Recently, remote sensing technology as a nondestructive method has been utilized to detectthe quantity and quality of crops using unmanned aerial system. To predict vegetation growth(leaf dry mass and nitrogen content) of soybean, two vegetation index(NDVI and Green NDVI)were calculated from images acquired by multi-spectral camera mounted on a UAV and eachprediction models between vegetation growth and index were evaluated. As a result, there wasno significant difference between vegetation growth and index when each vegetation stage foreach yellow and black bean were compared to each other. However, there was significantdifference between vegetation growth and index when all vegetation stage for each yellow andblack bean were compared to each other. Moreover, there was significant difference betweenvegetation growth and NDVI(r= 0.799 for leaf dry mass, r= 0.796 for nitrogen content), andGreen NDVI(r= 0.860 for leaf dry mass, r= 0.845 for nitrogen content) for all vegetation stageswith all soybeans. The accuracy and precision of Green NDVI model(R2= 0.740 for leaf drymass, R2= 0.714 for nitrogen content) were better than those of NDVI model regardless ofvarieties and vegetation growth. Therefore, Green NDVI has considerable potential to detect thequantity and quality of soybeans.

Underwater CCTV camera systems are increasingly replaced the traditional net approach of assessing the species, numbers and aggregation patterns of marine animals distributing around the artificial reefs installed in the inshore fishing grounds, in particular, in relation to the biological investigation of behavior and distribution patterns of target fishes. In relation to these needs, we developed a multi-directional underwater CCTV camera system to use in detecting and tracking marine animals in the artificial reef ground. The marine targets to be investigated were independently tracked by using a camera module toward the bottom and four camera modules installed in the interval of 90˚ in horizontal plane and inclination of 45˚ in vertical plane of the CCTV system without the overlap of video frames by each camera module. From the results of several field tests at sea, we believe that the developed multi-directional underwater CCTV camera system will contribute to a better understanding in evaluating the effect of artificial reefs installed in the inshore fishing grounds.

일반적으로 바늘구멍(pinhole)카메라는 사물을 육안으로 바라볼 때 물체들의 중첩현상으로 인하여 시야(view) 이미지을 극히 제한적으로 볼 수이다. 본 논문에서는 이러한 문제점을 보완하여 우리가 원하는 3D 시야 이미지에서 중첩현상으로 보지 못하는 부문을 단일 이미지로 생성을 시켜서 관찰할 수 있도록 개발한 그래프 카메라를 소개하려고 한다. 이러한 기술들은 3D 게임개발에 적용할 수 있을 뿐만 아니라 다양한 기술을 적용하여야 하는 그래픽 응용표현기술에 적용할 수 있겠다.

This paper describes a method of road tracking by using a vision and laser with extracting road boundary (road lane and curb) for navigation of intelligent transport robot in structured road environments. Road boundary information plays a major role in developing such intelligent robot. For global navigation, we use a global positioning system achieved by means of a global planner and local navigation accomplished with recognizing road lane and curb which is road boundary on the road and estimating the location of lane and curb from the current robot with EKF(Extended Kalman Filter) algorithm in the road assumed that it has prior information. The complete system has been tested on the electronic vehicles which is equipped with cameras, lasers, GPS. Experimental results are presented to demonstrate the effectiveness of the combined laser and vision system by our approach for detecting the curb of road and lane boundary detection.

The paper proposes a human-following behavior of mobile robot and an intelligent space (ISpace) is used in order to achieve these goals. An ISpace is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to follow the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and following of the walking human with the mobile robot are presented.