Abstract: Embedding and extraction of a secret information as
well as the restoration of the original un-watermarked image is
highly desirable in sensitive applications like military, medical, and
law enforcement imaging. This paper presents a novel reversible
data-hiding method for digital images using integer to integer
wavelet transform and companding technique which can embed and
recover the secret information as well as can restore the image to its
pristine state. The novel method takes advantage of block based
watermarking and iterative optimization of threshold for companding
which avoids histogram pre and post-processing. Consequently, it
reduces the associated overhead usually required in most of the
reversible watermarking techniques. As a result, it keeps the
distortion small between the marked and the original images.
Experimental results show that the proposed method outperforms the
existing reversible data hiding schemes reported in the literature.
Abstract: This paper investigates the problem of automated defect
detection for textile fabrics and proposes a new optimal filter design
method to solve this problem. Gabor Wavelet Network (GWN) is
chosen as the major technique to extract the texture features from
textile fabrics. Based on the features extracted, an optimal Gabor filter
can be designed. In view of this optimal filter, a new semi-supervised
defect detection scheme is proposed, which consists of one real-valued
Gabor filter and one smoothing filter. The performance of the scheme
is evaluated by using an offline test database with 78 homogeneous
textile images. The test results exhibit accurate defect detection with
low false alarm, thus showing the effectiveness and robustness of the
proposed scheme. To evaluate the detection scheme comprehensively,
a prototyped detection system is developed to conduct a real time test.
The experiment results obtained confirm the efficiency and
effectiveness of the proposed detection scheme.
Abstract: There have been numerous implementations of
security system using biometric, especially for identification and
verification cases. An example of pattern used in biometric is the iris
pattern in human eye. The iris pattern is considered unique for each
person. The use of iris pattern poses problems in encoding the human
iris.
In this research, an efficient iris recognition method is proposed.
In the proposed method the iris segmentation is based on the
observation that the pupil has lower intensity than the iris, and the
iris has lower intensity than the sclera. By detecting the boundary
between the pupil and the iris and the boundary between the iris and
the sclera, the iris area can be separated from pupil and sclera. A step
is taken to reduce the effect of eyelashes and specular reflection of
pupil. Then the four levels Coiflet wavelet transform is applied to the
extracted iris image. The modified Hamming distance is employed to
measure the similarity between two irises.
This research yields the identification success rate of 84.25% for
the CASIA version 1.0 database. The method gives an accuracy of
77.78% for the left eyes of MMU 1 database and 86.67% for the
right eyes. The time required for the encoding process, from the
segmentation until the iris code is generated, is 0.7096 seconds.
These results show that the accuracy and speed of the method is
better than many other methods.
Abstract: This paper proposes fractal patterns for power quality
(PQ) detection using color relational analysis (CRA) based classifier.
Iterated function system (IFS) uses the non-linear interpolation in the
map and uses similarity maps to construct various fractal patterns of
power quality disturbances, including harmonics, voltage sag, voltage
swell, voltage sag involving harmonics, voltage swell involving
harmonics, and voltage interruption. The non-linear interpolation
functions (NIFs) with fractal dimension (FD) make fractal patterns
more distinguishing between normal and abnormal voltage signals.
The classifier based on CRA discriminates the disturbance events in a
power system. Compared with the wavelet neural networks, the test
results will show accurate discrimination, good robustness, and faster
processing time for detecting disturbing events.
Abstract: In present paper we proposed a simple and effective method to compress an image. Here we found success in size reduction of an image without much compromising with it-s quality. Here we used Haar Wavelet Transform to transform our original image and after quantization and thresholding of DWT coefficients Run length coding and Huffman coding schemes have been used to encode the image. DWT is base for quite populate JPEG 2000 technique.
Abstract: The ability to detect and classify the type of fault
plays a great role in the protection of power system. This procedure
is required to be precise with no time consumption. In this paper
detection of fault type has been implemented using wavelet analysis
together with wavelet entropy principle. The simulation of power
system is carried out using PSCAD/EMTDC. Different types of
faults were studied obtaining various current waveforms. These
current waveforms were decomposed using wavelet analysis into
different approximation and details. The wavelet entropy of such
decompositions is analyzed reaching a successful methodology for
fault classification. The suggested approach is tested using different
fault types and proven successful identification for the type of fault.
