Abstract: This study is about the structural transformations of
aluminium examining with the Dynamic Mechanical Thermal
Analyzer (DMTA). It is a faster and simpler measuring method to
make consequence about the metal’s structural transformations. The
device measures the changing of the mechanical characteristics
depending on the heating rate, and concludes certain transformations.
This measuring method fast and shows clean-cut results comparing
the conventional ways.
Applying polymer measuring devices for metal investigations is
not widespread method. One of the adaptable ways is shown in this
study. The article compares the results of the small specimen test and
the DMTA method, considering the temperature and the forming
dependence of recrystallization temperature.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: Mammography has been one of the most reliable
methods for early detection of breast cancer. There are different
lesions which are breast cancer characteristic such as
microcalcifications, masses, architectural distortions and bilateral
asymmetry. One of the major challenges of analysing digital
mammogram is how to extract efficient features from it for accurate
cancer classification. In this paper we proposed a hybrid feature
extraction method to detect and classify all four signs of breast
cancer. The proposed method is based on multiscale surrounding
region dependence method, Gabor filters, multi fractal analysis,
directional and morphological analysis. The extracted features are
input to self adaptive resource allocation network (SRAN) classifier
for classification. The validity of our approach is extensively
demonstrated using the two benchmark data sets Mammographic
Image Analysis Society (MIAS) and Digital Database for Screening
Mammograph (DDSM) and the results have been proved to be
progressive.
Abstract: Object detection using Wavelet Neural Network (WNN) plays a major contribution in the analysis of image processing. Existing cluster-based algorithm for co-saliency object detection performs the work on the multiple images. The co-saliency detection results are not desirable to handle the multi scale image objects in WNN. Existing Super Resolution (SR) scheme for landmark images identifies the corresponding regions in the images and reduces the mismatching rate. But the Structure-aware matching criterion is not paying attention to detect multiple regions in SR images and fail to enhance the result percentage of object detection. To detect the objects in the high-resolution remote sensing images, Tagged Grid Matching (TGM) technique is proposed in this paper. TGM technique consists of the three main components such as object determination, object searching and object verification in WNN. Initially, object determination in TGM technique specifies the position and size of objects in the current image. The specification of the position and size using the hierarchical grid easily determines the multiple objects. Second component, object searching in TGM technique is carried out using the cross-point searching. The cross out searching point of the objects is selected to faster the searching process and reduces the detection time. Final component performs the object verification process in TGM technique for identifying (i.e.,) detecting the dissimilarity of objects in the current frame. The verification process matches the search result grid points with the stored grid points to easily detect the objects using the Gabor wavelet Transform. The implementation of TGM technique offers a significant improvement on the multi-object detection rate, processing time, precision factor and detection accuracy level.
Abstract: Nowadays spinal deformities are very frequent
problems among teenagers. Scheuermann disease is a one
dimensional deformity of the spine, but it has prevalence over 11% of
the children. A traditional technology, the moiré method was used by
us for screening and diagnosing this type of spinal deformity. A
LabVIEW program has been developed to evaluate the moiré pictures
of patients with Scheuermann disease. Two different solutions were
tested in this computer program, the extreme and the inflexion point
calculation methods. Effects using these methods were compared and
according to the results both solutions seemed to be appropriate.
Statistical results showed better efficiency in case of the extreme
search method where the average difference was only 6,09⁰.
Abstract: In this study, we developed an algorithm for detecting
seam cracks in a steel plate. Seam cracks are generated in the edge
region of a steel plate. We used the Gabor filter and an adaptive double
threshold method to detect them. To reduce the number of pseudo
defects, features based on the shape of seam cracks were used. To
evaluate the performance of the proposed algorithm, we tested 989
images with seam cracks and 9470 defect-free images. Experimental
results show that the proposed algorithm is suitable for detecting seam
cracks. However, it should be improved to increase the true positive
rate.
Abstract: Script identification is one of the challenging steps in the development of optical character recognition system for bilingual or multilingual documents. In this paper an attempt is made for identification of English numerals at word level from Punjabi documents by using Gabor features. The support vector machine (SVM) classifier with five fold cross validation is used to classify the word images. The results obtained are quite encouraging. Average accuracy with RBF kernel, Polynomial and Linear Kernel functions comes out to be greater than 99%.
Abstract: This paper presents an automated inspection algorithm
for a thick plate. Thick plates typically have various types of surface
defects, such as scabs, scratches, and roller marks. These defects have
individual characteristics including brightness and shape. Therefore, it
is not simple to detect all the defects. In order to solve these problems
and to detect defects more effectively, we propose a dual light
switching lighting method and a defect detection algorithm based on
Gabor filters.
Abstract: Image retrieval is a topic where scientific interest is currently high. The important steps associated with image retrieval system are the extraction of discriminative features and a feasible similarity metric for retrieving the database images that are similar in content with the search image. Gabor filtering is a widely adopted technique for feature extraction from the texture images. The recently proposed sparsity promoting l1-norm minimization technique finds the sparsest solution of an under-determined system of linear equations. In the present paper, the l1-norm minimization technique as a similarity metric is used in image retrieval. It is demonstrated through simulation results that the l1-norm minimization technique provides a promising alternative to existing similarity metrics. In particular, the cases where the l1-norm minimization technique works better than the Euclidean distance metric are singled out.
