Abstract: In some applications, such as image recognition or
compression, segmentation refers to the process of partitioning a
digital image into multiple segments. Image segmentation is typically
used to locate objects and boundaries (lines, curves, etc.) in images.
Image segmentation is to classify or cluster an image into several
parts (regions) according to the feature of image, for example, the
pixel value or the frequency response. More precisely, image
segmentation is the process of assigning a label to every pixel in an
image such that pixels with the same label share certain visual
characteristics. The result of image segmentation is a set of segments
that collectively cover the entire image, or a set of contours extracted
from the image. Several image segmentation algorithms were
proposed to segment an image before recognition or compression. Up
to now, many image segmentation algorithms exist and be
extensively applied in science and daily life. According to their
segmentation method, we can approximately categorize them into
region-based segmentation, data clustering, and edge-base
segmentation. In this paper, we give a study of several popular image
segmentation algorithms that are available.
Abstract: This paper presents a new approach for the prob-ability density function estimation using the Support Vector Ma-chines (SVM) and the Expectation Maximization (EM) algorithms.In the proposed approach, an advanced algorithm for the SVM den-sity estimation which incorporates the Mean Field theory in the learning process is used. Instead of using ad-hoc values for the para-meters of the kernel function which is used by the SVM algorithm,the proposed approach uses the EM algorithm for an automatic optimization of the kernel. Experimental evaluation using simulated data set shows encouraging results.
Abstract: In this paper we present a novel approach for density estimation. The proposed approach is based on using the logistic regression model to get initial density estimation for the given empirical density. The empirical data does not exactly follow the logistic regression model, so, there will be a deviation between the empirical density and the density estimated using logistic regression model. This deviation may be positive and/or negative. In this paper we use a linear combination of Gaussian (LCG) with positive and negative components as a model for this deviation. Also, we will use the expectation maximization (EM) algorithm to estimate the parameters of LCG. Experiments on real images demonstrate the accuracy of our approach.
Abstract: This paper proposes new enhancement models to the
methods of nonlinear anisotropic diffusion to greatly reduce speckle
and preserve image features in medical ultrasound images. By
incorporating local physical characteristics of the image, in this case
scatterer density, in addition to the gradient, into existing tensorbased
image diffusion methods, we were able to greatly improve the
performance of the existing filtering methods, namely edge
enhancing (EE) and coherence enhancing (CE) diffusion. The new
enhancement methods were tested using various ultrasound images,
including phantom and some clinical images, to determine the
amount of speckle reduction, edge, and coherence enhancements.
Scatterer density weighted nonlinear anisotropic diffusion
(SDWNAD) for ultrasound images consistently outperformed its
traditional tensor-based counterparts that use gradient only to weight
the diffusivity function. SDWNAD is shown to greatly reduce
speckle noise while preserving image features as edges, orientation
coherence, and scatterer density. SDWNAD superior performances
over nonlinear coherent diffusion (NCD), speckle reducing
anisotropic diffusion (SRAD), adaptive weighted median filter
(AWMF), wavelet shrinkage (WS), and wavelet shrinkage with
contrast enhancement (WSCE), make these methods ideal
preprocessing steps for automatic segmentation in ultrasound
imaging.
Abstract: In manufacturing industries, development of measurement leads to increase the number of monitoring variables and eventually the importance of multivariate control comes to the fore. Statistical process control (SPC) is one of the most widely used as multivariate control chart. Nevertheless, SPC is restricted to apply in processes because its assumption of data as following specific distribution. Unfortunately, process data are composed by the mixture of several processes and it is hard to estimate as one certain distribution. To alternative conventional SPC, therefore, nonparametric control chart come into the picture because of the strength of nonparametric control chart, the absence of parameter estimation. SVDD based control chart is one of the nonparametric control charts having the advantage of flexible control boundary. However,basic concept of SVDD has been an oversight to the important of data characteristic, density distribution. Therefore, we proposed DW-SVDD (Density Weighted SVDD) to cover up the weakness of conventional SVDD. DW-SVDD makes a new attempt to consider dense of data as introducing the notion of density Weight. We extend as control chart using new proposed SVDD and a simulation study of various distributional data is conducted to demonstrate the improvement of performance.