Abstract: This paper proposes a dual tree complex wavelet transform (DT-CWT) based directional interpolation scheme for noisy images. The problems of denoising and interpolation are modelled as to estimate the noiseless and missing samples under the same framework of optimal estimation. Initially, DT-CWT is used to decompose an input low-resolution noisy image into low and high frequency subbands. The high-frequency subband images are interpolated by linear minimum mean square estimation (LMMSE) based interpolation, which preserves the edges of the interpolated images. For each noisy LR image sample, we compute multiple estimates of it along different directions and then fuse those directional estimates for a more accurate denoised LR image. The estimation parameters calculated in the denoising processing can be readily used to interpolate the missing samples. The inverse DT-CWT is applied on the denoised input and interpolated high frequency subband images to obtain the high resolution image. Compared with the conventional schemes that perform denoising and interpolation in tandem, the proposed DT-CWT based noisy image interpolation method can reduce many noise-caused interpolation artifacts and preserve well the image edge structures. The visual and quantitative results show that the proposed technique outperforms many of the existing denoising and interpolation methods.
Abstract: Image registration plays an important role in the
diagnosis of dental pathologies such as dental caries, alveolar bone
loss and periapical lesions etc. This paper presents a new wavelet
based algorithm for registering noisy and poor contrast dental x-rays.
Proposed algorithm has two stages. First stage is a preprocessing
stage, removes the noise from the x-ray images. Gaussian filter has
been used. Second stage is a geometric transformation stage.
Proposed work uses two levels of affine transformation. Wavelet
coefficients are correlated instead of gray values. Algorithm has been
applied on number of pre and post RCT (Root canal treatment)
periapical radiographs. Root Mean Square Error (RMSE) and
Correlation coefficients (CC) are used for quantitative evaluation.
Proposed technique outperforms conventional Multiresolution
strategy based image registration technique and manual registration
technique.
Abstract: The prediction of transmembrane helical segments
(TMHs) in membrane proteins is an important field in the
bioinformatics research. In this paper, a method based on discrete
wavelet transform (DWT) has been developed to predict the number
and location of TMHs in membrane proteins. PDB coded as 1F88 was
chosen as an example to describe the prediction of the number and
location of TMHs in membrane proteins by using this method. One
group of test data sets that contain total 19 protein sequences was
utilized to access the effect of this method. Compared with the
prediction results of DAS, PRED-TMR2, SOSUI, HMMTOP2.0 and
TMHMM2.0, the obtained results indicate that the presented method
has higher prediction accuracy.
Abstract: The principal purpose of this article is to present a new method based on Adaptive Neural Network Fuzzy Inference System (ANFIS) to generate additional artificial earthquake accelerograms from presented data, which are compatible with specified response spectra. The proposed method uses the learning abilities of ANFIS to develop the knowledge of the inverse mapping from response spectrum to earthquake records. In addition, wavelet packet transform is used to decompose specified earthquake records and then ANFISs are trained to relate the response spectrum of records to their wavelet packet coefficients. Finally, an interpretive example is presented which uses an ensemble of recorded accelerograms to demonstrate the effectiveness of the proposed method.
Abstract: This text studies glass bottle intelligent inspector
based machine vision instead of manual inspection. The system
structure is illustrated in detail in this paper. The text presents the
method based on watershed transform methods to segment the
possible defective regions and extract features of bottle wall by rules.
Then wavelet transform are used to exact features of bottle finish
from images. After extracting features, the fuzzy support vector
machine ensemble is putted forward as classifier. For ensuring that
the fuzzy support vector machines have good classification ability,
the GA based ensemble method is used to combining the several
fuzzy support vector machines. The experiments demonstrate that
using this inspector to inspect glass bottles, the accuracy rate may
reach above 97.5%.
Abstract: power-line networks are promise infrastructure for
broadband services provision to end users. However, the network
performance is affected by stochastic channel changing which is due
to load impedances, number of branches and branched line lengths. It
has been proposed that multi-carrier modulations techniques such as
orthogonal frequency division multiplexing (OFDM), Multi-Carrier
Spread Spectrum (MC-SS), wavelet OFDM can be used in such
environment. This paper investigates the performance of different
indoor topologies of power-line networks that uses MC-SS
modulation scheme.It is observed that when a branch is added in the
link between sending and receiving end of an indoor channel an
average of 2.5dB power loss is found. In additional, when the branch
is added at a node an average of 1dB power loss is found.
