Abstract: During the process of compaction in Hot-Mix Asphalt
(HMA) mixtures, the distance between aggregate particles decreases
as they come together and eliminate air-voids. By measuring the
inter-particle distances in a cut-section of a HMA sample the degree
of compaction can be estimated. For this, a calibration curve is
generated by computer simulation technique when the gradation and
asphalt content of the HMA mixture are known. A two-dimensional
cross section of HMA specimen was simulated using the mixture
design information (gradation, asphalt content and air-void content).
Nearest neighbor distance methods such as Delaunay triangulation
were used to study the changes in inter-particle distance and area
distribution during the process of compaction in HMA. Such
computer simulations would enable making several hundreds of
repetitions in a short period of time without the necessity to compact
and analyze laboratory specimens in order to obtain good statistics on
the parameters defined. The distributions for the statistical
parameters based on computer simulations showed similar trends as
those of laboratory specimens.
Abstract: DNA microarray technology is widely used by
geneticists to diagnose or treat diseases through gene expression.
This technology is based on the hybridization of a tissue-s DNA
sequence into a substrate and the further analysis of the image
formed by the thousands of genes in the DNA as green, red or yellow
spots. The process of DNA microarray image analysis involves
finding the location of the spots and the quantification of the
expression level of these. In this paper, a tool to perform DNA
microarray image analysis is presented, including a spot addressing
method based on the image projections, the spot segmentation
through contour based segmentation and the extraction of relevant
information due to gene expression.
Abstract: This paper presents an approach for early breast
cancer diagnostic by employing combination of artificial neural
networks (ANN) and multiwaveletpacket based subband image
decomposition. The microcalcifications correspond to high-frequency
components of the image spectrum, detection of microcalcifications
is achieved by decomposing the mammograms into different
frequency subbands,, reconstructing the mammograms from the
subbands containing only high frequencies. For this approach we
employed different types of multiwaveletpacket. We used the result
as an input of neural network for classification. The proposed
methodology is tested using the Nijmegen and the Mammographic
Image Analysis Society (MIAS) mammographic databases and
images collected from local hospitals. Results are presented as the
receiver operating characteristic (ROC) performance and are
quantified by the area under the ROC curve.
Abstract: This paper reports a new pattern recognition approach for face recognition. The biological model of light receptors - cones and rods in human eyes and the way they are associated with pattern vision in human vision forms the basis of this approach. The functional model is simulated using CWD and WPD. The paper also discusses the experiments performed for face recognition using the features extracted from images in the AT & T face database. Artificial Neural Network and k- Nearest Neighbour classifier algorithms are employed for the recognition purpose. A feature vector is formed for each of the face images in the database and recognition accuracies are computed and compared using the classifiers. Simulation results show that the proposed method outperforms traditional way of feature extraction methods prevailing for pattern recognition in terms of recognition accuracy for face images with pose and illumination variations.
Abstract: This paper presents a novel approach to assessing textile porosity by the application of the image analysis techniques. The images of different types of sample fabrics, taken through a microscope when the fabric is placed over a constant light source,transfer the problem into the image analysis domain. Indeed, porosity can thus be expressed in terms of a brightness percentage index calculated on the digital microscope image. Furthermore, it is meaningful to compare the brightness percentage index with the air permeability and the tightness indices of each fabric type. We have experimentally shown that there exists an approximately linear relation between brightness percentage and air permeability indices.
Abstract: Real-time hand tracking is a challenging task in many
computer vision applications such as gesture recognition. This paper
proposes a robust method for hand tracking in a complex environment
using Mean-shift analysis and Kalman filter in conjunction with 3D
depth map. The depth information solve the overlapping problem
between hands and face, which is obtained by passive stereo measuring
based on cross correlation and the known calibration data of
the cameras. Mean-shift analysis uses the gradient of Bhattacharyya
coefficient as a similarity function to derive the candidate of the hand
that is most similar to a given hand target model. And then, Kalman
filter is used to estimate the position of the hand target. The results
of hand tracking, tested on various video sequences, are robust to
changes in shape as well as partial occlusion.
Abstract: This paper aims to present a survey of object
recognition/classification methods based on image moments. We
review various types of moments (geometric moments, complex
moments) and moment-based invariants with respect to various
image degradations and distortions (rotation, scaling, affine
transform, image blurring, etc.) which can be used as shape
descriptors for classification. We explain a general theory how to
construct these invariants and show also a few of them in explicit
forms. We review efficient numerical algorithms that can be used
for moment computation and demonstrate practical examples of
using moment invariants in real applications.
Abstract: The two-dimensional gel electrophoresis method
(2-DE) is widely used in Proteomics to separate thousands of proteins
in a sample. By comparing the protein expression levels of proteins in
a normal sample with those in a diseased one, it is possible to identify
a meaningful set of marker proteins for the targeted disease. The major
shortcomings of this approach involve inherent noises and irregular
geometric distortions of spots observed in 2-DE images. Various
experimental conditions can be the major causes of these problems. In
the protein analysis of samples, these problems eventually lead to
incorrect conclusions. In order to minimize the influence of these
problems, this paper proposes a partition based pair extension method
that performs spot-matching on a set of gel images multiple times and
segregates more reliable mapping results which can improve the
accuracy of gel image analysis. The improved accuracy of the
proposed method is analyzed through various experiments on real
2-DE images of human liver tissues.
Abstract: Pattern recognition and image recognition methods are commonly developed and tested using testbeds, which contain known responses to a query set. Until now, testbeds available for image analysis and content-based image retrieval (CBIR) have been scarce and small-scale. Here we present the one million images CEA-List Image Collection (CLIC) testbed that we have produced, and report on our use of this testbed to evaluate image analysis merging techniques. This testbed will soon be made publicly available through the EU MUSCLE Network of Excellence.