Abstract: This paper proposes a method, combining color and
layout features, for identifying documents captured from lowresolution
handheld devices. On one hand, the document image color
density surface is estimated and represented with an equivalent
ellipse and on the other hand, the document shallow layout structure
is computed and hierarchically represented. The combined color and
layout features are arranged in a symbolic file, which is unique for
each document and is called the document-s visual signature. Our
identification method first uses the color information in the
signatures in order to focus the search space on documents having a
similar color distribution, and finally selects the document having the
most similar layout structure in the remaining search space. Finally,
our experiment considers slide documents, which are often captured
using handheld devices.
Abstract: Traffic density, an indicator of traffic
conditions, is one of the most critical characteristics to
Intelligent Transport Systems (ITS). This paper investigates
recursive traffic density estimation using the information
provided from inductive loop detectors. On the basis of the
phenomenological relationship between speed and density, the
existing studies incorporate a state space model and update the
density estimate using vehicular speed observations via the
extended Kalman filter, where an approximation is made
because of the linearization of the nonlinear observation
equation. In practice, this may lead to substantial estimation
errors. This paper incorporates a suitable transformation to
deal with the nonlinear observation equation so that the
approximation is avoided when using Kalman filter to
estimate the traffic density. A numerical study is conducted. It
is shown that the developed method outperforms the existing
methods for traffic density estimation.
Abstract: The density estimates considered in this paper comprise
a base density and an adjustment component consisting of a linear
combination of orthogonal polynomials. It is shown that, in the
context of density approximation, the coefficients of the linear combination
can be determined either from a moment-matching technique
or a weighted least-squares approach. A kernel representation of
the corresponding density estimates is obtained. Additionally, two
refinements of the Kronmal-Tarter stopping criterion are proposed
for determining the degree of the polynomial adjustment. By way of
illustration, the density estimation methodology advocated herein is
applied to two data sets.
Abstract: This paper proposes a method, combining color and layout features, for identifying documents captured from low-resolution handheld devices. On one hand, the document image color density surface is estimated and represented with an equivalent ellipse and on the other hand, the document shallow layout structure is computed and hierarchically represented. Our identification method first uses the color information in the documents in order to focus the search space on documents having a similar color distribution, and finally selects the document having the most similar layout structure in the remaining of the search space.
Abstract: Dengue is a mosquito-borne infection that has peaked to an alarming rate in recent decades. It can be found in tropical and sub-tropical climate. In Malaysia, dengue has been declared as one of the national health threat to the public. This study aimed to map the spatial distributions of dengue cases in the district of Hulu Langat, Selangor via a combination of Geographic Information System (GIS) and spatial statistic tools. Data related to dengue was gathered from the various government health agencies. The location of dengue cases was geocoded using a handheld GPS Juno SB Trimble. A total of 197 dengue cases occurring in 2003 were used in this study. Those data then was aggregated into sub-district level and then converted into GIS format. The study also used population or demographic data as well as the boundary of Hulu Langat. To assess the spatial distribution of dengue cases three spatial statistics method (Moran-s I, average nearest neighborhood (ANN) and kernel density estimation) were applied together with spatial analysis in the GIS environment. Those three indices were used to analyze the spatial distribution and average distance of dengue incidence and to locate the hot spot of dengue cases. The results indicated that the dengue cases was clustered (p < 0.01) when analyze using Moran-s I with z scores 5.03. The results from ANN analysis showed that the average nearest neighbor ratio is less than 1 which is 0.518755 (p < 0.0001). From this result, we can expect the dengue cases pattern in Hulu Langat district is exhibiting a cluster pattern. The z-score for dengue incidence within the district is -13.0525 (p < 0.0001). It was also found that the significant spatial autocorrelation of dengue incidences occurs at an average distance of 380.81 meters (p < 0.0001). Several locations especially residential area also had been identified as the hot spots of dengue cases in the district.
Abstract: In this paper we present an efficient system for
independent speaker speech recognition based on neural network
approach. The proposed architecture comprises two phases: a
preprocessing phase which consists in segmental normalization and
features extraction and a classification phase which uses neural
networks based on nonparametric density estimation namely the
general regression neural network (GRNN). The relative
performances of the proposed model are compared to the similar
recognition systems based on the Multilayer Perceptron (MLP), the
Recurrent Neural Network (RNN) and the well known Discrete
Hidden Markov Model (HMM-VQ) that we have achieved also.
Experimental results obtained with Arabic digits have shown that the
use of nonparametric density estimation with an appropriate
smoothing factor (spread) improves the generalization power of the
neural network. The word error rate (WER) is reduced significantly
over the baseline HMM method. GRNN computation is a successful
alternative to the other neural network and DHMM.