Abstract: Character segmentation is an important preprocessing
step for text recognition. In degraded documents, existence of
touching characters decreases recognition rate drastically, for any
optical character recognition (OCR) system. In this paper we have
proposed a complete solution for segmenting touching characters in
all the three zones of printed Gurmukhi script. A study of touching
Gurmukhi characters is carried out and these characters have been
divided into various categories after a careful analysis. Structural
properties of the Gurmukhi characters are used for defining the
categories. New algorithms have been proposed to segment the
touching characters in middle zone, upper zone and lower zone.
These algorithms have shown a reasonable improvement in
segmenting the touching characters in degraded printed Gurmukhi
script. The algorithms proposed in this paper are applicable only to
machine printed text. We have also discussed a new and useful
technique to segment the horizontally overlapping lines.
Abstract: Although lots of research work has been done for
human pose recognition, the view-point of cameras is still critical
problem of overall recognition system. In this paper, view-point
insensitive human pose recognition is proposed. The aims of the
proposed system are view-point insensitivity and real-time processing.
Recognition system consists of feature extraction module, neural
network and real-time feed forward calculation. First, histogram-based
method is used to extract feature from silhouette image and it is
suitable for represent the shape of human pose. To reduce the
dimension of feature vector, Principle Component Analysis(PCA) is
used. Second, real-time processing is implemented by using Compute
Unified Device Architecture(CUDA) and this architecture improves
the speed of feed-forward calculation of neural network. We
demonstrate the effectiveness of our approach with experiments on
real environment.
Abstract: To increase reliability of face recognition system, the
system must be able to distinguish real face from a copy of face such
as a photograph. In this paper, we propose a fast and memory efficient
method of live face detection for embedded face recognition system,
based on the analysis of the movement of the eyes. We detect eyes in
sequential input images and calculate variation of each eye region to
determine whether the input face is a real face or not. Experimental
results show that the proposed approach is competitive and promising
for live face detection.
Abstract: This manuscript presents, palmprint recognition by
combining different texture extraction approaches with high accuracy.
The Region of Interest (ROI) is decomposed into different frequencytime
sub-bands by wavelet transform up-to two levels and only the
approximate image of two levels is selected, which is known as
Approximate Image ROI (AIROI). This AIROI has information of
principal lines of the palm. The Competitive Index is used as the
features of the palmprint, in which six Gabor filters of different
orientations convolve with the palmprint image to extract the orientation
information from the image. The winner-take-all strategy
is used to select dominant orientation for each pixel, which is
known as Competitive Index. Further, PCA is applied to select highly
uncorrelated Competitive Index features, to reduce the dimensions of
the feature vector, and to project the features on Eigen space. The
similarity of two palmprints is measured by the Euclidean distance
metrics. The algorithm is tested on Hong Kong PolyU palmprint
database. Different AIROI of different wavelet filter families are also
tested with the Competitive Index and PCA. AIROI of db7 wavelet
filter achievs Equal Error Rate (EER) of 0.0152% and Genuine
Acceptance Rate (GAR) of 99.67% on the palm database of Hong
Kong PolyU.
Abstract: Nowadays, web-based technologies influence in
people-s daily life such as in education, business and others.
Therefore, many web developers are too eager to develop their web
applications with fully animation graphics and forgetting its
accessibility to its users. Their purpose is to make their web
applications look impressive. Thus, this paper would highlight on the
usability and accessibility of a voice recognition browser as a tool to
facilitate the visually impaired and blind learners in accessing virtual
learning environment. More specifically, the objectives of the study
are (i) to explore the challenges faced by the visually impaired
learners in accessing virtual learning environment (ii) to determine
the suitable guidelines for developing a voice recognition browser
that is accessible to the visually impaired. Furthermore, this study
was prepared based on an observation conducted with the Malaysian
visually impaired learners. Finally, the result of this study would
underline on the development of an accessible voice recognition
browser for the visually impaired.
Abstract: Dealing with hundreds of features in character
recognition systems is not unusual. This large number of features
leads to the increase of computational workload of recognition
process. There have been many methods which try to remove
unnecessary or redundant features and reduce feature dimensionality.
Besides because of the characteristics of Farsi scripts, it-s not
possible to apply other languages algorithms to Farsi directly. In this
paper some methods for feature subset selection using genetic
algorithms are applied on a Farsi optical character recognition (OCR)
system. Experimental results show that application of genetic
algorithms (GA) to feature subset selection in a Farsi OCR results in
lower computational complexity and enhanced recognition rate.