Abstract: For about two decades scientists have been
developing techniques for enhancing the quality of medical images
using Fourier transform, DWT (Discrete wavelet transform),PDE
model etc., Gabor wavelet on hexagonal sampled grid of the images
is proposed in this work. This method has optimal approximation
theoretic performances, for a good quality image. The computational
cost is considerably low when compared to similar processing in the
rectangular domain. As X-ray images contain light scattered pixels,
instead of unique sigma, the parameter sigma of 0.5 to 3 is found to
satisfy most of the image interpolation requirements in terms of high
Peak Signal-to-Noise Ratio (PSNR) , lower Mean Squared Error
(MSE) and better image quality by adopting windowing technique.
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: This paper presents an online method that learns the
corresponding points of an object from un-annotated grayscale images
containing instances of the object. In the first image being
processed, an ensemble of node points is automatically selected
which is matched in the subsequent images. A Bayesian posterior
distribution for the locations of the nodes in the images is formed.
The likelihood is formed from Gabor responses and the prior assumes
the mean shape of the node ensemble to be similar in a translation
and scale free space. An association model is applied for separating
the object nodes and background nodes. The posterior distribution is
sampled with Sequential Monte Carlo method. The matched object
nodes are inferred to be the corresponding points of the object
instances. The results show that our system matches the object nodes
as accurately as other methods that train the model with annotated
training images.
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: This paper presents a method for the detection of OD in the retina which takes advantage of the powerful preprocessing techniques such as the contrast enhancement, Gabor wavelet transform for vessel segmentation, mathematical morphology and Earth Mover-s distance (EMD) as the matching process. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Vessel segmentation method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel-s feature vector. Feature vectors are composed of the pixel-s intensity and 2D Gabor wavelet transform responses taken at multiple scales. A simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity using the EMD. The minimum distance provides an estimate of the OD center coordinates. The method-s performance is evaluated on publicly available DRIVE and STARE databases. On the DRIVE database the OD center was detected correctly in all of the 40 images (100%) and on the STARE database the OD was detected correctly in 76 out of the 81 images, even in rather difficult pathological situations.
Abstract: In this paper we present the deep study about the Bio-
Medical Images and tag it with some basic extracting features (e.g.
color, pixel value etc). The classification is done by using a nearest
neighbor classifier with various distance measures as well as the
automatic combination of classifier results. This process selects a
subset of relevant features from a group of features of the image. It
also helps to acquire better understanding about the image by
describing which the important features are. The accuracy can be
improved by increasing the number of features selected. Various
types of classifications were evolved for the medical images like
Support Vector Machine (SVM) which is used for classifying the
Bacterial types. Ant Colony Optimization method is used for optimal
results. It has high approximation capability and much faster
convergence, Texture feature extraction method based on Gabor
wavelets etc..
Abstract: Gabor-based face representation has achieved enormous success in face recognition. This paper addresses a novel algorithm for face recognition using neural networks trained by Gabor features. The system is commenced on convolving a face image with a series of Gabor filter coefficients at different scales and orientations. Two novel contributions of this paper are: scaling of rms contrast and introduction of fuzzily skewed filter. The neural network employed for face recognition is based on the multilayer perceptron (MLP) architecture with backpropagation algorithm and incorporates the convolution filter response of Gabor jet. The effectiveness of the algorithm has been justified over a face database with images captured at different illumination conditions.
Abstract: Starting from a biologically inspired framework, Gabor filters were built up from retinal filters via LMSE algorithms. Asubset of retinal filter kernels was chosen to form a particular Gabor filter by using a weighted sum. One-dimensional optimization approaches were shown to be inappropriate for the problem. All model parameters were fixed with biological or image processing constraints. Detailed analysis of the optimization procedure led to the introduction of a minimization constraint. Finally, quantization of weighting factors was investigated. This resulted in an optimized cascaded structure of a Gabor filter bank implementation with lower computational cost.
Abstract: We here propose improved version of elastic graph matching (EGM) as a face detector, called the multi-scale EGM (MS-EGM). In this improvement, Gabor wavelet-based pyramid reduces computational complexity for the feature representation often used in the conventional EGM, but preserving a critical amount of information about an image. The MS-EGM gives us higher detection performance than Viola-Jones object detection algorithm of the AdaBoost Haar-like feature cascade. We also show rapid detection speeds of the MS-EGM, comparable to the Viola-Jones method. We find fruitful benefits in the MS-EGM, in terms of topological feature representation for a face.
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: Facial recognition and expression analysis is rapidly
becoming an area of intense interest in computer science and humancomputer
interaction design communities. The most expressive way
humans display emotions is through facial expressions. In this paper
skin and non-skin pixels were separated. Face regions were extracted
from the detected skin regions. Facial expressions are analyzed from
facial images by applying Gabor wavelet transform (GWT) and
Discrete Cosine Transform (DCT) on face images. Radial Basis
Function (RBF) Network is used to identify the person and to classify
the facial expressions. Our method reliably works even with faces,
which carry heavy expressions.