for optimal
results. It has high approximation capability and much faster
convergence, Texture feature extraction method based on Gabor
wavelets etc..
Abstract: Clusters of microcalcifications in mammograms are an
important sign of breast cancer. This paper presents a complete
Computer Aided Detection (CAD) scheme for automatic detection of
clustered microcalcifications in digital mammograms. The proposed
system, MammoScan μCaD, consists of three main steps. Firstly
all potential microcalcifications are detected using a a method for
feature extraction, VarMet, and adaptive thresholding. This will also
give a number of false detections. The goal of the second step,
Classifier level 1, is to remove everything but microcalcifications.
The last step, Classifier level 2, uses learned dictionaries and sparse
representations as a texture classification technique to distinguish
single, benign microcalcifications from clustered microcalcifications,
in addition to remove some remaining false detections. The system
is trained and tested on true digital data from Stavanger University
Hospital, and the results are evaluated by radiologists. The overall
results are promising, with a sensitivity > 90 % and a low false
detection rate (approx 1 unwanted pr. image, or 0.3 false pr. image).
Abstract: This paper proposes to use ETM+ multispectral data
and panchromatic band as well as texture features derived from the
panchromatic band for land cover classification. Four texture features
including one 'internal texture' and three GLCM based textures
namely correlation, entropy, and inverse different moment were used
in combination with ETM+ multispectral data. Two data sets
involving combination of multispectral, panchromatic band and its
texture were used and results were compared with those obtained by
using multispectral data alone. A decision tree classifier with and
without boosting were used to classify different datasets. Results
from this study suggest that the dataset consisting of panchromatic
band, four of its texture features and multispectral data was able to
increase the classification accuracy by about 2%. In comparison, a
boosted decision tree was able to increase the classification accuracy
by about 3% with the same dataset.
Abstract: The main purpose of this research aimed to create tactile texture designed media for the blind used for extra learning outside classrooms in order to enhance imagination of the blind about Himmapan creatures, furthermore, the main objective of the research focused on improving the visual disabled perception to be equal to normal people. The target group of the research is blinded students studying in The Bangkok school for the blind between grade 4-6 in the second semester of 2011 who are able to read the braille language. The research methodology consisted of the field study and the documentary study related to the blind, tactile texture designed media and Himmapan creatures. 10 pictures of tactile texture designed media were created in the designing process which began after the analysis had conducted based the primary and secondary data. The works had presented to experts in the visual disabled field who evaluated the works. After approval, the works used as prototype to teach the blind. KeywordsBlind, Himmapan Creatures, Tactile Texture.
Abstract: This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.
Abstract: Random and natural textures classification is still
one of the biggest challenges in the field of image processing and
pattern recognition. In this paper, texture feature extraction using
Slant Hadamard Transform was studied and compared to other
signal processing-based texture classification schemes. A
parametric SHT was also introduced and employed for natural
textures feature extraction. We showed that a subtly modified
parametric SHT can outperform ordinary Walsh-Hadamard
transform and discrete cosine transform. Experiments were carried
out on a subset of Vistex random natural texture images using a
kNN classifier.
Abstract: The relationships between Proteolysis and soluble
calcium levels with hardness of cheese texture were investigated in
Iranian UF white cheese during 90 d ripening. Cheeses were sampled
in interior and exterior. Results showed that levels of proteolysis,
soluble calcium and hardness of cheese texture changed significantly
(p< 0.05) over ripening. Levels of proteolysis and hardness were
significantly (p< 0.05) different in interior and exterior zones of
cheeses. External zones of cheeses became softer and had higher
levels of proteolysis compared to internal zones during ripening. The
highest correlation coefficient (r2= 0.979; p
Abstract: Segmentation of a color image composed of different
kinds of regions can be a hard problem, namely to compute for an
exact texture fields. The decision of the optimum number of
segmentation areas in an image when it contains similar and/or un
stationary texture fields. A novel neighborhood-based segmentation
approach is proposed. A genetic algorithm is used in the proposed
segment-pass optimization process. In this pass, an energy function,
which is defined based on Markov Random Fields, is minimized. In
this paper we use an adaptive threshold estimation method for image
thresholding in the wavelet domain based on the generalized
Gaussian distribution (GGD) modeling of sub band coefficients. This
method called Normal Shrink is computationally more efficient and
adaptive because the parameters required for estimating the threshold
depend on sub band data energy that used in the pre-stage of
segmentation. A quad tree is employed to implement the multi
resolution framework, which enables the use of different strategies at
different resolution levels, and hence, the computation can be
accelerated. The experimental results using the proposed
segmentation approach are very encouraging.
