• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.721-728
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• 2005

This paper suggests effective object detection system for moving objects with specified color and motion information. The proposed detection system includes the object extraction and definition process which uses MCN(Multiple Color Normalization) and MCWUPC(Moving Color Weighted Unmatched Pixel Count) computation to decide the existence of moving object and object segmentation technique using signature information is used to exactly extract the objects with high probability. Finally, real time detection system is implemented to verify the effectiveness of the technique and experiments show that the success rate of object tracking is more than $89\%$ of total 120 image frames.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.488-494
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• 2001

Many literatures, so far, have concentrated on approaches employing dependent coordinates set resulting in computational burden of constraint forces, which is needless in many cases. Some researchers developed methods to remove or calculate it efficiently. But systematic generation of the motion equation using independent coordinates set by Kane's equation is possible for any closed loop system. Independent velocity transformation method builds the smallest size of motion equation, but needs practically more complicated code implementation. In this study, dependent velocity matrix is systematically transformed into independent one using dependent-independent transformation matrix of each body group, and then motion equation free of constraint force is constructed. This method is compared with the other approach by counting the number of multiplications for car model with 15 d.o.f..

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.8
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• pp.1356-1362
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• 2008

In this paper. we propose a novel auto white balance algorithm which is one of the representative functions on cameras. White balance is the process of removing unrealistic color casts, which will make the captured white objects appear white. For white balance, we employ $CIEL^*a^*b^*$ color space which is the most complete color model available and is conventionally used to describe all the colors visible to the human eye and estimate the color difference on white objects with distribution of the image which is called the reference white estimation. For accuracy, we form groups or sets of pixels that are altered by the light sources and other elements. Moreover, Standard group is decided by judgment of specific-case images with the information of groups. Then, the reference white estimation is performed by the color sampling which is to choose all the accumulated pixels contained within the standard group. The color gain for image compensation by considering the color saturation is also computed. the proposed algorithm provides a significant performance.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2023.04a
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• pp.35-35
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• 2023

안정적인 유묘의 확보는 스마트작물생산을 위한 공정육묘 생산에서도 중요하며, 기내배양시 유전적 안정성이 높은 유묘의 대량증식은 유묘생산과 공정육묘생산에서 중요한 과정이다. 기내배양시 배양과 정에서 존재하는 체세포영양계변이(somaclonal variation)라는 장벽을 제거하는 것이 중요하다. 본 연구에서는 화본과 식물인 홍띠(Imperata cylindrica ‘Rubra’)로부터 기관분화 단계별 재분화체를 작성하여 기관분화 시 기내재생체의 유전적 안정성을 조사하였다. ISSR 마커에 기반하여 유전적 변이성을 조사하고자 7종류 총 21개체의 기관분화 단계별 재분화체 및 재분화식물체에 대하여 분석한 결과, 유전적 다형성은 기관분화 단계별 재분화체 및 순화 재분화체에서 대조구인 모식물체(1.4％) 대비 같거나 높게 나타나서 재분화체에서 유전적 안정성이 다소 낮은 것으로 나타났다. 또한, Jaccard 계수(Jaccard coefficient)로 총 21개체들 간의 유전적 유사도 지수를 평가한 결과, 유전적 유사도 지수는 0.747~1.0 사이에 분포하며, 평균 0.868로 나타났다. ISSR 마커 밴드에 기반하여 평균연결법(Average linkage method)으로 군집 분석한 결과, 모든 개체는 유사도 지수 0.809 ~ 1.000 내에 분포하였다. 유전적 유사도 지수 0.809에서 2개 그룹으로 유집되었으며, 모식물체와 실내재배, 노지재배 재분화 녹색 식물체가 같은 그룹으로 분류되었다. 이상의 결과는 화본과 식물의 기내배양에서 기관분화 시 존재하는 체세포영양계변이에 대한 기초 정보를 제공해 준다. 이들 기관분화에 따른 기내재생체의 안정성에 대한 연구자료는 향후 기내식물의 안정적인 대량번식에 있어 유익한 배경을 제공해 줄 것이다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.5
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• pp.9-17
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• 2019

