Abstract: In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.
Abstract: Texture is an important characteristic in real and
synthetic scenes. Texture analysis plays a critical role in inspecting
surfaces and provides important techniques in a variety of
applications. Although several descriptors have been presented to
extract texture features, the development of object recognition is still a
difficult task due to the complex aspects of texture. Recently, many
robust and scaling-invariant image features such as SIFT, SURF and
ORB have been successfully used in image retrieval and object
recognition. In this paper, we have tried to compare the performance
for texture classification using these feature descriptors with k-means
clustering. Different classifiers including K-NN, Naive Bayes, Back
Propagation Neural Network , Decision Tree and Kstar were applied in
three texture image sets － UIUCTex, KTH-TIPS and Brodatz,
respectively. Experimental results reveal SIFTS as the best average
accuracy rate holder in UIUCTex, KTH-TIPS and SURF is
advantaged in Brodatz texture set. BP neuro network works best in the
test set classification among all used classifiers.
Abstract: Automatic License plate recognition (ALPR) is a technology which recognizes the registration plate or number plate or License plate of a vehicle. In this paper, an Indian vehicle number plate is mined and the characters are predicted in efficient manner. ALPR involves four major technique i) Pre-processing ii) License Plate Location Identification iii) Individual Character Segmentation iv) Character Recognition. The opening phase, named pre-processing helps to remove noises and enhances the quality of the image using the conception of Morphological Operation and Image subtraction. The second phase, the most puzzling stage ascertain the location of license plate using the protocol Canny Edge detection, dilation and erosion. In the third phase, each characters characterized by Connected Component Approach (CCA) and in the ending phase, each segmented characters are conceptualized using cross correlation template matching- a scheme specifically appropriate for fixed format. Major application of ALPR is Tolling collection, Border Control, Parking, Stolen cars, Enforcement, Access Control, Traffic control. The database consists of 500 car images taken under dissimilar lighting condition is used. The efficiency of the system is 97%. Our future focus is Indian Vehicle License Plate Validation (Whether License plate of a vehicle is as per Road transport and highway standard).
Abstract: In this study, the potential benefits of playing action
video game among congenitally deaf and dumb subjects is reported in
terms of EEG ratio indices. The frontal and occipital lobes are
associated with development of motor skills, cognition, and visual
information processing and color recognition. The sixteen hours of
First-Person shooter action video game play resulted in the increase
of the ratios β/(α+θ) and β/θ in frontal and occipital lobes. This can
be attributed to the enhancement of certain aspect of cognition among
deaf and dumb subjects.
Abstract: In the deep south of Thailand, checkpoints for people
verification are necessary for the security management of risk zones,
such as official buildings in the conflict area. In this paper, we
propose an automatic checkpoint system that verifies persons using
information from ID cards and facial features. The methods for a
person’s information abstraction and verification are introduced
based on useful information such as ID number and name, extracted
from official cards, and facial images from videos. The proposed
system shows promising results and has a real impact on the local
society.
Abstract: Myoelectric control system is the fundamental
component of modern prostheses, which uses the myoelectric signals
from an individual’s muscles to control the prosthesis movements.
The surface electromyogram signal (sEMG) being noninvasive has
been used as an input to prostheses controllers for many years.
Recent technological advances has led to the development of
implantable myoelectric sensors which enable the internal
myoelectric signal (MES) to be used as input to these prostheses
controllers. The intramuscular measurement can provide focal
recordings from deep muscles of the forearm and independent signals
relatively free of crosstalk thus allowing for more independent
control sites. However, little work has been done to compare the two
inputs. In this paper we have compared the classification accuracy of
six pattern recognition based myoelectric controllers which use
surface myoelectric signals recorded using untargeted (symmetric)
surface electrode arrays to the same controllers with multichannel
intramuscular myolectric signals from targeted intramuscular
electrodes as inputs. There was no significant enhancement in the
classification accuracy as a result of using the intramuscular EMG
measurement technique when compared to the results acquired using
the surface EMG measurement technique. Impressive classification
accuracy (99%) could be achieved by optimally selecting only five
channels of surface EMG.