Abstract: In this paper we propose a new content-weighted
method for full reference (FR) video quality control using a region of
interest (ROI) and wherein two-component weighted metrics for Deaf
People Video Communication. In our approach, an image is
partitioned into region of interest and into region "dry-as-dust", then
region of interest is partitioned into two parts: edges and background
(smooth regions), while the another methods (metrics) combined and
weighted three or more parts as edges, edges errors, texture, smooth
regions, blur, block distance etc. as we proposed. Using another idea
that different image regions from deaf people video communication
have different perceptual significance relative to quality. Intensity
edges certainly contain considerable image information and are
perceptually significant.
Abstract: Stairway Ushtobin Village is one of the five villages with original and sustainable architecture in Northwest of Iran along the border of Armenia, which has been able to maintain its environment and sustainable ecosystem. Studying circulation, function and scale (grand, medium and minor) of space, ratio of full and empty spaces, number and height of stairs, ratio of compound volume to luxury spaces, openings, type of local masonry (stone, mud, wood) and form of covering elements have been carried out in four houses of this village comparatively as some samples in this article, and furthermore, this article analyzes that the architectural shapes and organic texture of the village meet the needs of cold and dry climate. Finally, some efficient plans are offered suiting the present needs of the village to have a sustainable architecture.
Abstract: The textural parameters, together with appearance and
flavor, are sensory attributes of great importance for the product to be
accepted by the consumer. The objective of the present study was the
evaluation of the textural attributes of Packhams pears in the fresh
state, after drying in a chamber with forced convection at 50ºC,
lyophilized and re-hydrated. In texture analysis it was used the
method of Texture Profile Analysis (TPA). The parameters analyzed
were hardness, cohesiveness, adhesiveness, elasticity and chewiness.
From the results obtained is possible to see that the drying operation
greatly affected some textural properties of the pears, so that the
hardness diminished very much with drying, for both drying
methods.
Abstract: Image fusion aims to enhance the perception
of a scene by combining important information captured by
different sensors. Dual-Tree Complex Wavelet (DT-CWT) has been
thouroughly investigated for image fusion, since it takes advantages
of approximate shift invariance and direction selectivity. But it can
only handle limited direction information. To allow a more flexible
directional expansion for images, we propose a novel fusion scheme,
referred to as complex contourlet transform (CCT). It successfully
incorporates directional filter banks (DFB) into DT-CWT. As a result
it efficiently deal with images containing contours and textures,
whereas it retains the property of shift invariance. Experimental
results demonstrated that the method features high quality fusion
performance and can facilitate many image processing applications.
Abstract: Limited infrastructure development on peats and
organic soils is a serious geotechnical issues common to many
countries of the world especially Malaysia which distributed 1.5 mill
ha of those problematic soil. These soils have high water content and
organic content which exhibit different mechanical properties and
may also change chemically and biologically with time. Constructing
structures on peaty ground involves the risk of ground failure and
extreme settlement. Nowdays, much efforts need to be done in
making peatlands usable for construction due to increased landuse.
Deep mixing method employing cement as binders, is generally used
as measure again peaty/ organic ground failure problem. Where the
technique is widely adopted because it can improved ground
considerably in a short period of time. An understanding of
geotechnical properties as shear strength, stiffness and compressibility
behavior of these soils was requires before continues construction on
it. Therefore, 1- 1.5 meter peat soil sample from states of Johor and
an organic soil from Melaka, Malaysia were investigated. Cement
were added to the soil in the pre-mixing stage with water cement ratio
at range 3.5,7,14,140 for peats and 5,10,30 for organic soils,
essentially to modify the original soil textures and properties. The
mixtures which in slurry form will pour to polyvinyl chloride (pvc)
tube and cured at room temperature 250C for 7,14 and 28 days.
Laboratory experiments were conducted including unconfined
compressive strength and bender element , to monitor the improved
strength and stiffness of the 'stabilised mixed soils'. In between,
scanning electron miscroscopic (SEM) were observations to
investigate changes in microstructures of stabilised soils and to
evaluated hardening effect of a peat and organic soils stabilised
cement. This preliminary effort indicated that pre-mixing peat and
organic soils contributes in gaining soil strength while help the
engineers to establish a new method for those problematic ground
improvement in further practical and long term applications.
Abstract: In this paper, a novel deinterlacing algorithm is
proposed. The proposed algorithm approximates the distribution of the
luminance into a polynomial function. Instead of using one
polynomial function for all pixels, different polynomial functions are
used for the uniform, texture, and directional edge regions. The
function coefficients for each region are computed by matrix
multiplications. Experimental results demonstrate that the proposed
method performs better than the conventional algorithms.
