Abstract: Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is 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 was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.
Abstract: We present a new method for the fully automatic 3D
reconstruction of the coronary artery centerlines, using two X-ray
angiogram projection images from a single rotating monoplane
acquisition system. During the first stage, the input images are
smoothed using curve evolution techniques. Next, a simple yet
efficient multiscale method, based on the information of the Hessian
matrix, for the enhancement of the vascular structure is introduced.
Hysteresis thresholding using different image quantiles, is used to
threshold the arteries. This stage is followed by a thinning procedure
to extract the centerlines. The resulting skeleton image is then pruned
using morphological and pattern recognition techniques to remove
non-vessel like structures. Finally, edge-based stereo correspondence
is solved using a parallel evolutionary optimization method based on
f symbiosis. The detected 2D centerlines combined with disparity
map information allow the reconstruction of the 3D vessel
centerlines. The proposed method has been evaluated on patient data
sets for evaluation purposes.
Abstract: A human verification system is presented in this
paper. The system consists of several steps: background subtraction,
thresholding, line connection, region growing, morphlogy, star
skelatonization, feature extraction, feature matching, and decision
making. The proposed system combines an advantage of star
skeletonization and simple statistic features. A correlation matching
and probability voting have been used for verification, followed by a
logical operation in a decision making stage. The proposed system
uses small number of features and the system reliability is
convincing.
Abstract: Single biometric modality recognition is not able to meet the high performance supplies in most cases with its application become more and more broadly. Multimodal biometrics identification represents an emerging trend recently. This paper investigates a novel algorithm based on fusion of both fingerprint and fingervein biometrics. For both biometric recognition, we employ the Monogenic Local Binary Pattern (MonoLBP). This operator integrate the orginal LBP (Local Binary Pattern ) with both other rotation invariant measures: local phase and local surface type. Experimental results confirm that a weighted sum based proposed fusion achieves excellent identification performances opposite unimodal biometric systems. The AUC of proposed approach based on combining the two modalities has very close to unity (0.93).
Abstract: ICA which is generally used for blind source separation
problem has been tested for feature extraction in Speech recognition
system to replace the phoneme based approach of MFCC. Applying
the Cepstral coefficients generated to ICA as preprocessing has
developed a new signal processing approach. This gives much better
results against MFCC and ICA separately, both for word and speaker
recognition. The mixing matrix A is different before and after MFCC
as expected. As Mel is a nonlinear scale. However, cepstrals
generated from Linear Predictive Coefficient being independent
prove to be the right candidate for ICA. Matlab is the tool used for
all comparisons. The database used is samples of ISOLET.
Abstract: This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network (RBFN). The proposed approach has been tested on the ORL database. The experimental results show that the LDA+RBFN algorithm has achieved a recognition rate of 93.5%
Abstract: The use of High Order Statistics (HOS) analysis is
expected to provide so many candidates of features that can be selected for pattern recognition. More candidates of the feature can
be extracted using simple manipulation through a specific mathematical function prior to the HOS analysis. Feature extraction
method using HOS analysis combined with Difference to the Nth-Power manipulation has been examined in application for Automatic
Modulation Recognition (AMR) to perform scheme recognition of three digital modulation signal, i.e. QPSK-16QAM-64QAM in the
AWGN transmission channel. The simulation results is reported
when the analysis of HOS up to order-12 and the manipulation of Difference to the Nth-Power up to N = 4. The obtained accuracy rate
of AMR using the method of Simple Decision obtained 90% in SNR > 10 dB in its classifier, while using the method of Voted Decision is
96% in SNR > 2 dB.
Abstract: A wide spectrum of systems require reliable
personal recognition schemes to either confirm or determine the
identity of an individual person. This paper considers multimodal
biometric system and their applicability to access control,
authentication and security applications. Strategies for feature
extraction and sensor fusion are considered and contrasted. Issues
related to performance assessment, deployment and standardization
are discussed. Finally future directions of biometric systems
development are discussed.