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 SAR (synthetic aperture radar) 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 real SAR images
and its performance in terms of the mean square error (MSE) metric
was calculated. We showed that the SVM-detected SAR 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
(the detection hypotheses) in the original images.
Abstract: This paper presents a new color face image database
for benchmarking of automatic face detection algorithms and human
skin segmentation techniques. It is named the VT-AAST image
database, and is divided into four parts. Part one is a set of 286 color
photographs that include a total of 1027 faces in the original format
given by our digital cameras, offering a wide range of difference in
orientation, pose, environment, illumination, facial expression and
race. Part two contains the same set in a different file format. The
third part is a set of corresponding image files that contain human
colored skin regions resulting from a manual segmentation
procedure. The fourth part of the database has the same regions
converted into grayscale. The database is available on-line for
noncommercial use. In this paper, descriptions of the database
development, organization, format as well as information needed for
benchmarking of algorithms are depicted in detail.
Abstract: Autofluorescence (AF) bronchoscopy is an
established method to detect dysplasia and carcinoma in situ (CIS).
For this reason the “Sotiria" Hospital uses the Karl Storz D-light
system. However, in early tumor stages the visualization is not that
obvious. With the help of a PC, we analyzed the color images we
captured by developing certain tools in Matlab®. We used statistical
methods based on texture analysis, signal processing methods based
on Gabor models and conversion algorithms between devicedependent
color spaces. Our belief is that we reduced the error made
by the naked eye. The tools we implemented improve the quality of
patients' life.
Abstract: The fine structure of supercavitation in the wake of a
symmetrical cylinder is studied with high-speed video cameras. The
flow is observed in a cavitation tunnel at the speed of 8m/sec when the
sidewall and the wake are partially filled with the massive cavitation
bubbles. The present experiment observed that a two-dimensional
ripple wave with a wave length of 0.3mm is propagated in a
downstream direction, and then abruptly increases to a thicker
three-dimensional layer. IR-photography recorded that the wakes
originated from the horseshoe vortexes alongside the cylinder. The
wake was developed to inside the dead water zone, which absorbed the
bubbly wake propelled from the separated vortices at the center of the
cylinder. A remote sensing classification technique (maximum most
likelihood) determined that the surface porosity was 0.2, and the mean
speed in the mixed wake was 7m/sec. To confirm the existence of
two-dimensional wave motions in the interface, the experiments were
conducted at a very low frequency, and showed similar gravity waves
in both the upper and lower interfaces.
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: This paper presents a new system developed in Java®
for pattern recognition and pattern summarisation in multi-band
(RGB) satellite images. The system design is described in some
detail. Results of testing the system to analyse and summarise
patterns in SPOT MS images and LANDSAT images are also
discussed.
Abstract: Advances in clinical medical imaging have brought about the routine production of vast numbers of medical images that need to be analyzed. As a result an enormous amount of computer vision research effort has been targeted at achieving automated medical image analysis. Computed Tomography (CT) is highly accurate for diagnosing liver tumors. This study aimed to evaluate the potential role of the wavelet and the neural network in the differential diagnosis of liver tumors in CT images. The tumors considered in this study are hepatocellular carcinoma, cholangio carcinoma, hemangeoma and hepatoadenoma. Each suspicious tumor region was automatically extracted from the CT abdominal images and the textural information obtained was used to train the Probabilistic Neural Network (PNN) to classify the tumors. Results obtained were evaluated with the help of radiologists. The system differentiates the tumor with relatively high accuracy and is therefore clinically useful.
Abstract: Current advancements in nanotechnology are dependent on the capabilities that can enable nano-scientists to extend their eyes and hands into the nano-world. For this purpose, a haptics (devices capable of recreating tactile or force sensations) based system for AFM (Atomic Force Microscope) is proposed. The system enables the nano-scientists to touch and feel the sample surfaces, viewed through AFM, in order to provide them with better understanding of the physical properties of the surface, such as roughness, stiffness and shape of molecular architecture. At this stage, the proposed work uses of ine images produced using AFM and perform image analysis to create virtual surfaces suitable for haptics force analysis. The research work is in the process of extension from of ine to online process where interaction will be done directly on the material surface for realistic analysis.
Abstract: In recent five decades, textured yarns of polyester fiber produced by false twist method are the most
important and mass-produced manmade fibers. There are
many parameters of cross section which affect the physical and mechanical properties of textured yarns. These parameters
are surface area, perimeter, equivalent diameter, large
diameter, small diameter, convexity, stiffness, eccentricity, and hydraulic diameter. These parameters were evaluated by
digital image processing techniques. To find trends between production criteria and evaluated parameters of cross section, three criteria of production line have been adjusted and different types of yarns were produced. These criteria are
temperature, drafting ratio, and D/Y ratio. Finally the relations between production criteria and cross section parameters were
considered. The results showed that the presented technique can recognize and measure the parameters of fiber cross section in acceptable accuracy. Also, the optimum condition
of adjustments has been estimated from results of image analysis evaluation.
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: Extraction of edge-end-pixels is an important step for the edge linking process to achieve edge-based image segmentation. This paper presents an algorithm to extract edge-end pixels together with their directional sensitivities as an augmentation to the currently available mathematical models. The algorithm is implemented in the Java environment because of its inherent compatibility with web interfaces since its main use is envisaged to be for remote image analysis on a virtual instrumentation platform.