Abstract: In Thailand, the practice of pre-hospital Emergency
Medical Service (EMS) in each area reveals the different growth
rates and effectiveness of the practices. Those can be found as the
diverse quality and quantity. To shorten the learning curve prior to
speed-up the practices in other areas, story telling and lessons learnt
from the effective practices are valued as meaningful knowledge. To
this paper, it was to ascertain the factors, lessons learnt and best
practices that have impact as contributing to the success of prehospital
EMS system. Those were formulized as model prior to
speedup the practice in other areas. To develop the model, Malcolm
Baldrige National Quality Award (MBNQA), which is widely
recognized as a framework for organizational quality assessment and
improvement, was chosen as the discussion framework. Remarkably,
this study was based on the consideration of knowledge capture;
however it was not to complete the loop of knowledge activities.
Nevertheless, it was to highlight the recognition of knowledge
capture, which is the initiation of knowledge management.
Abstract: A comparison between the performance of Latin and
Arabic handwritten digits recognition problems is presented. The
performance of ten different classifiers is tested on two similar
Arabic and Latin handwritten digits databases. The analysis shows
that Arabic handwritten digits recognition problem is easier than that
of Latin digits. This is because the interclass difference in case of
Latin digits is smaller than in Arabic digits and variances in writing
Latin digits are larger. Consequently, weaker yet fast classifiers are
expected to play more prominent role in Arabic handwritten digits
recognition.
Abstract: Image data holds a large amount of different context
information. However, as of today, these resources remain largely
untouched. It is thus the aim of this paper to present a basic technical
framework which allows for a quick and easy exploitation of context
information from image data especially by non-expert users.
Furthermore, the proposed framework is discussed in detail
concerning important social and ethical issues which demand special
requirements in system design. Finally, a first sensor prototype is
presented which meets the identified requirements. Additionally,
necessary implications for the software and hardware design of the
system are discussed, rendering a sensor system which could be
regarded as a good, acceptable and justifiable technical and thereby
enabling the extraction of context information from image data.
Abstract: Support Vector Machine (SVM) is a recent class of statistical classification and regression techniques playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM is applied to an infrared (IR) binary communication system with different types of channel models including Ricean multipath fading and partially developed scattering channel with additive white Gaussian noise (AWGN) at the receiver. The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these channel stochastic models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to classical binary signal maximum likelihood detection using a matched filter driven by On-Off keying (OOK) modulation. We found that the performance of SVM is superior to that of the traditional optimal detection schemes used in statistical communication, especially for very low signal-to-noise ratio (SNR) ranges. For large SNR, the performance of the SVM is similar to that of the classical detectors. The implication of these results is that SVM can prove very beneficial to IR communication systems that notoriously suffer from low SNR at the cost of increased computational complexity.
Abstract: SoftBoost is a recently presented boosting algorithm,
which trades off the size of achieved classification margin and
generalization performance. This paper presents a performance
evaluation of SoftBoost algorithm on the generic object recognition
problem. An appearance-based generic object recognition
model is used. The evaluation experiments are performed using
a difficult object recognition benchmark. An assessment with respect
to different degrees of label noise as well as a comparison to
the well known AdaBoost algorithm is performed. The obtained
results reveal that SoftBoost is encouraged to be used in cases
when the training data is known to have a high degree of noise.
Otherwise, using Adaboost can achieve better performance.
Abstract: The goal of this project is to design a system to
recognition voice commands. Most of voice recognition systems
contain two main modules as follow “feature extraction" and “feature
matching". In this project, MFCC algorithm is used to simulate
feature extraction module. Using this algorithm, the cepstral
coefficients are calculated on mel frequency scale. VQ (vector
quantization) method will be used for reduction of amount of data to
decrease computation time. In the feature matching stage Euclidean
distance is applied as similarity criterion. Because of high accuracy
of used algorithms, the accuracy of this voice command system is
high. Using these algorithms, by at least 5 times repetition for each
command, in a single training session, and then twice in each testing
session zero error rate in recognition of commands is achieved.