Abstract: Named Entity Recognition (NER) aims to classify each word of a document into predefined target named entity classes and is now-a-days considered to be fundamental for many Natural Language Processing (NLP) tasks such as information retrieval, machine translation, information extraction, question answering systems and others. This paper reports about the development of a NER system for Bengali and Hindi using Support Vector Machine (SVM). Though this state of the art machine learning technique has been widely applied to NER in several well-studied languages, the use of this technique to Indian languages (ILs) is very new. The system makes use of the different contextual information of the words along with the variety of features that are helpful in predicting the four different named (NE) classes, such as Person name, Location name, Organization name and Miscellaneous name. We have used the annotated corpora of 122,467 tokens of Bengali and 502,974 tokens of Hindi tagged with the twelve different NE classes 1, defined as part of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (SSEAL) 2. In addition, we have manually annotated 150K wordforms of the Bengali news corpus, developed from the web-archive of a leading Bengali newspaper. We have also developed an unsupervised algorithm in order to generate the lexical context patterns from a part of the unlabeled Bengali news corpus. Lexical patterns have been used as the features of SVM in order to improve the system performance. The NER system has been tested with the gold standard test sets of 35K, and 60K tokens for Bengali, and Hindi, respectively. Evaluation results have demonstrated the recall, precision, and f-score values of 88.61%, 80.12%, and 84.15%, respectively, for Bengali and 80.23%, 74.34%, and 77.17%, respectively, for Hindi. Results show the improvement in the f-score by 5.13% with the use of context patterns. Statistical analysis, ANOVA is also performed to compare the performance of the proposed NER system with that of the existing HMM based system for both the languages.
Abstract: Biometrics methods include recognition techniques
such as fingerprint, iris, hand geometry, voice, face, ears and gait. The gait recognition approach has some advantages, for example it
does not need the prior concern of the observed subject and it can
record many biometric features in order to make deeper analysis, but
most of the research proposals use high computational cost. This
paper shows a gait recognition system with feature subtraction on a
bundle rectangle drawn over the observed person. Statistical results
within a database of 500 videos are shown.
Abstract: Temperature dependence of force of gravitation is one
of the fundamental problems of physics. This problem has got special
value in connection with that the general theory of relativity,
supposing the weakest positive influence of a body temperature on its
weight, actually rejects an opportunity of measurement of negative
influence of temperature on gravity in laboratory conditions. Really,
the recognition of negative temperature dependence of gravitation,
for example, means basic impossibility of achievement of a
singularity («a black hole») at a gravitational collapse. Laboratory
experiments with exact weighing the heated up metal samples,
indicating negative influence temperatures of bodies on their physical
weight are described. Influence of mistakes of measurements is
analyzed. Calculations of distribution of temperature in volume of the
bar, agreed with experimental data of time dependence of weight of
samples are executed. The physical substantiation of negative
temperature dependence of weight of the bodies, based on correlation
of acceleration at thermal movement of micro-particles of a body and
its absolute temperature, are given.
Abstract: The paper presents a complete discrete statistical framework, based on a novel vector quantization (VQ) front-end process. This new VQ approach performs an optimal distribution of VQ codebook components on HMM states. This technique that we named the distributed vector quantization (DVQ) of hidden Markov models, succeeds in unifying acoustic micro-structure and phonetic macro-structure, when the estimation of HMM parameters is performed. The DVQ technique is implemented through two variants. The first variant uses the K-means algorithm (K-means- DVQ) to optimize the VQ, while the second variant exploits the benefits of the classification behavior of neural networks (NN-DVQ) for the same purpose. The proposed variants are compared with the HMM-based baseline system by experiments of specific Arabic consonants recognition. The results show that the distributed vector quantization technique increase the performance of the discrete HMM system.
Abstract: Initial values of reference vectors have significant influence on recognition accuracy in LVQ. There are several existing techniques, such as SOM and k-means, for setting initial values of reference vectors, each of which has provided some positive results. However, those results are not sufficient for the improvement of recognition accuracy. This study proposes an ACO-used method for initializing reference vectors with an aim to achieve recognition accuracy higher than those obtained through conventional methods. Moreover, we will demonstrate the effectiveness of the proposed method by applying it to the wine data and English vowel data and comparing its results with those of conventional methods.
Abstract: Electronic Government is one of the special concepts
which has been performed successfully within recent decades.
