Abstract: The Ant Colony Optimization (ACO) is a metaheuristic inspired by the behavior of real ants in their search for the shortest paths to food sources. It has recently attracted a lot of attention and has been successfully applied to a number of different optimization problems. Due to the importance of the feature selection problem and the potential of ACO, this paper presents a novel method that utilizes the ACO algorithm to implement a feature subset search procedure. Initial results obtained using the classification of speech segments are very promising.
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: Hidden Markov Model (HMM) is a stochastic method
which has been used in various signal processing and character
recognition. This study proposes to use HMM to recognize Javanese
characters from a number of different handwritings, whereby HMM
is used to optimize the number of state and feature extraction. An
85.7 % accuracy is obtained as the best result in 16-stated vertical
model using pure HMM. This initial result is satisfactory for
prompting further research.
Abstract: Computerized lip reading has been one of the most
actively researched areas of computer vision in recent past because
of its crime fighting potential and invariance to acoustic environment.
However, several factors like fast speech, bad pronunciation,
poor illumination, movement of face, moustaches and beards make
lip reading difficult. In present work, we propose a solution for
automatic lip contour tracking and recognizing letters of English
language spoken by speakers using the information available from
lip movements. Level set method is used for tracking lip contour
using a contour velocity model and a feature vector of lip movements
is then obtained. Character recognition is performed using modified
k nearest neighbor algorithm which assigns more weight to nearer
neighbors. The proposed system has been found to have accuracy
of 73.3% for character recognition with speaker lip movements as
the only input and without using any speech recognition system in
parallel. The approach used in this work is found to significantly
solve the purpose of lip reading when size of database is small.
Abstract: Real-time hand tracking is a challenging task in many
computer vision applications such as gesture recognition. This paper
proposes a robust method for hand tracking in a complex environment
using Mean-shift analysis and Kalman filter in conjunction with 3D
depth map. The depth information solve the overlapping problem
between hands and face, which is obtained by passive stereo measuring
based on cross correlation and the known calibration data of
the cameras. Mean-shift analysis uses the gradient of Bhattacharyya
coefficient as a similarity function to derive the candidate of the hand
that is most similar to a given hand target model. And then, Kalman
filter is used to estimate the position of the hand target. The results
of hand tracking, tested on various video sequences, are robust to
changes in shape as well as partial occlusion.
Abstract: Everyday the usages of the Internet increase and simply a world of the data become accessible. Network providers do not want to let the provided services to be used in harmful or terrorist affairs, so they used a variety of methods to protect the special regions from the harmful data. One of the most important methods is supposed to be the firewall. Firewall stops the transfer of such packets through several ways, but in some cases they do not use firewall because of its blind packet stopping, high process power needed and expensive prices. Here we have proposed a method to find a discriminate function to distinguish between usual packets and harmful ones by the statistical processing on the network router logs. So an administrator can alarm to the user. This method is very fast and can be used simply in adjacent with the Internet routers.
Abstract: This paper proposes new hybrid approaches for face
recognition. Gabor wavelets representation of face images is an
effective approach for both facial action recognition and face
identification. Perform dimensionality reduction and linear
discriminate analysis on the down sampled Gabor wavelet faces can
increase the discriminate ability. Nearest feature space is extended to
various similarity measures. In our experiments, proposed Gabor
wavelet faces combined with extended neural net feature space
classifier shows very good performance, which can achieve 93 %
maximum correct recognition rate on ORL data set without any preprocessing
step.
Abstract: The use of solar control film on windows as one of
solar passive strategies for building have becoming important and is
gaining recognition. Malaysia located close to equator is having
warm humid climate with long sunshine hours and abundant solar
radiation throughout the year. Hence, befitting solar control on
windows is absolutely necessary to capture the daylight whilst
moderating thermal impact and eliminating glare problems. This is
one of the energy efficient strategies to achieve thermal and visual
comfort in buildings. Therefore, this study was carried out to
investigate the effect of window solar controls on thermal and visual
performance of naturally ventilated buildings. This was conducted via
field data monitoring using a test building facility. Four types of
window glazing systems were used with three types of solar control
films. Data were analysed for thermal and visual impact with
reference to thermal and optical characteristics of the films. Results
show that for each glazing system, the surface temperature of
windows are influenced by the Solar Energy Absorption property, the
indoor air temperature are influenced by the Solar Energy
Transmittance and Solar Energy Reflectance, and the daylighting by
Visible Light Transmission and Shading Coefficient. Further
investigations are underway to determine the mathematical relation
between thermal energy and visual performance with the thermal and
optical characteristics of solar control films.