Abstract: The beginning of 21st century has witnessed new
advancements in the design and use of new materials for biosensing
applications, from nano to macro, protein to tissue. Traditional
analytical methods lack a complete toolset to describe the
complexities introduced by living systems, pathological relations,
discrete hierarchical materials, cross-phase interactions, and
structure-property dependencies. Materiomics – via systematic
molecular dynamics (MD) simulation – can provide structureprocess-
property relations by using a materials science approach
linking mechanisms across scales and enables oriented biosensor
design. With this approach, DNA biosensors can be utilized to detect
disease biomarkers present in individuals’ breath such as acetone for
diabetes. Our wireless sensor array based on single-stranded DNA
(ssDNA)-decorated single-walled carbon nanotubes (SWNT) has
successfully detected trace amount of various chemicals in vapor
differentiated by pattern recognition. Here, we present how MD
simulation can revolutionize the way of design and screening of DNA
aptamers for targeting biomarkers related to oral diseases and oral
health monitoring. It demonstrates great potential to be utilized to
build a library of DNDA sequences for reliable detection of several
biomarkers of one specific disease, and as well provides a new
methodology of creating, designing, and applying of biosensors.
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: River Hindon is an important river catering the
demand of highly populated rural and industrial cluster of western
Uttar Pradesh, India. Water quality of river Hindon is deteriorating at
an alarming rate due to various industrial, municipal and agricultural
activities. The present study aimed at identifying the pollution
sources and quantifying the degree to which these sources are
responsible for the deteriorating water quality of the river. Various
water quality parameters, like pH, temperature, electrical
conductivity, total dissolved solids, total hardness, calcium, chloride,
nitrate, sulphate, biological oxygen demand, chemical oxygen
demand, and total alkalinity were assessed. Water quality data
obtained from eight study sites for one year has been subjected to the
two multivariate techniques, namely, principal component analysis
and cluster analysis. Principal component analysis was applied with
the aim to find out spatial variability and to identify the sources
responsible for the water quality of the river. Three Varifactors were
obtained after varimax rotation of initial principal components using
principal component analysis. Cluster analysis was carried out to
classify sampling stations of certain similarity, which grouped eight
different sites into two clusters. The study reveals that the
anthropogenic influence (municipal, industrial, waste water and
agricultural runoff) was the major source of river water pollution.
Thus, this study illustrates the utility of multivariate statistical
techniques for analysis and elucidation of multifaceted data sets,
recognition of pollution sources/factors and understanding
temporal/spatial variations in water quality for effective river water
quality management.
Abstract: The purposes of this research are to make comparisons in
respect of the behaviors on the use of the services of metered taxi
classified by the demographic factor and to study the influence of the
recognition on service quality having the effect on usage behaviors of
metered taxi services of consumers in Bangkok Metropolitan Areas. The
samples used in this research were 400 metered taxi service users in
Bangkok Metropolitan Areas and questionnaire was used as the tool for
collecting the data. Analysis statistics are mean and multiple regression
analysis. Results of the research revealed that the consumers recognize the
overall quality of services in each aspect include tangible aspects of the
service, responses to customers, assurance on the confidence,
understanding and knowing of customers which is rated at the moderate
level except the aspect of the assurance on the confidence and
trustworthiness which are rated at a high level. For the result of
hypothetical test, it is found that the quality in providing the services on
the aspect of the assurance given to the customers has the effect on the
usage behaviors of metered taxi services and the aspect of the frequency
on the use of the services per month which in this connection. Such
variable can forecast at one point nine percent (1.9%). In addition, quality
in providing the services and the aspect of the responses to customers
have the effect on the behaviors on the use of metered taxi services on the
aspect of the expenses on the use of services per month which in this
connection, such variable can forecast at two point one percent (2.1%).
Abstract: In this paper, we propose the variational EM inference
algorithm for the multi-class Gaussian process classification model
that can be used in the field of human behavior recognition. This
algorithm can drive simultaneously both a posterior distribution of a
latent function and estimators of hyper-parameters in a Gaussian
process classification model with multiclass. Our algorithm is based
on the Laplace approximation (LA) technique and variational EM
framework. This is performed in two steps: called expectation and
maximization steps. First, in the expectation step, using the Bayesian
formula and LA technique, we derive approximately the posterior
distribution of the latent function indicating the possibility that each
observation belongs to a certain class in the Gaussian process
classification model. Second, in the maximization step, using a derived
posterior distribution of latent function, we compute the maximum
likelihood estimator for hyper-parameters of a covariance matrix
necessary to define prior distribution for latent function. These two
steps iteratively repeat until a convergence condition satisfies.
