Abstract: Titanium gels doped with water-soluble cationic porphyrin were synthesized by the sol–gel polymerization of Ti (OC4H9)4. In this work we investigate the spectroscopic properties along with SEM images of tetra carboxyl phenyl porphyrin when incorporated into porous matrix produced by the sol–gel technique.
Abstract: In this paper, we propose ablock-wise watermarking scheme for color image authentication to resist malicious tampering of digital media. The thresholding technique is incorporated into the scheme such that the tampered region of the color image can be recovered with high quality while the proofing result is obtained. The watermark for each block consists of its dual authentication data and the corresponding feature information. The feature information for recovery iscomputed bythe thresholding technique. In the proofing process, we propose a dual-option parity check method to proof the validity of image blocks. In the recovery process, the feature information of each block embedded into the color image is rebuilt for high quality recovery. The simulation results show that the proposed watermarking scheme can effectively proof the tempered region with high detection rate and can recover the tempered region with high quality.
Abstract: Large volumes of fingerprints are collected and stored
every day in a wide range of applications, including forensics, access
control etc. It is evident from the database of Federal Bureau of
Investigation (FBI) which contains more than 70 million finger
prints. Compression of this database is very important because of this
high Volume. The performance of existing image coding standards
generally degrades at low bit-rates because of the underlying block
based Discrete Cosine Transform (DCT) scheme. Over the past
decade, the success of wavelets in solving many different problems
has contributed to its unprecedented popularity. Due to
implementation constraints scalar wavelets do not posses all the
properties which are needed for better performance in compression.
New class of wavelets called 'Multiwavelets' which posses more
than one scaling filters overcomes this problem. The objective of this
paper is to develop an efficient compression scheme and to obtain
better quality and higher compression ratio through multiwavelet
transform and embedded coding of multiwavelet coefficients through
Set Partitioning In Hierarchical Trees algorithm (SPIHT) algorithm.
A comparison of the best known multiwavelets is made to the best
known scalar wavelets. Both quantitative and qualitative measures of
performance are examined for Fingerprints.
Abstract: Identity verification of authentic persons by their multiview faces is a real valued problem in machine vision. Multiview faces are having difficulties due to non-linear representation in the feature space. This paper illustrates the usability of the generalization of LDA in the form of canonical covariate for face recognition to multiview faces. In the proposed work, the Gabor filter bank is used to extract facial features that characterized by spatial frequency, spatial locality and orientation. Gabor face representation captures substantial amount of variations of the face instances that often occurs due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images of rotated profile views produce Gabor faces with high dimensional features vectors. Canonical covariate is then used to Gabor faces to reduce the high dimensional feature spaces into low dimensional subspaces. Finally, support vector machines are trained with canonical sub-spaces that contain reduced set of features and perform recognition task. The proposed system is evaluated with UMIST face database. The experiment results demonstrate the efficiency and robustness of the proposed system with high recognition rates.
Abstract: This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.
Abstract: X-ray mammography is the most effective method for
the early detection of breast diseases. However, the typical diagnostic
signs such as microcalcifications and masses are difficult to detect
because mammograms are of low-contrast and noisy. In this paper, a
new algorithm for image denoising and enhancement in Orthogonal
Polynomials Transformation (OPT) is proposed for radiologists to
screen mammograms. In this method, a set of OPT edge coefficients
are scaled to a new set by a scale factor called OPT scale factor. The
new set of coefficients is then inverse transformed resulting in
contrast improved image. Applications of the proposed method to
mammograms with subtle lesions are shown. To validate the
effectiveness of the proposed method, we compare the results to
those obtained by the Histogram Equalization (HE) and the Unsharp
Masking (UM) methods. Our preliminary results strongly suggest
that the proposed method offers considerably improved enhancement
capability over the HE and UM methods.
Abstract: A new fuzzy filter is presented for noise reduction of
images corrupted with additive noise. The filter consists of two
stages. In the first stage, all the pixels of image are processed for
determining noisy pixels. For this, a fuzzy rule based system
associates a degree to each pixel. The degree of a pixel is a real
number in the range [0,1], which denotes a probability that the pixel
is not considered as a noisy pixel. In the second stage, another fuzzy
rule based system is employed. It uses the output of the previous
fuzzy system to perform fuzzy smoothing by weighting the
contributions of neighboring pixel values. Experimental results are
obtained to show the feasibility of the proposed filter. These results
are also compared to other filters by numerical measure and visual
inspection.