Additionally when the terminal impedances of the branch change
from line characteristic impedance to impedance either higher or
lower values the channel performances were tremendously improved.
For example changing terminal load from characteristic impedance
(85 .) to 5 . the signal to noise ratio (SNR) required to attain the
same performances were decreased from 37dB to 24dB respectively.
Also, changing the terminal load from channel characteristic
impedance (85 .) to very higher impedance (1600 .) the SNR
required to maintain the same performances were decreased from
37dB to 23dB. The result concludes that MC-SS performs better
compared with OFDM techniques in all aspects and especially when
the channel is terminated in either higher or lower impedances.
Abstract: Image watermarking has become an important tool for
intellectual property protection and authentication. In this paper a
watermarking technique is suggested that incorporates two
watermarks in a host image for improved protection and robustness.
A watermark, in form of a PN sequence (will be called the secondary
watermark), is embedded in the wavelet domain of a primary
watermark before being embedded in the host image. The technique
has been tested using Lena image as a host and the camera man as
the primary watermark. The embedded PN sequence was detectable
through correlation among other five sequences where a PSNR of
44.1065 dB was measured. Furthermore, to test the robustness of the
technique, the watermarked image was exposed to four types of
attacks, namely compression, low pass filtering, salt and pepper noise
and luminance change. In all cases the secondary watermark was
easy to detect even when the primary one is severely distorted.
Abstract: Non-stationary trend in R-R interval series is
considered as a main factor that could highly influence the evaluation
of spectral analysis. It is suggested to remove trends in order to obtain
reliable results. In this study, three detrending methods, the
smoothness prior approach, the wavelet and the empirical mode
decomposition, were compared on artificial R-R interval series with
four types of simulated trends. The Lomb-Scargle periodogram was
used for spectral analysis of R-R interval series. Results indicated that
the wavelet method showed a better overall performance than the other
two methods, and more time-saving, too. Therefore it was selected for
spectral analysis of real R-R interval series of thirty-seven healthy
subjects. Significant decreases (19.94±5.87% in the low frequency
band and 18.97±5.78% in the ratio (p
Abstract: The prediction of transmembrane helical segments
(TMHs) in membrane proteins is an important field in the
bioinformatics research. In this paper, a new method based on discrete
wavelet transform (DWT) has been developed to predict the number
and location of TMHs in membrane proteins. PDB coded as 1KQG
was chosen as an example to describe the prediction of the number and
location of TMHs in membrane proteins by using this method. To
access the effect of the method, 80 proteins with known 3D-structure
from Mptopo database are chosen at random as the test objects
(including 325 TMHs), 308 of which can be predicted accurately, the
average predicted accuracy is 96.3%. In addition, the above 80
membrane proteins are divided into 13 groups according to their
function and type. In particular, the results of the prediction of TMHs
of the 13 groups are satisfying.
Abstract: Real world Speaker Identification (SI) application
differs from ideal or laboratory conditions causing perturbations that
leads to a mismatch between the training and testing environment
and degrade the performance drastically. Many strategies have been
adopted to cope with acoustical degradation; wavelet based Bayesian
marginal model is one of them. But Bayesian marginal models
cannot model the inter-scale statistical dependencies of different
wavelet scales. Simple nonlinear estimators for wavelet based
denoising assume that the wavelet coefficients in different scales are
independent in nature. However wavelet coefficients have significant
inter-scale dependency. This paper enhances this inter-scale
dependency property by a Circularly Symmetric Probability Density
Function (CS-PDF) related to the family of Spherically Invariant
Random Processes (SIRPs) in Log Gabor Wavelet (LGW) domain
and corresponding joint shrinkage estimator is derived by Maximum
a Posteriori (MAP) estimator. A framework is proposed based on
these to denoise speech signal for automatic speaker identification
problems. The robustness of the proposed framework is tested for
Text Independent Speaker Identification application on 100 speakers
of POLYCOST and 100 speakers of YOHO speech database in three
different noise environments. Experimental results show that the
proposed estimator yields a higher improvement in identification
accuracy compared to other estimators on popular Gaussian Mixture
Model (GMM) based speaker model and Mel-Frequency Cepstral
Coefficient (MFCC) features.