Abstract: To improve the classification rate of the face
recognition, features combination and a novel non-linear kernel are
proposed. The feature vector concatenates three different radius of
local binary patterns and Gabor wavelet features. Gabor features are
the mean, standard deviation and the skew of each scaling and
orientation parameter. The aim of the new kernel is to incorporate
the power of the kernel methods with the optimal balance between
the features. To verify the effectiveness of the proposed method,
numerous methods are tested by using four datasets, which are
consisting of various emotions, orientations, configuration,
expressions and lighting conditions. Empirical results show the
superiority of the proposed technique when compared to other
methods.
Abstract: In this paper a new approach to face recognition is
presented that achieves double dimension reduction, making the
system computationally efficient with better recognition results and
out perform common DCT technique of face recognition. In pattern
recognition techniques, discriminative information of image
increases with increase in resolution to a certain extent, consequently
face recognition results change with change in face image resolution
and provide optimal results when arriving at a certain resolution
level. In the proposed model of face recognition, initially image
decimation algorithm is applied on face image for dimension
reduction to a certain resolution level which provides best
recognition results. Due to increased computational speed and feature
extraction potential of Discrete Cosine Transform (DCT), it is
applied on face image. A subset of coefficients of DCT from low to
mid frequencies that represent the face adequately and provides best
recognition results is retained. A tradeoff between decimation factor,
number of DCT coefficients retained and recognition rate with
minimum computation is obtained. Preprocessing of the image is
carried out to increase its robustness against variations in poses and
illumination level. This new model has been tested on different
databases which include ORL , Yale and EME color database.
Abstract: augmented reality is a technique used to insert virtual objects in real scenes. One of the most used libraries in the area is the ARToolkit library. It is based on the recognition of the markers that are in the form of squares with a pattern inside. This pattern which is mostly textual is source of confusing. In this paper, we present the results of a classification of Latin characters as a pattern on the ARToolkit markers to know the most distinguishable among them.
Abstract: Proper orthogonal decomposition (POD) is used to reconstruct spatio-temporal data of a fully developed turbulent channel flow with density variation at Reynolds number of 150, based on the friction velocity and the channel half-width, and Prandtl number of 0.71. To apply POD to the fully developed turbulent channel flow with density variation, the flow field (velocities, density, and temperature) is scaled by the corresponding root mean square values (rms) so that the flow field becomes dimensionless. A five-vector POD problem is solved numerically. The reconstructed second-order moments of velocity, temperature, and density from POD eigenfunctions compare favorably to the original Direct Numerical Simulation (DNS) data.
Abstract: Detection and tracking of the lip contour is an important
issue in speechreading. While there are solutions for lip tracking
once a good contour initialization in the first frame is available,
the problem of finding such a good initialization is not yet solved
automatically, but done manually. We have developed a new tracking
solution for lip contour detection using only few landmarks (15
to 25) and applying the well known Active Shape Models (ASM).
The proposed method is a new LMS-like adaptive scheme based on
an Auto regressive (AR) model that has been fit on the landmark
variations in successive video frames. Moreover, we propose an extra
motion compensation model to address more general cases in lip
tracking. Computer simulations demonstrate a fair match between
the true and the estimated spatial pixels. Significant improvements
related to the well known LMS approach has been obtained via a
defined Frobenius norm index.
Abstract: A new conceptual architecture for low-level neural
pattern recognition is presented. The key ideas are that the brain
implements support vector machines and that support vectors are
represented as memory patterns in competitive queuing memories. A
binary classifier is built from two competitive queuing memories
holding positive and negative valence training examples respectively.
The support vector machine classification function is calculated in
synchronized evaluation cycles. The kernel is computed by bisymmetric
feed-forward networks feed by sensory input and by
competitive queuing memories traversing the complete sequence of
support vectors. Temporary summation generates the output
classification. It is speculated that perception apparatus in the brain
reuses structures that have evolved for enabling fluent execution of
prepared action sequences so that pattern recognition is built on
internalized motor programmes.
Abstract: Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.
Abstract: The knowledge base of welding defect recognition is
essentially incomplete. This characteristic determines that the recognition results do not reflect the actual situation. It also has a further influence on the classification of welding quality. This paper is
concerned with the study of a rough set based method to reduce the influence and improve the classification accuracy. At first, a rough set
model of welding quality intelligent classification has been built. Both condition and decision attributes have been specified. Later on, groups
of the representative multiple compound defects have been chosen
from the defect library and then classified correctly to form the
decision table. Finally, the redundant information of the decision table has been reducted and the optimal decision rules have been reached. By this method, we are able to reclassify the misclassified defects to
the right quality level. Compared with the ordinary ones, this method
has higher accuracy and better robustness.