• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.953-964
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• 2020

Ever since media contents took over the world, the importance of typography has increased, and the influence of fonts has be n recognized. Nevertheless, the current Hangul font system is very poor and is provided passively, so it is practically impossible to understand and utilize all the shape characteristics of more than six thousand Hangul fonts. In this paper, the characteristics of Hangul font shapes were selected based on the Hangul structure of similar fonts. The stroke element detection training was performed by fine tuning Faster R-CNN Inception v2, one of the deep learning object detection models. We also propose a system that automatically extracts the stroke element characteristics from characters by introducing an automatic extraction algorithm. In comparison to the previous research which showed poor accuracy while using SVM(Support Vector Machine) and Sliding Window Algorithm, the proposed system in this paper has shown the result of 10 % accuracy to properly detect and extract stroke elements from various fonts. In conclusion, if the stroke element characteristics based on the Hangul structural information extracted through the system are used for similar classification, problems such as copyright will be solved in an era when typography's competitiveness becomes stronger, and an automated process will be provided to users for more convenience.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1537-1545
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• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• Journal of the Optical Society of Korea
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• v.14 no.4
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• pp.388-394
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• 2010

In this paper the object recognition performance of a photon counting integral imaging system is quantitatively compared with that of a conventional gray scale imaging system. For 3D imaging of objects with a small number of photons, the elemental image set of a 3D scene is obtained using the integral imaging set up. We assume that the elemental image detection follows a Poisson distribution. Computational geometrical ray back propagation algorithm and parametric maximum likelihood estimator are applied to the photon counting elemental image set in order to reconstruct the original 3D scene. To evaluate the photon counting object recognition performance, the normalized correlation peaks between the reconstructed 3D scenes are calculated for the varied and fixed total number of photons in the reconstructed sectional image changing the total number of image channels in the integral imaging system. It is quantitatively illustrated that the recognition performance of the photon counting integral imaging system can be similar to that of a conventional gray scale imaging system as the number of image viewing channels in the photon counting integral imaging (PCII) system is increased up to the threshold point. Also, we present experiments to find the threshold point on the total number of image channels in the PCII system which can guarantee a comparable recognition performance with a gray scale imaging system. To the best of our knowledge, this is the first report on comparisons of object recognition performance with 3D photon counting & gray scale images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.514-517
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• 2003

Oxygen supply is one of the most basic things in human body. Breathing is controlled by the lungs' stationary function and the respiratory center which is in the mesulla oblongata. Nothing but, the external breathing that air movement between the lungs and atmosphere and the internal breathing that cellular air movement between the hemoglobin and a single cell. The adult's number of times of the respirations is about 15∼20 per 1 minute, but it depends ages, exercise, temperature, disease, etc. The important thing in detecting the respiration is that doing it in object person's resting time. Detecting the respiration have to be done without attracting any attention of object person. In present using method is detecting the pulse with catching an object person's wrist and observing the object person's movement. In this paper, we propose the mobile respiration detection diagnostic system using ultrasound sensing method that does not be influenced by the inertia error and the pressure error.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.86-97
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• 2011

This paper presents a novel algorithm to detect more accurate road lane with image sensor-based color classification and directional edge clustering. With treatment of road region and lane as a recognizable color object, the classification of color cues is processed by an iterative optimization of statistical parameters to each color object. These clustered color objects are taken into considerations as initial kernel information for color object detection and recognition. In order to improve the limitation of object classification using the color cues, the directional edge cures within the estimated region of interest in the lane boundary (ROI-LB) are clustered and combined. The results of color classification and directional edge clustering are optimally integrated to obtain the best detection of road lane. The characteristic of the proposed system is to obtain robust result to all real road environments because of using non-parametric approach based only on information of color and edge clustering without a particular mathematical road and lane model. The experimental results to the various real road environments and imaging conditions are presented to evaluate the effectiveness of the proposed method.

• Journal of information and communication convergence engineering
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• v.9 no.3
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• pp.347-352
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• 2011

In this paper, although Radar has been used for a long time, integrated scheme with visual camera is an efficient way to enhance marine surveillance system. Camera image is focused by radar information but it is easy to be fallen into inaccurate operation due to environmental noises. We have proposed a method to filter the noises by moving average filter and Kalman filter. It is proved that Kalman filtered results preserves linearity while the former includes larger variance.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.365-368
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• 2001

This work describes a real-time method, based on motion energy detection, for detecting and tracking moving object in the consecutive image sequences. The motion of moving objects is detected by taking the difference of the two consecutive image frames. In addition an edge information of the current image is utilized in order to further increase the accuracy of detection. We can track the moving objects continuously by detecting the motion of objects from the sequence of image frames. A prototype system has been implemented using a TI TMS320C6201 EVM fixed-point DSP board, which can successfully track a moving human in real-time.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.23-24
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• 2023

본 논문에서는 신호등이 설치되지 않은 위험 구역에 대해 신호등을 설치하는 것이 아닌 객체 인식 기반의 반응형 교통 시스템을 설계하여 보행자나 운전자 모두에게 사고의 위험을 줄이는 시스템을 구현한다. 특정 구역에 보행자가 길을 건너기 위해 존재한다면 운전자에게 보행자가 있음을 직관적으로 보여주며, 보행자가 길을 건너고 있으면 운전자에게 보행자가 건너고 있다는 것을 나타내어 기존의 경직적인 신호 체계가 아닌 유동적으로 보행자와 운전자 간의 안전한 환경을 만드는 것을 목표로 구현했다. 데이터는 CGMU dataset과 MIO-TCD dataset에서 사람과 차량의 이미지를 추가로 수집한 이후 학습에 사용하였으며, 객체 인식은 YOLOv5를 기반으로 사용하였으며 이때 성능은 mAP 0.753을 보여주었다.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.2
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• pp.88-94
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• 2012

Many hand gesture recognition systems using advanced computer vision techniques to eliminate the need for a TV remote controller have been proposed. Nevertheless, some issues still remain, such as high computational complexity and insufficient information on the target object and background. Moreover, none of the proposed techniques consider how to enter the control mode of the system. This means that they may need a TV remote controller to enter the control mode. This paper proposes a hand mouse system using a pre-defined gesture with high background adaptability. By doing so, a remote controller to enter the control mode of the IPTV system can be eliminated.