Abstract: In this paper, a novel method for a biometric system based on the ECG signal is proposed, using spectral coefficients computed through linear predictive coding (LPC). ECG biometric systems have traditionally incorporated characteristics of fiducial points of the ECG signal as the feature set. These systems have been shown to contain loopholes and thus a non-fiducial system allows for tighter security. In the proposed system, incorporating non-fiducial features from the LPC spectrum produced a segment and subject recognition rate of 99.52% and 100% respectively. The recognition rates outperformed the biometric system that is based on the wavelet packet decomposition (WPD) algorithm in terms of recognition rates and computation time. This allows for LPC to be used in a practical ECG biometric system that requires fast, stringent and accurate recognition.
Abstract: The identification and classification of weeds are of
major technical and economical importance in the agricultural
industry. To automate these activities, like in shape, color and
texture, weed control system is feasible. The goal of this paper is to
build a real-time, machine vision weed control system that can detect
weed locations. In order to accomplish this objective, a real-time
robotic system is developed to identify and locate outdoor plants
using machine vision technology and pattern recognition. The
algorithm is developed to classify images into broad and narrow class
for real-time selective herbicide application. The developed
algorithm has been tested on weeds at various locations, which have
shown that the algorithm to be very effectiveness in weed
identification. Further the results show a very reliable performance
on weeds under varying field conditions. The analysis of the results
shows over 90 percent classification accuracy over 140 sample
images (broad and narrow) with 70 samples from each category of
weeds.
Abstract: In this paper the problem of face recognition under variable illumination conditions is considered. Most of the works in the literature exhibit good performance under strictly controlled acquisition conditions, but the performance drastically drop when changes in pose and illumination occur, so that recently number of approaches have been proposed to deal with such variability. The aim of this work is to introduce an efficient local appearance feature extraction method based steerable pyramid (SP) for face recognition. Local information is extracted from SP sub-bands using LBP(Local binary Pattern). The underlying statistics allow us to reduce the required amount of data to be stored. The experiments carried out on different face databases confirm the effectiveness of the proposed approach.
Abstract: Automated operations based on voice commands will become more and more important in many applications, including robotics, maintenance operations, etc. However, voice command recognition rates drop quite a lot under non-stationary and chaotic noise environments. In this paper, we tried to significantly improve the speech recognition rates under non-stationary noise environments. First, 298 Navy acronyms have been selected for automatic speech recognition. Data sets were collected under 4 types of noisy environments: factory, buccaneer jet, babble noise in a canteen, and destroyer. Within each noisy environment, 4 levels (5 dB, 15 dB, 25 dB, and clean) of Signal-to-Noise Ratio (SNR) were introduced to corrupt the speech. Second, a new algorithm to estimate speech or no speech regions has been developed, implemented, and evaluated. Third, extensive simulations were carried out. It was found that the combination of the new algorithm, the proper selection of language model and a customized training of the speech recognizer based on clean speech yielded very high recognition rates, which are between 80% and 90% for the four different noisy conditions. Fourth, extensive comparative studies have also been carried out.
Abstract: Feature selection is an important step in many pattern
classification problems. It is applied to select a subset of features,
from a much larger set, such that the selected subset is sufficient to
perform the classification task. Due to its importance, the problem of
feature selection has been investigated by many researchers. In this
paper, a novel feature subset search procedure that utilizes the Ant
Colony Optimization (ACO) is presented. The ACO is a
metaheuristic inspired by the behavior of real ants in their search for
the shortest paths to food sources. It looks for optimal solutions by
considering both local heuristics and previous knowledge. When
applied to two different classification problems, the proposed
algorithm achieved very promising results.
