• Journal of KIISE
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• v.42 no.11
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• pp.1372-1379
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• 2015

This paper presents an analysis of feature extraction methods used for distinguishing the speech of patients with cleft palates and people with normal palates. This research is a basic study on the development of a software system for automatic recognition and restoration of speech disorders, in pursuit of improving the welfare of speech disabled persons. Monosyllable voice data for experiments were collected for three groups: normal speech, cleft palate speech, and simulated clef palate speech. The data consists of 14 basic Korean consonants, 5 complex consonants, and 7 vowels. Feature extractions are performed using three well-known methods: LPC, MFCC, and PLP. The pattern recognition process is executed using the acoustic model GMM. From our experiments, we concluded that the MFCC method is generally the most effective way to identify speech distortions. These results may contribute to the automatic detection and correction of the distorted speech of cleft palate patients, along with the development of an identification tool for levels of speech distortion.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.221-225
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• 2016

This paper is about vision system that exhibits automatic examination of the conditions of fuses and relay boxes using a camera. Proposed vision system is composed of three parts: image acquisition, vision algorithm, and user interface. The image acquisition part is composed of illumination and optics. The vision algorithmis the examining part, using the grabbed fuse box image. Lastly, user interface is divided into two parts, user interface for registering features of fuse box and user interface for examination operation.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.2
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• pp.161-168
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• 2013

Nowadays, one of the most interested area of automobile industry is the production of vehicle which has collision safety and ability to produce less amount of $CO_2$. The achievement of such a dual performance is done by choosing the materials like dual phase steel, ferrite bainite steel, etc. These steels have been used in automotive chassis and body parts, and also used to be formed by hole flanging to meet the goal of strength and design requirement. The formability of sheet material was experimented by hole expansion test and the judgement relies on human eye and his experience. This manual judgement involves many errors and large deviation. This paper develops the automatic crack recognition system which finds a crack based on CCD image to complement the problem of the current method depending on human's sense.

• Journal of Bio-Environment Control
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• v.20 no.4
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• pp.277-282
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• 2011

We carried out shape recognition. We found out cucumber's feature shape by means of neural network and back propagation algorithm. We developed an algorithm which finds object position and shape in real image and we gained following conclusion as a result. It was processed for feature shape extraction of cucumber to detect automatic. The output pattern rates of the miss-detected objects was 0.1~4.2% in the output pattern which was recognized as cucumber. We were gained output pattern according to image resolution $445{\times}363$, $501{\times}391$, $450{\times}271$, $297{\times}421$. It was appeared that no change was detected. When learning pattern was increased to 25, miss-detection ratio was 16.02%, and when learning pattern had 2 pattern, it didn't detect 8 cucumber in 40 images.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5446-5451
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• 2014

Because there are some mistakes by hand in processing electronic maps using a navigation terminal, this paper proposes an automatic offline recognition for traffic signs, which are considered ingredient navigation information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which have been used widely in the field of 2D face recognition as computer vision and pattern recognition applications, was used to recognize traffic signs. First, using PCA, a high-dimensional 2D image data was projected to a low-dimensional feature vector. The LDA maximized the between scatter matrix and minimized the within scatter matrix using the low-dimensional feature vector obtained from PCA. The extracted traffic signs under a real-world road environment were recognized successfully with a 92.3% recognition rate using the 40 feature vectors created by the proposed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.8
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• pp.579-587
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• 1988

Conventional methods of automatic recognition of Korean characters consist of the thinning processing, the segmentation of connected fundamental phonemes and the recognition of each fundamental character. These methods, however require the thinning processing which is complex and time consuming. Also several noise components make worse effects on the recognition of characters than in the case of no thinning. This paper describes the extraction method of the feature components of Korean fundamental characters of the Gothic Korean letter without the thinning. We regard line-components of the contour which describes the character's external boundary as the feature-components. The line-component includes the directional code, the length and the start point in the image. Each fundamental character is represented by the string of directional codes. Therefore the recognition process is only the string pattern matching. We use the Gothic-hangul in the experiment. The ecognition rate is 92%.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.368-374
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• 2010

Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.525-534
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• 2018

Camera calibration is the process of determining the parameters such as the focal length of a camera, the position of a principal point, and lens distortions. For this purpose, images of checkerboard have been mainly used. When targets were automatically recognized in checkerboard image, the existing studies had limitations in that the user should have a good understanding of the input parameters for recognizing the target or that all checkerboard should appear in the image. In this study, a methodology for automatic target recognition was proposed. In this method, even if only a part of the checkerboard image was captured using rectangles including eight blobs, four each at the central portion and the outer portion of the checkerboard, the index of the target can be automatically assigned. In addition, there is no need for input parameters. In this study, three conditions were used to automatically extract the center point of the checkerboard target: the distortion of black and white pattern, the frequency of edge change, and the ratio of black and white pixels. Also, the direction and numbering of the checkerboard targets were made with blobs. Through experiments on two types of checkerboards, it was possible to automatically recognize checkerboard targets within a minute for 36 images.

• Journal of the Korean Society for information Management
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• v.32 no.3
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• pp.377-396
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• 2015

This study proposed a key-frame automatic extraction method for video storyboard surrogates based on users' cognitive responses, EEG signals and discriminant analysis. Using twenty participants, we examined which ERP pattern is suitable for each step, assuming that there are five image recognition and process steps (stimuli attention, stimuli perception, memory retrieval, stimuli/memory comparison, relevance judgement). As a result, we found that each step has a suitable ERP pattern, such as N100, P200, N400, P3b, and P600. Moreover, we also found that the peak amplitude of left parietal lobe (P7) and the latency of FP2 are important variables in distinguishing among relevant, partial, and non-relevant frames. Using these variables, we conducted a discriminant analysis to classify between relevant and non-relevant frames.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.275-278
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• 2007

In this paper, we proposed a automatic extraction method as a preprocessing stage for extraction of basic insect footprint segments. In general, sizes and strides of footprints may be different according to type and size of an insect for recognition. Therefore we proposed an improved algorithm for extraction of basic insect footprint segments regardless of size and stride of footprint pattern. In the proposed algorithm, threshold value for clustering is determined automatically using contour shape of the graph created by accumulating distances between all the spots of footprint pattern. In the experimental results applying the proposed method, The basic footprint segments should be extracted from a whole insect footprint image using significant information in order to find out appropriate features for classification.