• Journal of Korea Multimedia Society
• /
• v.15 no.1
• /
• pp.63-70
• /
• 2012

In this paper an extension to an original active shape model(ASM) for facial feature extraction is presented. The original ASM suffers from poor shape alignment by aligning the shape model to a new instant of the object in a given image using a simple similarity transformation. It exploits only informations such as scale, rotation and shift in horizontal and vertical directions, which does not cope effectively with the complex pose variation. To solve the problem, new shape alignment with 6 degrees of freedom is derived, which corresponds to an affine transformation. Another extension is to speed up the calculation of the Mahalanobis distance for 2-D profiles by trimming the profile covariance matrices. Extensive experiment is conducted with several images of varying poses to check the performance of the proposed method to segment the human faces.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.568-571
• /
• 1997

Recently, the number of techniques for analyzing medical images has been increasing in computer vision, employing X-ray CT images, ultrasound images, MR images, moire topographic images, etc. Spinal deformity is a serious problem especially for teenagers and medical doctors inspect moire topographic images of their backs visually for the primary screening. If a subject is normal, the moire image is almost symmetric with respect to the middle line of the subject's back, otherwise it shows asymmetric shape. In this paper, an image analysis technique is described for discriminating suspicious cases from normal in human spinal deformity by recognizing asymmetric moire images of human backs. The principal axes which are sensitive to asymmetry of the moire image are extracted at two parts on a subject's back and their angles are evaluated with respect to the detected middle line of the back. The two angles compose a 2-D feature space and inspected cases are divided into two clusters in the space by a linear discriminant function based on the Mahalanobis distance. Given 120 cases, 60 normal and 60 abnormal, the leave-out method was applied for the recognition and 75% recognition rate was achieved.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.2059-2064
• /
• 2004

Texture analysis is an important role for automatic visual insfection. This paper presents an application of wavelet extension and Gray level co-occurrence matrix (GLCM) for detection of defect encountered in textured images. Texture characteristic in low quality images is not to easy task to perform caused by noise, low frequency and small dimension. In order to solve this problem, we have developed a procedure called wavelet image extension. Wavelet extension procedure is used to determine the frequency bands carrying the most information about the texture by decomposing images into multiple frequency bands and to form an image approximation with higher resolution. Thus, wavelet extension procedure offers the ability to robust feature extraction in images. Then the features are extracted from the co-occurrence matrices computed from the sub-bands which performed by partitioning the texture image into sub-window. In the detection part, Mahalanobis distance classifier is used to decide whether the test image is defective or non defective.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.6
• /
• pp.3104-3120
• /
• 2017

The intelligent monitoring system has been successfully applied in many fields such as: monitoring of production lines, transportation, etc. Smart surveillance systems have been developed and proven effective in some specific areas such as monitoring of human activity, traffic, etc. Most of critical application monitoring systems involve object tracking as one of the key steps. However, task of tracking of moving object is not easy. In this paper, the authors propose a method to implement human object tracking in outdoor environment based on human features in shearlet domain. The proposed method uses shearlet transform which combines the human features with context-sensitiveness in order to improve the accuracy of human tracking. The proposed algorithm not only improves the edge accuracy, but also reduces wrong positions of the object between the frames. The authors validated the proposed method by calculating Euclidean distance and Mahalanobis distance values between centre of actual object and centre of tracked object, and it has been found that the proposed method gives better result than the other recent available methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.4
• /
• pp.231-240
• /
• 2017

This paper aims to study a method to estimate precise carbon absorption by quantification of forest information that uses accurate LiDAR data, hyperspectral image. To estimate precise carbon absorption value by using spatial data, a problem was found out of carbon absorption value estimation method with statistical method, which is already existed method, and then offered optimized carbon absorption estimation method with spatial information by analyzing with methods of compare digital aerial photogrammetry and LiDAR data. It turned out possible Precise classification and quantification in case of using LiDAR and hyperspectral image. Various classification of tree species was possible with use of LiDAR and hyperspectral image. Classification of hyperspectral image was matched in general with field survey and Mahalanobis distance classification method. Precise forest resources could be extracted using high density LiDAR data. Compared with existing method, 19.7% in forest area, 19.2% in total carbon absorption, 0.9% in absorption per unit area of difference created, and improvement was found out to be estimated precisely in international code.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.10 no.5
• /
• pp.617-628
• /
• 2018