Moreover, we apply the proposed algorithm with human action
classification problem using a public database, namely, the KTH
human action data set. Experimental results reveal that the proposed
algorithm shows good performance on this data set.
Abstract: Noninvasive diagnostics of diseases via breath
analysis has attracted considerable scientific and clinical interest for
many years and become more and more promising with the rapid
advancements in nanotechnology and biotechnology. The volatile
organic compounds (VOCs) in exhaled breath, which are mainly
blood borne, particularly provide highly valuable information about
individuals’ physiological and pathophysiological conditions.
Additionally, breath analysis is noninvasive, real-time, painless, and
agreeable to patients. We have developed a wireless sensor array
based on single-stranded DNA (ssDNA)-functionalized single-walled
carbon nanotubes (SWNT) for the detection of a number of
physiological indicators in breath. Seven DNA sequences were used
to functionalize SWNT sensors to detect trace amount of methanol,
benzene, dimethyl sulfide, hydrogen sulfide, acetone, and ethanol,
which are indicators of heavy smoking, excessive drinking, and
diseases such as lung cancer, breast cancer, and diabetes. Our test
results indicated that DNA functionalized SWNT sensors exhibit
great selectivity, sensitivity, and repeatability; and different
molecules can be distinguished through pattern recognition enabled
by this sensor array. Furthermore, the experimental sensing results
are consistent with the Molecular Dynamics simulated ssDNAmolecular
target interaction rankings. Thus, the DNA-SWNT sensor
array has great potential to be applied in chemical or biomolecular
detection for the noninvasive diagnostics of diseases and personal
health monitoring.
Abstract: Intermediate cities which also called medium size
cities have an important role in the process of globalization. It is
argued that, in some cases this type of cities may be depopulated or in
otherwise may be transformed as the periphery of metropolitans, so
that the personal identity of the city and its local cultural heritage
could suffer from its neighbor metropolitan. Over the last decades,
the role of tourism in the development process and the cultural
heritage has increased. The impact of tourism on socioeconomic
growth makes motivation for the study of tourism development in
regional and urban planning process. There are evidences that
tourism has a positive impact in local development and makes
economic motivations for cultural heritage protection. In this study,
by considering the role of tourism in local development, especially by
its economic and socio-cultural impacts, it is tried to introduce a
strategy for tourism development through a method of urban planning
for intermediate cities called as Base plan. Damavand is an
intermediate city located in Tehran province, Iran with a high
potential in tourism by its local specific characteristic like social
structure, antiquities and natural attractions. It’s selected as a suitable
case study for intended strategy which is a combination of urban
planning and tourism development methods. Focusing on recognition
of the historical and cultural heritage of Damavand, in this paper
through “base plan methodology” a strategy of urban planning
toward tourism development is prepared in order to make tourism
development as a support for cultural heritage of this city.
Abstract: The article includes the results and conclusions from
empirical researches that had been done. The research focuses on the
impact of investments made in small and medium-sized enterprises
financed from EU funds on the competitiveness of these companies.
The researches includes financial results in sales revenue and net
income, expenses, and many other new products/services on offer,
higher quality products and services, more modern methods of
production, innovation in management processes, increase in the
number of customers, increase in market share, increase in
profitability of production and provision of services. The main
conclusions are that, companies with direct investments under this
measure shall apply the modern methods of production. The
consequence of this is to increase the quality of our products and
services. Furthermore, both small and medium-sized enterprises have
introduced new products and services. Investments were carried out,
thus enabling better work organization in enterprises. Entrepreneurs
would guarantee higher quality of service, which would result in
better relationships with their customers, what is more, noting the rise
in number of clients. More than half of the companies indicated that
the investments contributed to the increase in market share. Same
thing as for market reach and brand recognition of particular
company. An interesting finding is that, investments in small
enterprises were more effective than medium-sized enterprises.
Abstract: Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.