Abstract: In this paper a novel algorithm is proposed to merit
the accuracy of finger vein recognition. The performances of
Principal Component Analysis (PCA), Kernel Principal Component
Analysis (KPCA), and Kernel Entropy Component Analysis (KECA)
in this algorithm are validated and compared with each other in order
to determine which one is the most appropriate one in terms of finger
vein recognition.
Abstract: We report in this paper the model adopted by our
system of continuous speech recognition in Arab language SySRA
and the results obtained until now. This system uses the database
Arabdic-10 which is a corpus of word for the Arab language and
which was manually segmented. Phonetic decoding is represented
by an expert system where the knowledge base is translated in the
form of production rules. This expert system transforms a vocal
signal into a phonetic lattice. The higher level of the system takes
care of the recognition of the lattice thus obtained by deferring it in
the form of written sentences (orthographical Form). This level
contains initially the lexical analyzer which is not other than the
module of recognition. We subjected this analyzer to a set of
spectrograms obtained by dictating a score of sentences in Arab
language. The rate of recognition of these sentences is about 70%
which is, to our knowledge, the best result for the recognition of the
Arab language. The test set consists of twenty sentences from four
speakers not having taken part in the training.
Abstract: With the advance of information technology in the
new era the applications of Internet to access data resources has
steadily increased and huge amount of data have become accessible
in various forms. Obviously, the network providers and agencies,
look after to prevent electronic attacks that may be harmful or may
be related to terrorist applications. Thus, these have facilitated the
authorities to under take a variety of methods to protect the special
regions from harmful data. One of the most important approaches is
to use firewall in the network facilities. The main objectives of
firewalls are to stop the transfer of suspicious packets in several
ways. However because of its blind packet stopping, high process
power requirements and expensive prices some of the providers are
reluctant to use the firewall. In this paper we proposed a method to
find a discriminate function to distinguish between usual packets and
harmful ones by the statistical processing on the network router logs.
By discriminating these data, an administrator may take an approach
action against the user. This method is very fast and can be used
simply in adjacent with the Internet routers.
Abstract: In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.
Abstract: We introduce an algorithm based on the
morphological shared-weight neural network. Being nonlinear and
translation-invariant, the MSNN can be used to create better
generalization during face recognition. Feature extraction is
performed on grayscale images using hit-miss transforms that are
independent of gray-level shifts. The output is then learned by
interacting with the classification process. The feature extraction and
classification networks are trained together, allowing the MSNN to
simultaneously learn feature extraction and classification for a face.
For evaluation, we test for robustness under variations in gray levels
and noise while varying the network-s configuration to optimize
recognition efficiency and processing time. Results show that the
MSNN performs better for grayscale image pattern classification
than ordinary neural networks.
Abstract: Current OCR technology does not allow to
accurately recognizing small text images, such as those found
in web images. Our goal is to investigate new approaches to
recognize very low resolution text images containing antialiased
character shapes.
This paper presents a preliminary study on the variability of
such characters and the feasibility to discriminate them by
using geometrical features. In a first stage we analyze the
distribution of these features. In a second stage we present a
study on the discriminative power for recognizing isolated
characters, using various rendering methods and font
properties. Finally we present interesting results of our
evaluation tests leading to our conclusion and future focus.
Abstract: Rapid advancement in computing technology brings
computers and humans to be seamlessly integrated in future. The
emergence of smartphone has driven computing era towards
ubiquitous and pervasive computing. Recognizing human activity has
garnered a lot of interest and has raised significant researches-
concerns in identifying contextual information useful to human
activity recognition. Not only unobtrusive to users in daily life,
smartphone has embedded built-in sensors that capable to sense
contextual information of its users supported with wide range
capability of network connections. In this paper, we will discuss the
classification algorithms used in smartphone-based human activity.
Existing technologies pertaining to smartphone-based researches in
human activity recognition will be highlighted and discussed. Our
paper will also present our findings and opinions to formulate
improvement ideas in current researches- trends. Understanding
research trends will enable researchers to have clearer research
direction and common vision on latest smartphone-based human
activity recognition area.