Electronic government is a digital, wall-free government with a
virtual organization for presenting of online governmental services
and further cooperation in different political/social activities. In order
to have a successful implementation of electronic government
strategy and benefiting from its complete potential and benefits and
generally for establishment and applying of electronic government, it
is necessary to have different infrastructures as the basics of
electronic government with lack of which it is impossible to benefit
from mentioned services. For this purpose, in this paper we have
managed to recognize relevant obstacles for establishment of
electronic government in Iran. All required data for recognition of
obstacles were collected from statistical society of involved
specialists of Ministry of Communications & Information
Technology of Iran and Information Technology Organization of
Tehran Municipality through questionnaire. Then by considering of
five-point Likert scope and μ =3 as the index of relevant factors of
proposed model, we could specify current obstacles against
electronic government in Iran along with some guidelines and
proposal in this regard. According to the results, mentioned obstacles
for applying of electronic government in Iran are as follows:
Technical & technological problems, Legal, judicial & safety
problems, Economic problems and Humanistic Problems.
Abstract: In this paper we proposed a method for finding video
frames representing one sign in the finger alphabet. The method is
based on determining hands location, segmentation and the use of
standard video quality evaluation metrics. Metric calculation is
performed only in regions of interest. Sliding mechanism for finding
local extrema and adaptive threshold based on local averaging is used
for key frames selection. The success rate is evaluated by recall,
precision and F1 measure. The method effectiveness is compared
with metrics applied to all frames. Proposed method is fast, effective
and relatively easy to realize by simple input video preprocessing
and subsequent use of tools designed for video quality measuring.
Abstract: Human identification at a distance has recently gained
growing interest from computer vision researchers. Gait recognition
aims essentially to address this problem by identifying people based
on the way they walk [1]. Gait recognition has 3 steps. The first step
is preprocessing, the second step is feature extraction and the third
one is classification. This paper focuses on the classification step that
is essential to increase the CCR (Correct Classification Rate).
Multilayer Perceptron (MLP) is used in this work. Neural Networks
imitate the human brain to perform intelligent tasks [3].They can
represent complicated relationships between input and output and
acquire knowledge about these relationships directly from the data
[2]. In this paper we apply MLP NN for 11 views in our database and
compare the CCR values for these views. Experiments are performed
with the NLPR databases, and the effectiveness of the proposed
method for gait recognition is demonstrated.
Abstract: The temporal nature of negative selection is an under exploited area. In a negative selection system, newly generated antibodies go through a maturing phase, and the survivors of the phase then wait to be activated by the incoming antigens after certain number of matches. These without having enough matches will age and die, while these with enough matches (i.e., being activated) will become active detectors. A currently active detector may also age and die if it cannot find any match in a pre-defined (lengthy) period of time. Therefore, what matters in a negative selection system is the dynamics of the involved parties in the current time window, not the whole time duration, which may be up to eternity. This property has the potential to define the uniqueness of negative selection in comparison with the other approaches. On the other hand, a negative selection system is only trained with “normal" data samples. It has to learn and discover unknown “abnormal" data patterns on the fly by itself. Consequently, it is more appreciate to utilize negation selection as a system for pattern discovery and recognition rather than just pattern recognition. In this paper, we study the potential of using negative selection in discovering unknown temporal patterns.
Abstract: The purpose of this paper is to solve the problem of protecting aerial lines from high impedance faults (HIFs) in distribution systems. This investigation successfully applies 3I0 zero sequence current to solve HIF problems. The feature extraction system based on discrete wavelet transform (DWT) and the feature identification technique found on statistical confidence are then applied to discriminate effectively between the HIFs and the switch operations. Based on continuous wavelet transform (CWT) pattern recognition of HIFs is proposed, also. Staged fault testing results demonstrate that the proposed wavelet based algorithm is feasible performance well.
Abstract: The extraction of meaningful information from image
could be an alternative method for time series analysis. In this paper,
we propose a graphical analysis of time series grouped into table
with adjusted colour scale for numerical values. The advantages of
this method are also discussed. The proposed method is easy to
understand and is flexible to implement the standard methods of
pattern recognition and verification, especially for noisy
environmental data.
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.