Abstract: A growing demand is felt today for realistic 3D
models enabling the cognition and popularization of historical-artistic
heritage. Evaluation and preservation of Cultural Heritage is
inextricably connected with the innovative processes of gaining,
managing, and using knowledge. The development and perfecting of
techniques for acquiring and elaborating photorealistic 3D models,
made them pivotal elements for popularizing information of objects
on the scale of architectonic structures.
Abstract: Obturator Foramen is a specific structure in Pelvic
bone images and recognition of it is a new concept in medical image
processing. Moreover, segmentation of bone structures such as
Obturator Foramen plays an essential role for clinical research in
orthopedics. In this paper, we present a novel method to analyze the
similarity between the substructures of the imaged region and a hand
drawn template as a preprocessing step for computation of Pelvic
bone rotation on hip radiographs. This method consists of integrated
usage of Marker-controlled Watershed segmentation and Zernike
moment feature descriptor and it is used to detect Obturator Foramen
accurately. Marker-controlled Watershed segmentation is applied to
separate Obturator Foramen from the background effectively. Then,
Zernike moment feature descriptor is used to provide matching
between binary template image and the segmented binary image for
final extraction of Obturator Foramens. Finally, Pelvic bone rotation
rate calculation for each hip radiograph is performed automatically to
select and eliminate hip radiographs for further studies which depend
on Pelvic bone angle measurements. The proposed method is tested
on randomly selected 100 hip radiographs. The experimental results
demonstrated that the proposed method is able to segment Obturator
Foramen with 96% accuracy.
Abstract: In this paper, we describe an application for face
recognition. Many studies have used local descriptors to characterize
a face, the performance of these local descriptors remain low by
global descriptors (working on the entire image). The application of
local descriptors (cutting image into blocks) must be able to store
both the advantages of global and local methods in the Discrete
Cosine Transform (DCT) domain. This system uses neural network
techniques. The letter method provides a good compromise between
the two approaches in terms of simplifying of calculation and
classifying performance. Finally, we compare our results with those
obtained from other local and global conventional approaches.
Abstract: The aim of the study is to compare behavioral and
EEG reactions in Turkic-speaking inhabitants of Siberia (Tuvinians
and Yakuts) and Russians during the recognition of syntax errors in
native and foreign languages. Sixty-three healthy aboriginals of the
Tyva Republic, 29 inhabitants of the Sakha (Yakutia) Republic, and
55 Russians from Novosibirsk participated in the study. EEG were
recorded during execution of error-recognition task in Russian and
English language (in all participants) and in native languages
(Tuvinian or Yakut Turkic-speaking inhabitants). Reaction time (RT)
and quality of task execution were chosen as behavioral measures.
Amplitude and cortical distribution of P300 and P600 peaks of ERP
were used as a measure of speech-related brain activity. In Tuvinians,
there were no differences in the P300 and P600 amplitudes as well as
in cortical topology for Russian and Tuvinian languages, but there
was a difference for English. In Yakuts, the P300 and P600
amplitudes and topology of ERP for Russian language were the same
as Russians had for native language. In Yakuts, brain reactions during
Yakut and English language comprehension had no difference, while
the Russian language comprehension was differed from both Yakut
and English. We found out that the Tuvinians recognized both Russian and
Tuvinian as native languages, and English as a foreign language. The
Yakuts recognized both English and Yakut as foreign languages, but
Russian as a native language. According to the inquirer, both
Tuvinians and Yakuts use the national language as a spoken
language, whereas they do not use it for writing. It can well be a
reason that Yakuts perceive the Yakut writing language as a foreign
language while writing Russian as their native.
Abstract: In the context of the handwriting recognition, we
propose an off line system for the recognition of the Arabic
handwritten words of the Algerian departments. The study is based
mainly on the evaluation of neural network performances, trained
with the gradient back propagation algorithm. The used parameters to
form the input vector of the neural network are extracted on the
binary images of the handwritten word by several methods. The
Distribution parameters, the centered moments of the different
projections of the different segments, the centered moments of the
word image coding according to the directions of Freeman, and the
Barr features applied binary image of the word and on its different
segments. The classification is achieved by a multi layers perceptron.
A detailed experiment is carried and satisfactory recognition results
are reported.