• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.110-112
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• 2003

The researchers, who seek geological and environmental information, depend on the remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, the adverse weather conditions and the expensive equipment can restrict that the researcher can collect their data anywhere and any time. To allow for better flexibility, we have developed a compact, a multispectral automatic Aerial photographic system. This system's Multi-spectral camera can catch the visible (RGB) and infrared (NIR) bands (3032${\ast}$2008 pixel) image. Our system consists of a thermal infrared camera and automatic balance control, and it managed and controlled by a palm-top computer. And it includes a camera gimbals system, GPS receiver, weather sensor and etc. As a result, we have successfully tested its ability to acquire aerial photography, weather data, as well as GPS data, making it a very flexible tool for environmental data monitoring.

• Journal of the Korean Society for Nondestructive Testing
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• v.5 no.1
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• pp.3-8
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• 1985

A three dimensional vision is obtained by stereoscopic view using a modified commercial TV set and matching color filter glasses. Two video signals from two CCTV cameras are connected to the RGB (red, green, blue) inputs of picture tube selecting two different colors for two video signals. A synchronizing signal drives a CCTV camera and the color TV set. On the other hand, a delayed synchronizing signal drives the other CCTV camera shifting its image on display. This shift is used in correcting image distortion.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.113-121
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• 2020

Human Pose Estimation (HPE) which localizes the human body joints becomes a high potential for high-level applications in the field of computer vision. The main challenges of HPE in real-time are occlusion, illumination change and diversity of pose appearance. The single RGB image is fed into HPE framework in order to reduce the computation cost by using depth-independent device such as a common camera, webcam, or phone cam. However, HPE based on the single RGB is not able to solve the above challenges due to inherent characteristics of color or texture. On the other hand, depth information which is fed into HPE framework and detects the human body parts in 3D coordinates can be usefully used to solve the above challenges. However, the depth information-based HPE requires the depth-dependent device which has space constraint and is cost consuming. Especially, the result of depth information-based HPE is less reliable due to the requirement of pose initialization and less stabilization of frame tracking. Therefore, this paper proposes a new method of HPE which is robust in estimating self-occlusion. There are many human parts which can be occluded by other body parts. However, this paper focuses only on head self-occlusion. The new method is a combination of the RGB image-based HPE framework and the depth information-based HPE framework. We evaluated the performance of the proposed method by COCO Object Keypoint Similarity library. By taking an advantage of RGB image-based HPE method and depth information-based HPE method, our HPE method based on RGB-D achieved the mAP of 0.903 and mAR of 0.938. It proved that our method outperforms the RGB-based HPE and the depth-based HPE.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1595-1613
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• 2017

The IR camera and laser-based IR projector provide an effective solution for real-time collection of moving targets in RGB-D videos. Different from the traditional RGB videos, the captured depth videos are not affected by the illumination variation. In this paper, we propose a novel feature extraction framework to describe human activities based on the above optical video capturing method, namely spatial-temporal texture features for 3D human activity recognition. Spatial-temporal texture feature with depth information is insensitive to illumination and occlusions, and efficient for fine-motion description. The framework of our proposed algorithm begins with video acquisition based on laser projection, video preprocessing with visual background extraction and obtains spatial-temporal key images. Then, the texture features encoded from key images are used to generate discriminative features for human activity information. The experimental results based on the different databases and practical scenarios demonstrate the effectiveness of our proposed algorithm for the large-scale data sets.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.1123-1127
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• 2005

The Conventional methods of color separation in computer-based machine vision offer only weak performance because of environmental factors such as light source, camera sensitivity, and others. In this paper, we propose an improved color separation method using RGB, HLS, color coordination space, and fuzzy similarity measure. RGB consists of red, green and blue, the three primary colors of light. HLS includes hue, light and saturation, the human recognition elements of co]or. A fuzzy similarity measure was employed for evaluate the similarity among fuzzy colors with the six features of RGB and HLS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.2
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• pp.45-53
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• 2019

