• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.933-940
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• 2022

In this paper, the algorithm was developed to recognize hand 3D position through 2D image sensor and implemented a system to remotely control the 6 d.o.f. robot arm by using it. The system consists of a camera that acquires hand position in 2D, a computer that controls robot arm that performs movement by hand position recognition. The image sensor recognizes the specific color of the glove putting on operator's hand and outputs the recognized range and position by including the color area of the glove as a shape of rectangle. We recognize the velocity vector of end effector and control the robot arm by the output data of the position and size of the detected rectangle. Through the several experiments using developed 6 axis robot, it was confirmed that the 6 d.o.f. robot arm remote control was successfully performed.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.43 no.5 s.311
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• pp.89-100
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• 2006

The carotid intima-media thickness (IMT) is very important, because the severity of it is an independent predictor of transient cerebral ischemia, stroke, and coronary events such as myocardial infarction. The conventional image processing to measure the IMT has not been satisfactory, because the methods have relied on the manual section drawing and a regional segmentation by differential estimation. We propose a new image processing technology effective to extract features from the carotid artery image whose pixels have the directional vector properties with composed color distribution. The technique we presented here is not by differential variation but by verification of the layer properties of carotid artery image. Iterated vertical and horizontal analysis and segmentation of the IMT image show the vector characteristics. This new technique makes it possible to cluster the layers statistically, and to classify mathematical correlation between regions and resulting in correct measurements of thickness and its variation. The advantages and effectiveness of this approach are applicable to region process and character extraction of such a vector image.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.609-616
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• 2017

In this paper, we propose an algorithm to obtain more stable and high recognition rate by using left and right rotation information of input image in order to obtain a stable recognition rate in face recognition. The proposed algorithm uses the facial image as the input information in the web camera environment to reduce the size of the image and normalize the information about the brightness and color to obtain the improved recognition rate. We apply Principal Component Analysis (PCA) to the detected candidate regions to obtain feature vectors and classify faces. Also, In order to reduce the error rate range of the recognition rate, a set of data with the left and right $45^{\circ}$ rotation information is constructed considering the directionality of the input face image, and each feature vector is obtained with PCA. In order to obtain a stable recognition rate with the obtained feature vector, it is after scattered in the eigenspace and the final face is recognized by comparing euclidean distant distances to each feature. The PCA-based feature vector is low-dimensional data, but there is no problem in expressing the face, and the recognition speed can be fast because of the small amount of calculation. The method proposed in this paper can improve the safety and accuracy of recognition and recognition rate faster than other algorithms, and can be used for real-time recognition system.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.2
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• pp.110-116
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• 2016

Autonomous vehicles must obey the traffic laws in order to drive actual roads. Traffic signs erected at the side of roads explain the road traffic information or regulations. Therefore, traffic sign recognition is necessary for the autonomous vehicles. In this paper, color characteristics are first considered to detect traffic sign candidates. Subsequently, we establish HOG (Histogram of Oriented Gradients) features from the detected candidate and recognize the traffic sign through a SVM (Support Vector Machine). However, owing to various circumstances, such as changes in weather and lighting, it is difficult to recognize the traffic signs robustly using only SVM. In order to solve this problem, we propose a tracking algorithm with RANSAC-based motion estimation. Using two-point motion estimation, inlier feature points within the traffic sign are selected and then the optimal motion is calculated with the inliers through a bundle adjustment. This approach greatly enhances the traffic sign recognition performance.

• Journal of KIISE:Software and Applications
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• v.30 no.5_6
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• pp.430-443
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• 2003

The objective of this paper is to evaluate most useful feature vector space using the angle, velocity and location features from gesture trajectory which extracted hand regions from consecutive input images and track them by connecting their positions. For this purpose, the gesture tracking algorithm using color and motion information is developed. The recognition module is a HMM model to adaptive time various data. The proposed algorithm was applied to a database containing 4,800 alphabetical handwriting gestures of 20 persons who was asked to draw his/her handwriting gestures five times for each of the 48 characters.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.8
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• pp.710-718
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• 2000

In this paper we described an approach to automation of visual inspection of solder joint defects of SMC(Surface Mounted Components) on PCBs(Printed Circuit Board) by using neural network and fuzzy rule-based classification method. Inherently the surface of the solder joints is curved tiny and specular reflective it induces difficulty of taking good image of the solder joints. And the shape of the solder joints tends to greatly vary with the soldering condition and the shapes are not identical to each other even though the solder joints belong to a set of the same soldering quality. This problem makes it difficult to classify the solder joints according to their qualities. Neural network and fuzzy rule-based classification method is proposed to effi-ciently make human-like classification criteria of the solder joint shapes. The performance of the proposed approach is tested on numerous samples of commercial computer PCB boards and compared with the results of the human inspector performance and the conventional Kohonen network.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.562-566
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• 2009

In this paper, we present a novel system to categorize web image search results using emotional concepts and to browse the results more conveniently and easily. The proposed system can categorize search results into 8 emotional categories based on emotion vector, which obtained by color and pattern features. Here, we use Kobayashi’s emotional categories: {romantic, natural, casual, elegant, chic, classic, dandy and modern}. With search results for a given query, the proposed system can provide categorized images for each emotional category. With 1,000 Yahoo! search images, we compared the proposed method with Yahoo! image search engine in respect of satisfaction, efficiency, convenience and relevance with a user study. Our experimental results show the effectiveness of the proposed method.

• Journal of Korea Multimedia Society
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• v.19 no.6
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• pp.1024-1033
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• 2016

In this study, a system is proposed for analyzing whether driver's eyes are open or closed on the basis of images to determine driver's drowsiness. The proposed system converts eye areas detected by a camera to a color space area to effectively detect eyes in a dark situation, for example, tunnels, and a bright situation due to a backlight. In addition, the system used a thickness distribution of a detected eye area as a feature value to analyze whether eyes are open or closed through the Support Vector Machine(SVM), representing 90.09% of accuracy. In the experiment for the images of driver wearing glasses, 83.83% of accuracy was obtained. In addition, in a comparative experiment with the existing PCA method by using Eigen-eye and Pupil Measuring System the detection rate is shown improved. After the experiment, driver's drowsiness was identified accurately by using the method of summing up the state of driver's eyes open and closes over time and the method of detecting driver's eyes that continue to be closed to examine drowsy driving.

• Journal of Korea Multimedia Society
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• v.13 no.2
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• pp.196-204
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• 2010

In this paper, we develope a defect inspection method for each buttons of keypad of cellular phones before they are assembled. The proposed algorithm consists of the similar color checking and its classification, font error detection, and scratch detection based on the segmentation of keypad area and font, translation and rotation processing sequentially. Especially, the proposed segmentation method approximate the pad region as B-spline function to deal with illumination change due to the shape of key button with the slant and curved surface followed by simple thresholding. And also, the rotational information is obtained by using eigen value and eigen vector very fast and effectively. The experimental results show that the performance of the proposed algorithm is good when it is applied to in-line process.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.223-229
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• 2018

This paper proposes a robust technique of image segmentation, which can be obtained if the topological persistence of each connected component is used as the feature vector for the graph-based image segmentation. The topological persistence of the components, which are obtained from the super-level set of the image, is computed from the morse function which is associated with the gray-level or color value of each pixel of the image. The procedure for the components to be born and be merged with the other components is presented in terms of zero-dimensional homology group. Extensive experiments are conducted with a variety of images to show the more correct image segmentation can be obtained by merging the components of small persistence into the adjacent components of large persistence.