Abstract: Early detection of lung cancer through chest radiography is a widely used method due to its relatively affordable cost. In this paper, an approach to improve lung nodule visualization on chest radiographs is presented. The approach makes use of linear phase high-frequency emphasis filter for digital filtering and
histogram equalization for contrast enhancement to achieve improvements. Results obtained indicate that a filtered image can
reveal sharper edges and provide more details. Also, contrast enhancement offers a way to further enhance the global (or local) visualization by equalizing the histogram of the pixel values within
the whole image (or a region of interest). The work aims to improve lung nodule visualization of chest radiographs to aid detection of lung cancer which is currently the leading cause of cancer deaths worldwide.
Abstract: Because of increasing demands for security in today-s
society and also due to paying much more attention to machine
vision, biometric researches, pattern recognition and data retrieval in
color images, face detection has got more application. In this article
we present a scientific approach for modeling human skin color, and
also offer an algorithm that tries to detect faces within color images
by combination of skin features and determined threshold in the
model. Proposed model is based on statistical data in different color
spaces. Offered algorithm, using some specified color threshold, first,
divides image pixels into two groups: skin pixel group and non-skin
pixel group and then based on some geometric features of face
decides which area belongs to face.
Two main results that we received from this research are as follow:
first, proposed model can be applied easily on different databases and
color spaces to establish proper threshold. Second, our algorithm can
adapt itself with runtime condition and its results demonstrate
desirable progress in comparison with similar cases.
Abstract: This study investigates the relationships between
Work Motivation, Work Stress, and Job Satisfaction toward
cross-strait employees. The target subjects are three
manufacturing firms in Mainland China and Taiwan. Out of 450
distributed surveys, 352 valid surveys were obtained with the
response rate of 78.22%.The findings have addressed three main
pull factors toward cross-strait employees in choosing jobs, which
are (1) high level of firm stability, (2) good firm image, and (3)
good employee benefits. In addition, various employee attributes
exert different impacts on Work Motivation, Work Stress, and
Job Satisfaction. The comparison between expected and actual
perceived Job Satisfaction toward cross-strait employees shows
that “salary" ranks highest regarding expected Job Satisfaction
whereas “co-worker relationship" ranks highest regarding actual
perceived Job Satisfaction, which implies actual perceived Job
Satisfaction do not match employee expectations. Therefore, this
research further concludes that there exists differences between
employees- expected and actual perceived Job Satisfaction.
Abstract: Considering payload, reliability, security and operational lifetime as major constraints in transmission of images we put forward in this paper a steganographic technique implemented at the physical layer. We suggest transmission of Halftoned images (payload constraint) in wireless sensor networks to reduce the amount of transmitted data. For low power and interference limited applications Turbo codes provide suitable reliability. Ensuring security is one of the highest priorities in many sensor networks. The Turbo Code structure apart from providing forward error correction can be utilized to provide for encryption. We first consider the Halftoned image and then the method of embedding a block of data (called secret) in this Halftoned image during the turbo encoding process is presented. The small modifications required at the turbo decoder end to extract the embedded data are presented next. The implementation complexity and the degradation of the BER (bit error rate) in the Turbo based stego system are analyzed. Using some of the entropy based crypt analytic techniques we show that the strength of our Turbo based stego system approaches that found in the OTPs (one time pad).
Abstract: The validity of Herzberg-s Two-Factor Theory of
Motivation was tested empirically by surveying 2372 chemical fiber
employees in 2012. In the valid sample of 1875 respondents, the
degree of overall job satisfaction was more than moderate. The most
highly valued components of job satisfaction were: “corporate image,"
“collaborative working atmosphere," and “supervisor-s expertise";
whereas the lowest mean score was 34.65 for “job rotation and
promotion." The top three job retention options rated by the
participants were “good image of the enterprise," “good
compensation," and “workplace is close to my residence." The overall
evaluation of the level of thriving facilitation workplace reached
almost to “mostly agree." For those participants who chose at least
one motivator as their job retention options had significantly greater
job satisfaction than those who chose only hygiene factors as their
retention options. Therefore, Herzberg-s Two-Factor Theory of
Motivation was proven valid in this study.
Abstract: In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.
Abstract: In this paper, a novel and fast algorithm for segmental
and subsegmental lung vessel segmentation is introduced using
Computed Tomography Angiography images. This process is quite
important especially at the detection of pulmonary embolism, lung
nodule, and interstitial lung disease. The applied method has been
realized at five steps. At the first step, lung segmentation is achieved.
At the second one, images are threshold and differences between the
images are detected. At the third one, left and right lungs are gathered
with the differences which are attained in the second step and Exact
Lung Image (ELI) is achieved. At the fourth one, image, which is
threshold for vessel, is gathered with the ELI. Lastly, identifying and
segmentation of segmental and subsegmental lung vessel have been
carried out thanks to image which is obtained in the fourth step. The
performance of the applied method is found quite well for
radiologists and it gives enough results to the surgeries medically.