As the IoT environment becomes more popular, the safety of the M2M environment, which establishes the communication environment between objects and objects without human intervention, becomes important. Due to the nature of the wireless communication environment, there is a possibility of exposure to security threats in various aspects such as data exposure, falsification, tampering, deletion and privacy, and secure communication security technology is considered as an important requirement. In this paper, we propose a new method for group key generation and exchange using trap hash collision hash in existing 'M2M communication environment' using hash collision, And a mechanism for confirming the authentication of the device and the gateway after the group key is generated. The proposed method has attack resistance such as spoofing attack, meson attack, and retransmission attack in the group communication section by using the specificity of the collision message and collision hash, and is a technique for proving safety against vulnerability of hash collision.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.1
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• pp.49-57
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• 2005

The proposed algorithm is to achieve the robust optical flow detection which is applicable for the case that the outlier rate is over 80%. If the outlier rate of optical flows is over 30%, the discrimination between the inliers and outlier with the conventional algorithm is very difficult. The proposed algorithm is to overcome such difficulty with three steps of grouping algorithm; 1) constructing the 2D histogram with two axies of the lengths and the directions of optical flows. 2) sorting the number of optical flows in each bin of the two-dimensional histogram in the descending order and removing some bins with lower number of optical flows than threshold. 3) increasing the resolution of the two-dimensional histogram if the number of optical flows in a specific bin is over 20% and decreasing the resolution if the number of optical flows is less than 10%. Such processing is repeated until the number of optical flows falls into the range of 10%-20% in all the bins. The proposed algorithm works well on the different kinds of images with many of wrong optical flows. Experimental results are included.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.27-32
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• 2013

It plays an important role to detect the shape of an ellipse in many application areas of image processing. But it is very difficult to detect the ellipse in the real image because the noise was involved in the image, other objects obscured the ellipse or the ellipses were overlap with each other. In this paper, we extract the boundary (edge) to detect ellipse in the image and perform the grouping process in order to reduce amount of information. As a result, the speed of the ellipse detection was improved. Also in order to the ellipse detection, we selected the five ellipse parameters at random And then to select the optimal parameters of the ellipse, the linear least-squares approximation is applied. To verify the ellipse detection, RANSAC algorithm is applied. After the algorithm proposed in this study was implemented, the results applied to the real images showed an aocuracy of 75% and speed was very fast to compared with other researches. It mean that the proposed algorithm was valuable to detect the ellipses in the image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.1-6
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• 2005

In the Markov network which models disparity map with the Markov Random Fields(MRF), the belief propagation algorithm is operated by message passing between nodes corresponding to each pixel. The initial message value is converged by iterations of the algorithm and the algorithm requires many iterations to get converged messages. In this paper, we simplify the algorithm by regarding the objects in the disparity map as combinations of lines with same message valued nodes to reduce iterations of the algorithm.

• Journal of Broadcast Engineering
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• v.2 no.1
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• pp.74-83
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• 1997

We have proposed a composite feature measure which combines the color and shape features of an image for image retrieval. We improved the performance of retrieval based on the efficient color quantization using the Lloyd-Max quanizer and on the Histogram matrix matching method which considers the spatial correlation of quantized color group. We also supplemented the color information using shape information with the Improved Moment Invarlants. We have tested our technique on Image database consisting of 200 actual trademark images. Our experimental results showed that our approach improved the performance compared to the previous method under the various situations such as rotation images, translation images, noise added images, gamma corrected images and so on. The efficiency of retrieval is found to be very high and experimental results are

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.157-165
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• 2020

Aiming at the development of neural network and self-driving data set, it is also an idea to improve the performance of network model to detect moving objects by dividing the data set. In Darknet network framework, the YOLOv4 (You Only Look Once v4) network model was used to train and test Udacity data set. According to 7 proportions of the Udacity data set, it was divided into three subsets including training set, validation set and test set. K-means++ algorithm was used to conduct dimensional clustering of object boxes in 7 groups. By adjusting the super parameters of YOLOv4 network for training, Optimal model parameters for 7 groups were obtained respectively. These model parameters were used to detect and compare 7 test sets respectively. The experimental results showed that YOLOv4 can effectively detect the large, medium and small moving objects represented by Truck, Car and Pedestrian in the Udacity data set. When the ratio of training set, validation set and test set is 7:1.5:1.5, the optimal model parameters of the YOLOv4 have highest detection performance. The values show mAP50 reaching 80.89%, mAP75 reaching 47.08%, and the detection speed reaching 10.56 FPS.