• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.27 no.1
• /
• pp.735-742
• /
• 2009

National Geographic Institute recently has produced a national paper map by means of a computer aided editing system using a national digital map 2.0. However, the map generalization should be made due to the portrayal difference between the digital map and the paper one and the criterion of the map generalization should be determined by the visual image. The tolerance limit of the map generalization has to be decided based on human vision. For this purpose, this study attempts to measure the size of the building on various scale map and then analyze its result. As a consequence, this study shows us that the building size eligible for human vision should be over 0.4mm in the short side of building on the map. The tolerance limit of an isolated building, a reduced building and a densely built-up area for the map generalization is based on the criterion mentioned above.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.4
• /
• pp.39-43
• /
• 2004

A typical theme of active vision systems is gaze-fixing of a camera. Here gaze-fixing of a camera means by steering orientation of a camera so that a given point on the object is always at the center of the image. For this we need to combine a function to analyze image data and a function to control orientation of a camera. This paper presents an algorithm for gaze-fixing of a camera where image analysis and orientation control are designed in a frame. At this time, for avoiding difficulties in implementing and aiming for real-time applications we design the algorithm to be a simple closed-form without using my information related to calibration of the camera or structure estimation.

• Journal of KIBIM
• /
• v.7 no.1
• /
• pp.45-53
• /
• 2017

Continuous research efforts have been made on acquiring location data on construction sites. As a result, GPS and RFID are increasingly employed on the site to track the location of equipment and materials. However, these systems are based on radio frequency technologies which require attaching tags on every target entity. Implementing the systems incurs time and costs for attaching/detaching/managing the tags or sensors. For this reason, efforts are currently being made to track construction entities using only cameras. Vision-based 3D tracking has been presented in a previous research work in which the location of construction manpower, vehicle, and materials were successfully tracked. However, the proposed system is still in its infancy and yet to be implemented on practical applications for two reasons. First, it does not involve entity matching across two views, and thus cannot be used for tracking multiple entities, simultaneously. Second, the use of a checker board in the camera calibration process entails a focus-related problem when the baseline is long and the target entities are located far from the cameras. This paper proposes a vision-based method to track multiple workers simultaneously. An entity matching procedure is added to acquire the matching pairs of the same entities across two views which is necessary for tracking multiple entities. Also, the proposed method simplified the calibration process by avoiding the use of a checkerboard, making it more adequate to the realistic deployment on construction sites.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.644-647
• /
• 2003

This paper describes a neural network based machine vision system designed for inspecting yellow beans in real time. The system consists of a camera. lights, a belt conveyor, air ejectors, and a computer. Beans are conveyed in four lines on a belt and their images are taken by a monochrome line scan camera when they fall down from the belt. Beans are separated easily from their background on images by back-lighting. After analyzing the image, a decision is made by a multilayer artificial neural network (ANN) trained by the error back-propagation (EBP) algorithm. We use the global mean, variance and local change of gray levels of a bean for the input nodes of the network. In an our experiment, the system designed could process about 520kg/hour.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1910-1914
• /
• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.1743-1746
• /
• 2003

C. elegans often used to study of function of gene, but it is difficult for human observation to distinguish the mutants of C. elegans. To solve this problem, the system, which can be classified automatically using the computer vision, is studying now. In the previous works , they described the auto-tracking system and the egg-laying timing modeling, which are used to automated-classily system. In this paper, we use three kinds of features, which are related to movement , size and posture of the worm, and each feature is described mathematically and normalized. In experimental result, we validated the features for the hierarchical clustering, And we used the Calinski and Harabasz's method to find the appropriate cluster number.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.3
• /
• pp.1284-1292
• /
• 2015

As visual surveillance systems become more and more common in human lives, approaches based on these systems to solve security problems in practice are boosted, especially in railway applications. In this paper, we first propose a robust snag detection algorithm and then present a railway security system by using a combination of multiple sensors and the vision based snag detection algorithm. The system aims safety at several repeatedly occurred situations including slope protection, inspection of the falling-object from bridges, and the detection of snags and foreign objects on the rail. Experiments demonstrate that the snag detection is relatively robust and the system could guarantee the security of the railway through these real-time protections and detections.

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.319-320
• /
• 2006

In this paper, we propose a vision based system employing two cameras to provide effective touch screen function. The two main processes - determining touch (or no-touch) and contact location of screen plane - are essential for enabling touch screen function. First region of interest is found by using color characteristic and histogram for determining the contact mode. Second, if the hand touches the mirror, the fingertip point in image is found using the correlation coefficient based on the mirror attribute. Subsequently, the fingertip coordinate in image is transformed to the location in mirror plane by using four predefined points (termed as four-point method) and bilinear transform. Representative experimental results show that the proposed system is suited to touch screen.

• International Journal of Railway
• /
• v.3 no.3
• /
• pp.106-112
• /
• 2010

Accidents at level crossing have large portion on train accidents, and causes economical loss by train delay and operational interruption. Various safety equipments are employed to reduce the accident at level crossing, but existing warning device, and crossing barrier are simple train-oriented protection equipments. In this paper, intelligent railway level crossing system is proposed to prevent and reduce accidents. For train driver's prompt action, image of level crossing and obstacle warning message are continuously provided to train driver through wireless communication in level crossing control zone. Obstacle warning messages, which are extracted by computer vision processing of captured image at level crossing, are recognized by train driver through message color, flickering and warning sound. It helps train driver to decide how to take an action. Meanwhile, for vehicle driver's attention, location and speed of approaching train are given to roadside equipments. We identified the effect of proposed system through test installation at Sea train and Airport level crossing of Yeong-dong line.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.1549-1552
• /
• 1997

In this paper, a optimal algorithm that can produce the FOV is proposed in terms of using the Kohonen's Self-Organizing Map(KSOM). A FOV, that stands for "Field Of View", means maximum area where a camera could be wholly seen and influences the total time of inspection of vision system. Therefore, we draw algorithm with a KSOM which aims to map an input space of N-dimensions into a one-or two-dimensional lattice of output layer neurons in order to optimize the number and location of FOV, instead of former sequentila method. Then, we show demonstratin through computer simulation using the real PCB data. PCB data.