• The Transactions of the Korean Institute of Electrical Engineers P
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• v.55 no.4
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• pp.202-206
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• 2006

In this paper, an auto detection method of stator winding fault of small induction motor is suggested. The Park's vector pattern which is obtained from 3-phase current signal by d-q transforming, is very good to detect winding fault. Comparing the Park's vector pattern of testing motor with its of healthy motor, the Park's vector pattern of fault motor is became an ellipse and the asymmetry is increased by the winding fault series. So for detecting the dis-symmetry, id-filtered function, Min-value, and Max-value are suggested for auto detecting. Using LabVIEW programing, 3-phase healthy motor and several kind of winding fault motors are tested and the test results are shown that the suggested method can gives us a possibility of an auto detecting winding fault.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.473-481
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• 2008

This paper presents an approach to Back-propagation and Radial Basis Function neural network method with various training set for automatic vehicle detection from aerial images. The initial extraction of candidate object is based on Mean-shift algorithm with symmetric property of a vehicle structure. By fusing the density and the symmetry, the method can remove the ambiguous objects and reduce the cost of processing in the next stage. To extract features from the detected object, we describe the object as a log-polar shape histogram using edge strengths of object and represent the orientation and distance from its center. The spatial histogram is used for calculating the momentum of object and compensating the direction of object. BPNN and RBFNN are applied to verify the object as a vehicle using a variety of non-car training sets. The proposed algorithm shows the results which are according to the training data. By comparing the training sets, advantages and disadvantages of them have been discussed.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.331-334
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• 2000

This paper presents the method for the decision of eye states using the eye blink in video sequences. The entire procedure consists of two steps: in the first step, the accurate eye position is found in the input image by using symmetry information of faces and projection, and in the second step, the eye open/close state is decided by the horizontal and vertical projection. The method in this paper is also used for detecting drivers' fatigue in the drowsiness detection system.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.105.1-105
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• 2001

In this paper, a technique is described for classifying normal cases and abnormal cases in automatic spinal deformity detection by computer based on moire topographic images of human backs. Displacement is evaluated statistically between the middle line extracted from the entire moire image and the middle line obtained from a small rectangle area defined on the moire image. The middle line is calculated employing a developed potential symmetry analysis technique. The displacement is calculated in several regions and the mean and the standard deviation of the displacement values are chosen as two features. A linear discriminant function (LDF) is defined on the 2-D feature space based on the Mahalanobis distance and the features are classified into two categories, i.e., normal and ...

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.189-196
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• 2013

This paper presents a two-stage approach to detect pedestrians in video sequences taken from a moving vehicle. The first stage is a preprocessing step, in which potential pedestrians are hypothesized. During the preprocessing step, a difference image is constructed using a global motion estimation, vertical and horizontal edge maps are extracted, and the color difference between the road and pedestrians are determined to create candidate regions where pedestrians may be present. The candidate regions are refined further using the vertical edge symmetry features of the pedestrians' legs. In the next stage, each hypothesis is verified using the integral channel features and an AdaBoost classifier. In this stage, a decision is made as to whether or not each candidate region contains a pedestrian. The proposed algorithm was tested on a range of dataset images and showed good performance.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.568-571
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• 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
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• 2003.10a
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• pp.1990-1993
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• 2003

In this paper, we propose a technique for automatic spinal deformity detection from $moir\acute{e}$ topographic images. Normally the $moir\acute{e}$ stripes show symmetry as a human body is almost symmetric. According to the progress of the deformity of a spine, asymmetry becomes larger. Numerical representation of the degree of asymmetry is therefore useful in evaluating the deformity. First, displacement of local centroids and difference of gray values are evaluated statistically between the left- and the right-hand side regions of the $moir\acute{e}$ images with respect to the extracted middle line. We classify the moire images into two categories i.e., normal and abnormal cases from the features, employing discriminant analysis. An experiment was performed employing 1,200 $moir\acute{e}$ images and 85% of the images were classified correctly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.6
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• pp.640-647
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• 2001

In this paper, a noise-tolerant generalized symmetry transform(NTGST) is proposed as an effective attention operator for the spot detection in BLU inspection, in which various spots with variable sizes, shapes, gray levels, and low contrast, should be detected from the complex, noisy background with lattice shaped shading. The proposed NTGST takes into account the polarity of convergence and divergence of the radial orientation of the intensity gradient as well as it's magnitude and symmetry, and thereby can detect only the BLU spots from the noisy and lattice shaped shadows of background. Experiments are conducted on the BLU inspection image obtained by CCD camera, and the proposed NTGST is Proved to be effectively used in BLU inspection.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.90-93
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• 2008

In natural language, it is common that repetitive constituents in an expression are to be left out and it is necessary to figure out the constituents omitted at analyzing the meaning of the sentence. This paper is on recognition of boundaries of parallel noun phrases by figuring out constituents omitted. Recognition of parallel noun phrases can greatly reduce complexity at the phase of sentence parsing. Moreover, in natural language information retrieval, recognition of noun with modifiers can play an important role in making indexes. We propose an unsupervised probabilistic model that identifies parallel cores as well as boundaries of parallel noun phrases conjoined by a conjunctive particle. It is based on the idea of swapping constituents, utilizing symmetry (two or more identical constituents are repeated) and reversibility (the order of constituents is changeable) in parallel structure. Semantic features of the modifiers around parallel noun phrase, are also used the probabilistic swapping model. The model is language-independent and in this paper presented on parallel noun phrases in Korean language. Experiment shows that our probabilistic model outperforms symmetry-based model and supervised machine learning based approaches.