RGB-D SLAM (Simultaneous Localization and Mapping) refers to the technology of using deep camera as a visual sensor for SLAM. In view of the disadvantages of high cost and indefinite scale in the construction of maps for laser sensors and traditional single and binocular cameras, a method for creating three-dimensional map of indoor environment with deep environment data combined with RGB-D SLAM scheme is studied. The method uses a mobile robot system equipped with a consumer-grade RGB-D sensor (Kinect) to acquire depth data, and then creates indoor three-dimensional point cloud maps in real time through key technologies such as positioning point generation, closed-loop detection, and map construction. The actual field experiment results show that the average error of the point cloud map created by the algorithm is 0.0045m, which ensures the stability of the construction using deep data and can accurately create real-time three-dimensional maps of indoor unknown environment.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.91-102
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• 2012

As the basis of the environmental ecological river management, this research developed a method of habitat classification using imagery information to understand a distribution characteristics of fish living in a natural river. First, topographic survey and investigation of discharge and water temperature were carried out to analyze hydraulic characteristics of fish habitat, and the unmanned aerial photography was applied to acquire river imagery at the observation time. Riffle, pool, and glide regions were selected as river habitat to analyze fish distribution characteristics. Analysis showed that the standard deviation of RGB on the riffle is higher than pool and glide because of fast stream flow. From the classification accuracy estimation on riffle region according to resolution and kernel size using the characteristics of standard deviation of RGB, the highest classification accuracy was 77.17% for resolution with 30cm and kernel size with 11. As the result of water temperature observation on pool and glide using infrared camera, they were $19.6{\sim}21.3^{\circ}C$ and $15.5{\sim}16.5^{\circ}C$ respectively with the differences of $4{\sim}5^{\circ}C$. Therefore it is possible to classify pool and glide region using the infrared photography information. The habitat classification to figure out fish distribution can be carried out more efficiently, if unmanned aerial photography system with RGB and infrared band is applied.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.767-773
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• 2008

This paper presents a localization system based on the use of electric compass and tracking IR light source. Digital RGB(Red, Green, Blue)signal of digital CMOS Camera is sent to CPLD which converts the color image to binary image at 30 frames per second. CMOS camera has IR filter and UV filter in front of CMOS cell. The filters cut off above 720nm light source. Binary output data of CPLD is sent to DSP that rapidly tracks the IR light source by moving Camera tilt DC motor. At a robot toward north, electric compass signals and IR light source angles which are used for calculating the data of the location system. Because geomagnetic field is linear in local position, this location system is possible. Finally, it is shown that position error is within ${\pm}1.3cm$ in this system.

• The Journal of Korea Robotics Society
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• v.13 no.4
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• pp.237-247
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• 2018

In this paper, we describe a sensor module that monitors fire environment by analyzing fire characteristics. We analyzed the smoke characteristics of indoor fire. Six different environments were defined according to the type of smoke and the flame, and the sensors available for each environment were combined. Based on this analysis, the sensors were selected from the perspective of firefighter. The sensor module consists of an RGB camera, an infrared camera and a radar. It is designed with minimum weight to fit on the robot. the enclosure of sensor is designed to protect against the radiant heat of the fire scene. We propose a single camera mode, thermal stereo mode, data fusion mode, and radar mode that can be used depending on the fire scene. Thermal stereo was effectively refined using an image segmentation algorithm, SLIC (Simple Linear Iterative Clustering). In order to reproduce the fire scene, three fire test environments were built and each sensor was verified.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.6
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• pp.805-814
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• 2013

This paper proposes a robust hand segmentation method using view-invariant characteristic of a wrist-mounted camera, and deals with a hand shape recognition system based on segmented hand information. We actively utilize the advantage of the proposed camera device that provides view-invariant images physically, and segment hand region using a Bayesian rule based on adaptive histograms. We construct HSV histograms from RGB histograms, and update HSV histograms using hand region information from a current image. We also propose a user adaptation method by which hand models gradually approach user-dependent models from user-independent models as the user uses the system. The proposed method was evaluated using 16 Korean manual alphabet, and we obtained increases of 27.91% in recognition success rate.