Several techniques have been developed in the last years for energy conversion and aeronautic propulsion plants monitoring and diagnostics, to ensure non-stop availability and safety, mainly based on machine learning and pattern recognition methods, which need large databases of measures. This paper aims to describe a simulation based monitoring and diagnostic method to overcome the lack of data. An application on a gas turbine powered frigate is shown. A MATLAB-SIMULINK(R) model of the frigate propulsion system has been used to generate a database of different faulty conditions of the plant. A monitoring and diagnostic system, based on Mahalanobis distance and artificial neural networks have been developed. Experimental data measured during the sea trials have been used for model calibration and validation. Test runs of the procedure have been carried out in a number of simulated degradation cases: in all the considered cases, malfunctions have been successfully detected by the developed model.

• Korean journal of applied entomology
• /
• v.32 no.1
• /
• pp.68-75
• /
• 1993

Morphometric investigations of the leg characters of both sexes of brachypterous Korean N. lugens biotypes were made. Simple and multivariate statistical analyses revealed that the three N. lugens biotypes differed from one another. The amount of variation and segregation between and among the three biotype populations were approximated by the scatter plot diagrams based on the computed discriminant scores. The variables of leg characters provided the most significant segregations of three biotype populations, thus, categorizing the three biotypes as distinct intraspecific populations of N. lugens.

• KOREAN JOURNAL OF CROP SCIENCE
• /
• v.30 no.4
• /
• pp.375-380
• /
• 1985

Single linkage dendrograms by Mahalanobis's D$^2$, Q correlation, and distance from Principal Component Analysis, respectively, were made to eight rice breeding lines in the none and high fertilizer levels. The dendrograms in the two fertilizer levels were similar in shape. The shape of dendrograms by D$^2$ and Q correlation were identical and they were very similar in shape to that by PCA in the both fertilizer levels.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
• /
• 2001.06a
• /
• pp.4106-4106
• /
• 2001

A method to discriminate human gallstones by nea. infrared(NIR) spectrometry using a soft independent modeling of class analogy (SIMCA) has been studied. The fifty NIR spectra of gallstones in the wavenumber range from 4500 to 10,000 cm$\^$-1/ were measured. The forty samples were classified to three classes, cholesterol stone, calcium bilirubinate stone and calcium carbonate stone according to the contents of major components in each gallstone. The training set which contained objects of the different known class was constructed using forty NIR spectra and the test set was made with ten different gallstone spectra. The number of important principal components(PCs) to describe the class was determined by cross validation in order to improve the decision criterion of the SIMCA for the training set. The score plots of the class training set whose objects belong to the other classes were inspected. The critical distance of each class was computed using both the Euclidean distance and the Mahalanobis distance at a proper level of significance(${\alpha}$). Two methods were compared with respect to classification and their robustness towards the number of PCs selected to describe different classes.

• Journal of Biomedical Engineering Research
• /
• v.37 no.4
• /
• pp.127-133
• /
• 2016

This study proposes the feasibility for automatic classification of sleep/wakefulness using nasal pressure in patients with sleep-disordered breathing (SDB). First, SDB events were detected using the methods developed in our previous studies. In epochs for normal breathing, we extracted the features for classifying sleep/wakefulness based on time-domain, frequency-domain and non-linear analysis. And then, we conducted the independent two-sample t-test and calculated Mahalanobis distance (MD) between the two categories. As a results, $SD_{LEN}$ (MD = 0.84, p < 0.01), $P_{HF}$ (MD = 0.81, p < 0.01), $SD_{AMP}$ (MD = 0.76, p = 0.031) and $MEAN_{AMP}$ (MD = 0.75, p = 0.027) were selected as optimal feature. We classified sleep/wakefulness based on support vector machine (SVM). The classification results showed mean of sensitivity (Sen.), specificity (Spc.) and accuracy (Acc.) of 60.5%, 89.0% and 84.8% respectively. This method showed the possibilities to automatically classify sleep/wakefulness only using nasal pressure.