• Journal of Institute of Control, Robotics and Systems
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• v.3 no.2
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• pp.179-186
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• 1997

In this paper we suggest a development of surface defect inspection algorithms for cold mill strip. The defects which exist in a surface of cold mill strip have a scattering or singular distribution. This paper consists of preprocessing, feature extraction and defect classification. By preprocessing, the binarized defect image is achieved. In this procedure, Top-hit transform, adaptive thresholding, thinning and noise rejection are used. Especially, Top-hit transform using local min/max operation diminishes the effect of bad lighting. In feature extraction, geometric, moment and co-occurrence matrix features are calculated. For the defect classification, multilayer neural network is used. The proposed algorithm showed 15% error rate.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.173-188
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• 2013

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

• Journal of Oral Medicine and Pain
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• v.37 no.4
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• pp.227-231
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• 2012

Localized myositis ossificans is a disease with the main feature of formation of heterotropic bone and fibrous tissue involving muscle. It also called traumatic myositis ossificans. Myositis ossificans is likely to occur in the femoral region or brachium but, rarely in the head and neck including the masticatory muscles. It arises from traumatic episodes caused by prolonged mouth opening, surgical procedure, local anesthesia injection. The main clinical feature is the mouth open limitation. The diagnosis of myositis ossificans is usually based on the patient's history, clinical symptoms and on imaging finding. We present the case of patient with localized myositis ossificans of the temporal and lateral pterygoid muscles.

• The Journal of the Acoustical Society of Korea
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• v.14 no.1
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• pp.13-21
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• 1995

In this paper, we improve an image classifier algorithm based on neural network learning. It consists of two steps. The first is input pattern generation and the second, the global neural network implementation using an improved back-propagation algorithm. The feature vector for pattern recognition consists of the codebook data obtained from self-organization feature map learning. It decreases the input neuron number as well as the computational cost. The global neural network algorithm which is used in classifier inserts a control part and an address memory part to the back-propagation algorithm to control weights and unit-offsets. The simulation results show that it does not fall into the local minima and can implement easily the large-scale neural network. And it decreases largely the learning time.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.171-177
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• 2018

Recently there are many image datasets which has variety of data class and point to extract general features. But in order to this variety data class and point, deep learning model trained this dataset has not good performance in heterogeneous data feature local area. In this paper, we propose the structure which use sub-category and openset object detection methods to train more robust model, named multi-branch tree using ASSL. By using this structure, we can have more robust object detection deep learning model in heterogeneous data feature environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.142-149
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• 2016

This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.

• Journal of Dental Rehabilitation and Applied Science
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• v.34 no.3
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• pp.246-250
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• 2018

Ameloblastoma is a benign neoplasm originating from odontogenic epithelium. It is the most common neoplasm in the jaws and is characterized by aggressive behavior and local invasion. Unicystic ameloblastoma (UA) has a unilocular feature in radiologic examination and a cystic feature histologically. Decompression and marsupialization are conservative method of treatment of large UA. The purpose of decompression and marsupialization are size reduction of the mass, which makes it easy to handle at total enucleation with protection of nerve damage and facial deformity. Here we report successful conservative treatment of extensive UA using decompression and marsupialization with a review of literatures.

• Journal of Computational Design and Engineering
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• v.2 no.4
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• pp.253-260
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• 2015

Feature-based NC machining, which requires high quality of 3D CAD model, is widely used in machining aircraft structural part. However, there has been little research on how to automatically detect the CAD model errors. As a result, the user has to manually check the errors with great effort before NC programming. This paper proposes an automatic CAD model errors detection approach for aircraft structural part. First, the base faces are identified based on the reference directions corresponding to machining coordinate systems. Then, the CAD models are partitioned into multiple local regions based on the base faces. Finally, the CAD model error types are evaluated based on the heuristic rules. A prototype system based on CATIA has been developed to verify the effectiveness of the proposed approach.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.95-101
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• 1999

In this paper, we propose an adaptive method of tracking multiple moving objects using contour and features in surrounding conditions. We use an adaptive background model for robust processing in surrounding conditions. Object segmentation model detects pixels thresholded from local difference image between background and current image and extracts connected regions. Data association problem is solved by using feature extraction and object recognition model in searching window. We use Kalman filters for real-time tracking. The results of simulation show that the proposed method is good for tracking multiple moving objects in highway image sequences.

• Journal of Korea Multimedia Society
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• v.4 no.6
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• pp.534-542
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• 2001

Recent content-based image retrieval systems make use of a local single server contained a limited number of images. So these systems are not satisfactory for the Web user's needs that make request for various images on the Web. A content-based image retrieval system that has regard for a great number of Web images has to stand on the basis of real-time first of all. Therefore, to implement the above system we have to resolve a problem of large waste time to take for an image collection and feature extractions. In recent, PC clusters with a load distribution are implemented for the purpose of high-performance data processing. In this paper, we decreased the whole retrieval time by distributing the tasks of image collection and feature extraction to take much time among the slave computers of the PC cluster, and so we found the possibility of the real-time processing in the retrieval of Web images.