Abstract: This study investigated the use of modified
atmosphere packaging (MAP) and different packaging to extend the
shelf life of Barbari flat bread. Three atmospheres including 70%CO2
and 30%N2, 50% CO2 and 50%N2 and a normal air as control were
used. The bread samples were packaged in three type pouches. The
shelf life was determined by appearance of mold and yeast (M +Y) in
Barbari bread samples stored at 25 ± 1°C and 38 ± 2% relative
humidity. The results showed that it is possible to prolong the shelf
life of Barbari bread from four days to about 21 days by using
modified atmosphere packaging with high carbon dioxide
concentration and high-barrier laminated and vacuum bags packages.
However, the hardness of samples kept in MAP increase significantly
by increase of carbon dioxide concentration. The correlation
coefficient (r) between headspace CO2 concentration and hardness
was 0.997, 0.997 and 0.599 for A, B and C packaging respectively.
High negative correlation coefficients were found between the crumb
moisture and the hardness values in various packaging. There were
significant negative correlation coefficients between sensory
parameters and hardness of texture.
Abstract: This paper presents and evaluates a new classification
method that aims to improve classifiers performances and speed up
their training process. The proposed approach, called labeled
classification, seeks to improve convergence of the BP (Back
propagation) algorithm through the addition of an extra feature
(labels) to all training examples. To classify every new example, tests
will be carried out each label. The simplicity of implementation is the
main advantage of this approach because no modifications are
required in the training algorithms. Therefore, it can be used with
others techniques of acceleration and stabilization. In this work, two
models of the labeled classification are proposed: the LMLP
(Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro
Fuzzy Classifier). These models are tested using Iris, wine, texture
and human thigh databases to evaluate their performances.
Abstract: Texture information plays increasingly an important
role in remotely sensed imagery classification and many pattern
recognition applications. However, the selection of relevant textural
features to improve this classification accuracy is not a straightforward
task. This work investigates the effectiveness of two Mutual
Information Feature Selector (MIFS) algorithms to select salient
textural features that contain highly discriminatory information for
multispectral imagery classification. The input candidate features are
extracted from a SPOT High Resolution Visible(HRV) image using
Wavelet Transform (WT) at levels (l = 1,2).
The experimental results show that the selected textural features
according to MIFS algorithms make the largest contribution to
improve the classification accuracy than classical approaches such
as Principal Components Analysis (PCA) and Linear Discriminant
Analysis (LDA).
Abstract: In this work a dual laser triangulation system is presented for fast building of 2.5D textured models of objects within a production line. This scanner is designed to produce data suitable for 3D completeness inspection algorithms. For this purpose two laser projectors have been used in order to considerably reduce the problem of occlusions in the camera movement direction. Results of reconstruction of electronic boards are presented, together with a comparison with a commercial system.
Abstract: The objective of this research was to identify the
vegetation-soil relationships in Nodushan arid rangelands of Yazd. 5
sites were selected for measuring the cover of plant species and soil
attributes. Soil samples were taken in 0-10 and 10-80 cm layers. The
species studied were Salsola tomentosa, Salsola arbuscula, Peganum
harmala, Zygophylum eurypterum and Eurotia ceratoides. Canonical
correspondence analysis (CCA) was used to analyze the data. Based
on the CCA results, 74.9 % of vegetation-soil variation was explained
by axis 1-3. Axis 1, 2 and 3 accounted for 27.2%, 24.9 % and 22.8%
of variance respectively. Correlation between axis 1, 2, 3 and speciesedaphic
variables were 0.995, 0.989, 0.981 respectively. Soil texture,
lime, salinity and organic matter significantly influenced the
distribution of these plant species. Determination of soil-vegetation
relationships will be useful for managing and improving rangelands
in arid and semi arid environments.
Abstract: In this work, the primary compressive strength
components of human femur trabecular bone are qualitatively
assessed using image processing and wavelet analysis. The Primary
Compressive (PC) component in planar radiographic femur trabecular
images (N=50) is delineated by semi-automatic image processing
procedure. Auto threshold binarization algorithm is employed to
recognize the presence of mineralization in the digitized images. The
qualitative parameters such as apparent mineralization and total area
associated with the PC region are derived for normal and abnormal
images.The two-dimensional discrete wavelet transforms are utilized
to obtain appropriate features that quantify texture changes in medical
images .The normal and abnormal samples of the human femur are
comprehensively analyzed using Harr wavelet.The six statistical
parameters such as mean, median, mode, standard deviation, mean
absolute deviation and median absolute deviation are derived at level
4 decomposition for both approximation and horizontal wavelet
coefficients. The correlation coefficient of various wavelet derived
parameters with normal and abnormal for both approximated and
horizontal coefficients are estimated. It is seen that in almost all cases
the abnormal show higher degree of correlation than normals. Further
the parameters derived from approximation coefficient show more
correlation than those derived from the horizontal coefficients. The
parameters mean and median computed at the output of level 4 Harr
wavelet channel was found to be a useful predictor to delineate the
normal and the abnormal groups.