Abstract: A study of the obtainable watermark data rate for information hiding algorithms is presented in this paper. As the perceptual entropy for wideband monophonic audio signals is in the range of four to five bits per sample, a significant amount of additional information can be inserted into signal without causing any perceptual distortion. Experimental results showed that transform domain watermark embedding outperforms considerably watermark embedding in time domain and that signal decompositions with a high gain of transform coding, like the wavelet transform, are the most suitable for high data rate information hiding. Keywords?Digital watermarking, information hiding, audio watermarking, watermark data rate.
Abstract: Medical image modalities such as computed
tomography (CT), magnetic resonance imaging (MRI), ultrasound
(US), X-ray are adapted to diagnose disease. These modalities
provide flexible means of reviewing anatomical cross-sections and
physiological state in different parts of the human body. The raw
medical images have a huge file size and need large storage
requirements. So it should be such a way to reduce the size of those
image files to be valid for telemedicine applications. Thus the image
compression is a key factor to reduce the bit rate for transmission or
storage while maintaining an acceptable reproduction quality, but it is
natural to rise the question of how much an image can be compressed
and still preserve sufficient information for a given clinical
application. Many techniques for achieving data compression have
been introduced. In this study, three different MRI modalities which
are Brain, Spine and Knee have been compressed and reconstructed
using wavelet transform. Subjective and objective evaluation has
been done to investigate the clinical information quality of the
compressed images. For the objective evaluation, the results show
that the PSNR which indicates the quality of the reconstructed image
is ranging from (21.95 dB to 30.80 dB, 27.25 dB to 35.75 dB, and
26.93 dB to 34.93 dB) for Brain, Spine, and Knee respectively. For
the subjective evaluation test, the results show that the compression
ratio of 40:1 was acceptable for brain image, whereas for spine and
knee images 50:1 was acceptable.
Abstract: Cryptography provides the secure manner of
information transmission over the insecure channel. It authenticates
messages based on the key but not on the user. It requires a lengthy
key to encrypt and decrypt the sending and receiving the messages,
respectively. But these keys can be guessed or cracked. Moreover,
Maintaining and sharing lengthy, random keys in enciphering and
deciphering process is the critical problem in the cryptography
system. A new approach is described for generating a crypto key,
which is acquired from a person-s iris pattern. In the biometric field,
template created by the biometric algorithm can only be
authenticated with the same person. Among the biometric templates,
iris features can efficiently be distinguished with individuals and
produces less false positives in the larger population. This type of iris
code distribution provides merely less intra-class variability that aids
the cryptosystem to confidently decrypt messages with an exact
matching of iris pattern. In this proposed approach, the iris features
are extracted using multi resolution wavelets. It produces 135-bit iris
codes from each subject and is used for encrypting/decrypting the
messages. The autocorrelators are used to recall original messages
from the partially corrupted data produced by the decryption process.
It intends to resolve the repudiation and key management problems.
Results were analyzed in both conventional iris cryptography system
(CIC) and non-repudiation iris cryptography system (NRIC). It
shows that this new approach provides considerably high
authentication in enciphering and deciphering processes.
Abstract: To model the human visual system (HVS) in the region of interest, we propose a new objective metric evaluation adapted to wavelet foveation-based image compression quality measurement, which exploits a foveation setup filter implementation technique in the DWT domain, based especially on the point and region of fixation of the human eye. This model is then used to predict the visible divergences between an original and compressed image with respect to this region field and yields an adapted and local measure error by removing all peripheral errors. The technique, which we call foveation wavelet visible difference prediction (FWVDP), is demonstrated on a number of noisy images all of which have the same local peak signal to noise ratio (PSNR), but visibly different errors. We show that the FWVDP reliably predicts the fixation areas of interest where error is masked, due to high image contrast, and the areas where the error is visible, due to low image contrast. The paper also suggests ways in which the FWVDP can be used to determine a visually optimal quantization strategy for foveation-based wavelet coefficients and to produce a quantitative local measure of image quality.