• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.204-206
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• 2008

In this paper, we proposed the recognition method that automatically extracts cracks from a surface image acquired by a digital camera and recognizes the directions (horizontal, vertical, -45 degree, and 45 degree) of cracks using the fuzzy single layer perceptron. We compensate an effect of light on a concrete surface image by applying the closing operation, which is one of the morphological techniques, extract the edges of cracks by Sobel masking, and binarize the image by applying the iterated binarization technique. Two times of noise reduction are applied to the binary image for effective noise elimination. After the specific regions of cracks are automatically extracted from the preprocessed image by applying Glassfire labeling algorithm to the extracted crack image, the cracks of the specific region are enlarged or reduced to $30{\times}30$ pixels and then used as input patterns to the fuzzy single layer perceptron. The experiments using concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the fuzzy single layer perceptron was effective in the recognition of the extracted cracks directions.

• Journal of IKEEE
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• v.22 no.4
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• pp.1222-1225
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• 2018

This paper proposes an image detection algorithm to implement gesture. By using a camera sensor, the performance of the extracted image algorithm based on the gesture pattern was verified through experiments. In addition, through the experiments, we confirmed the proposed method's possibility of the implementation. For efficient image detection, we applied a segmentation technique based on image transition which divides into small units. To improve gesture recognition, the proposed method not only has high recognition rate and low false acceptance rate in real gesture environment, but also designed an algorithm that efficiently finds optimal thresholds that can be applied.

• Journal of Sensor Science and Technology
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• v.30 no.2
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• pp.76-81
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• 2021

In this study, we propose a machine vision system with a high object recognition rate. By utilizing a multiple-exposure image sensing technique, the proposed deep learning-based machine vision system can cover a wide light intensity range without further learning processes on the various light intensity range. If the proposed machine vision system fails to recognize object features, the system operates in a multiple-exposure sensing mode and detects the target object that is blocked in the near dark or bright region. Furthermore, short- and long-exposure images from the multiple-exposure sensing mode are synthesized to obtain accurate object feature information. That results in the generation of a wide dynamic range of image information. Even with the object recognition resources for the deep learning process with a light intensity range of only 23 dB, the prototype machine vision system with the multiple-exposure imaging method demonstrated an object recognition performance with a light intensity range of up to 96 dB.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.2
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• pp.139-146
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• 2020

The camouflage pattern was difficult to distinguish from the surrounding background, so it was difficult to classify the object and the background image when the color image is used as the training data of deep-learning. In this paper, we proposed a red-green color blindness image transformation method using the principle that people of red-green blindness distinguish green color better than ordinary people. Experimental results show that the camouflage soldier's recognition performance improved by proposed a deep learning model of the ensemble technique using the imitated red-green-blind image data and the original color image data.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.191-198
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• 2007

In this paper, we present a marker detection and recognition method from camera image for a disabled person to interact with a server system which can control appliance of surrounding environment. It converts the camera image to a binary image by using multi-threshold and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds rectangular markers by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted markers into exact squares by using the warping technique. It extracts feature vectors from marker image by using principal component analysis and then recognizes the marker. The proposed marker recognition system is robust for light change by using multi-threshold. Also, it is robust for angular variation of camera by using warping technique and principal component analysis. Experimental results show that the proposed method achieves 100% recognition rate at maximum for 21 markers and execution speed of 12 frames/sec.

• Korean Institute of Interior Design Journal
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• no.34
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• pp.149-156
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• 2002

This study intends to acquire objective models for basic quantitative analysis of pattern and space through image-recognition technique, and verify the effectiveness of such acquired models. Many experiments showed that the recognized result can be varied depending on the different viewpoints and the analysis based on the single-viewpoint images does not provide objectivity. The experiment using multi-viewpoint image models, which was attempted as an alternative for the disadvantages, showed the recognition similar to that of the actual model. Especially, images generated at laboratory using miniature model may be useful in comparing and understanding plural number of patterns. The models that have been acquired using such images may be hard to use in acquiring images for analyzing actual building patterns or indoor space, although they may be useful in pattern analysis using miniature model. The disadvantage, however, can be supplemented with panorama VR and C. G. simulation technique. Steady researches are required on the application of visual information to the image recognition principle and the model for quantitative analysis of pattern and space in addition to the research on the construction of the model that can be used in comparing and analyzing not only form and space but also miniature models.

• International Journal of Contents
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• v.3 no.2
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• pp.1-5
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• 2007

This paper proposes a technique for principal component analysis (PCA) to raise the recognition rate of a front face in a low dimension by hierarchical image and parallel processing structure. The conventional PCA shows a recognition rate of less than 50% in a low dimension (dimensions 1 to 6) when used for facial recognition. In this paper, a face is formed as images of 3 fixed-size levels: the 1st being a region around the nose, the 2nd level a region including the eyes, nose, and mouth, and the 3rd level image is the whole face. PCA of the 3-level images is treated by parallel processing structure, and finally their similarities are combined for high recognition rate in a low dimension. The proposed method under went experimental feasibility study with ORL face database for evaluation of the face recognition function. The experimental demonstration has been done by PCA and the proposed method according to each level. The proposed method showed high recognition of over 50% from dimensions 1 to 6.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.6
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• pp.379-383
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• 2015

This paper presents an approach to detect object distance with the use of the recently developed low-cost Kinect sensor. The technique is based on Kinect color depth-image processing and can be used to design various computer-vision applications, such as object recognition, video surveillance, and autonomous path finding. The proposed technique uses keypoint feature detection in the Kinect depth image and advantages of depth pixels to directly obtain the feature distance in the depth images. This highly reduces the computational overhead and obtains the pixel distance in the Kinect captured images.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Journal of Technologic Dentistry
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• v.32 no.2
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• pp.91-102
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• 2010

Purpose : The purpose of study was 500 people who are not related to dentistry in order to survey image about dental technicians. Methods : The subjects were composed of 500 people who are not related in Daegu Metropolitan city. This study was done using the Statistical Package for Social Sciences 17.0 for Windows. As for the analysis methods, the study used the frequency analysis, percentage, mean, t-test, analysis of variance. Results : The score on the image of dental technicians declined in the order of occupational image(2.98), work image(3.14), personal image(3.26), social image(2.87). 'It is hard and stressful' in the occupational image had the highest score with 3.69, 'Dentist and companionship are strong' in the occupational image had the lowest score with 2.21. 'It need expert knowledge and a skilled technology' in the work image had the highest score with 3.69, 'Health medical treatment side of health technique is occupation.' in the work image had the lowest score with 3.69. 'It always work busily' in the personal image had the highest score with 3.69, 'It is value and is effect work.' in the personal image had the lowest score with 3.69. 'An employment is easy after license acquisition.' in the social image had the highest score with 3.69, 'It admit independence' in the social image had the lowest score with 3.69. Conclusion : Dental technition research in order to image improvement and recognition, as the medical professional must construct the desirable dental technition image and recognition.