Abstract: Most simple nonlinear thresholding rules for
wavelet- based denoising assume that the wavelet coefficients are independent. However, wavelet coefficients of natural images have significant dependencies. This paper attempts to give a recipe for selecting one of the popular image-denoising algorithms based
on VisuShrink, SureShrink, OracleShrink, BayesShrink and BiShrink and also this paper compares different Bivariate models used for image denoising applications. The first part of the paper
compares different Shrinkage functions used for image-denoising.
The second part of the paper compares different bivariate models
and the third part of this paper uses the Bivariate model with modified marginal variance which is based on Laplacian assumption. This paper gives an experimental comparison on six 512x512 commonly used images, Lenna, Barbara, Goldhill,
Clown, Boat and Stonehenge. The following noise powers 25dB,26dB, 27dB, 28dB and 29dB are added to the six standard images and the corresponding Peak Signal to Noise Ratio (PSNR) values
are calculated for each noise level.
Abstract: One of the popular methods for recognition of facial
expressions such as happiness, sadness and surprise is based on
deformation of facial features. Motion vectors which show these
deformations can be specified by the optical flow. In this method, for
detecting emotions, the resulted set of motion vectors are compared
with standard deformation template that caused by facial expressions.
In this paper, a new method is introduced to compute the quantity of
likeness in order to make decision based on the importance of
obtained vectors from an optical flow approach. For finding the
vectors, one of the efficient optical flow method developed by
Gautama and VanHulle[17] is used. The suggested method has been
examined over Cohn-Kanade AU-Coded Facial Expression Database,
one of the most comprehensive collections of test images available.
The experimental results show that our method could correctly
recognize the facial expressions in 94% of case studies. The results
also show that only a few number of image frames (three frames) are
sufficient to detect facial expressions with rate of success of about
83.3%. This is a significant improvement over the available methods.
Abstract: Segmentation is an important step in medical image
analysis and classification for radiological evaluation or computer
aided diagnosis. This paper presents the problem of inaccurate lung
segmentation as observed in algorithms presented by researchers
working in the area of medical image analysis. The different lung
segmentation techniques have been tested using the dataset of 19
patients consisting of a total of 917 images. We obtained datasets of
11 patients from Ackron University, USA and of 8 patients from
AGA Khan Medical University, Pakistan. After testing the algorithms
against datasets, the deficiencies of each algorithm have been
highlighted.
Abstract: Lanthanide-doped upconversion nanoparticles which can convert near-infrared lights to visible lights have attracted growing interest because of their great potentials in fluorescence imaging. Upconversion fluorescence imaging technique with excitation in the near-infrared (NIR) region has been used for imaging of biological cells and tissues. However, improving the detection sensitivity and decreasing the absorption and scattering in biological tissues are as yet unresolved problems. In this present study, a novel NIR-reflected multispectral imaging system was developed for upconversion fluorescent imaging in small animals. Based on this system, we have obtained the high contrast images without the autofluorescence when biocompatible UCPs were injected near the body surface or deeply into the tissue. Furthermore, we have extracted respective spectra of the upconversion fluorescence and relatively quantify the fluorescence intensity with the multispectral analysis. To our knowledge, this is the first time to analyze and quantify the upconversion fluorescence in the small animal imaging.
Abstract: This paper compares Hilditch, Rosenfeld, Zhang-
Suen, dan Nagendraprasad Wang Gupta (NWG) thinning algorithms
for Javanese character image recognition. Thinning is an effective
process when the focus in not on the size of the pattern, but rather on
the relative position of the strokes in the pattern. The research
analyzes the thinning of 60 Javanese characters.
Time-wise, Zhang-Suen algorithm gives the best results with the
average process time being 0.00455188 seconds. But if we look at
the percentage of pixels that meet one-pixel thickness, Rosenfelt
algorithm gives the best results, with a 99.98% success rate. From the
number of pixels that are erased, NWG algorithm gives the best
results with the average number of pixels erased being 84.12%. It can
be concluded that the Hilditch algorithm performs least successfully
compared to the other three algorithms.
Abstract: In this paper we present a combined
hashing/watermarking method for image authentication. A robust
image hash, invariant to legitimate modifications, but fragile to
illegitimate modifications is generated from the local image
characteristics. To increase security of the system the watermark is
generated using the image hash as a key. Quantized Index
Modulation of DCT coefficients is used for watermark embedding.
Watermark detection is performed without use of the original image.
Experimental results demonstrate the effectiveness of the presented
method in terms of robustness and fragility.