• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11c
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• pp.569-577
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• 2000

The position of tobacco leaves is affluence to the quality. To evaluate its quality, sample leaves was collected according to the position of attachment. In Korea, the position was divided into four classes such as high, middle, low and inside positioned leaves. Until now, the grade of standard sample was determined by human expert from korea ginseng and tobacco company. Many research were done by the chemical and spectrum analysis using NIR and computer vision. The grade of tobacco leaves mainly classified into 5 grades according to the attached position and its chemical composition. In high and low positioned leaves shows a low level grade under grade 3. Generally, inside and medium positioned leaf has a high level grade. This is the basic research to develop a real time tobacco leaves grading system combined with portable NIR spectrum analysis system. However, this research just deals with position recognition and grading using the color machine vision. The RGB color information was converted to HSI image format and the sample was all investigated using the bundle of tobacco leaves. Quality grade and position recognition was performed through well known general error back propagation neural network. Finally, the relationship about attached leaf position and its grade was analyzed.

• Journal of the Ergonomics Society of Korea
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• v.19 no.1
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• pp.91-100
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• 2000

In this paper the intent recognition system which recognizes the human's head movements as a simple non-verbal intent representation is presented. The system recognizes five basic intent representations. i.e., strong/weak affirmation. strong/weak negation, and ambiguity by image processing of nodding or shaking movements of head. The vision system for tracking the head movements is composed of CCD camera, image processing board and personal computer. The modified template matching method which replaces the reference image with the searched target image in the previous step is used for the robust tracking of the head movements. For the improvement of the processing speed, the searching is performed in the pyramid representation of the original image. By inspecting the variance of the head movement trajectories. we can recognizes the two basic intent representations - affirmation and negation. Also, by focusing the speed of the head movements, we can see the possibility which recognizes the strength of the intent representation.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.80-83
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• 2005

A binocular robot vision system having an autonomously moving active viewpoint is proposed. By using this active viewpoint, the system constructs a correspondence between the images of a feature points on the right and left retinas and calculates the spatial coordinates of the feature points. The system incorporates a function of detecting straight lines in an image. To detect lines the system uses Hough transform. The system searches a region surrounded by 4 straight lines. Then the system recognizes the region as a quadrangle. The system constructs a correspondence between the quadrangles in the right and left images. By the use of the result of the constructed correspondence, the system calculates the spatial coordinates of an object. An experiment shows the effect of the line detection using Hough transform, the recognition of the surface of the object and the calculation of the spatial coordinates of the object.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.496-499
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• 1999

This paper describes a medular neural network(MNN) for a vision system which tracks a given object using a sequence of images from a camera unit. The MNN is used to precisely recognize the given robot endeffector and to minize the processing time. Since the robot endeffector can be viewed in many different shapes in 3-D space, a MNN structure, which contains a set of feedforwared neural networks, co be more attractive in recognizing the given object. Each single neural network learns the endeffector with a cluster of training patterns. The training patterns for a neural network share the similar charateristics so that they can be easily trained. The trained MNN is less sensitive to noise and it shows the better performance in recognizing the endeffector. The recognition rate of MNN is enhanced by 14% over the single neural network. A vision system with the MNN can precisely recognize the endeffector and place it at the center of a display for a remote operator.

• Proceedings of the Korean Society of Computer Information Conference
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• 2008.06a
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• pp.199-204
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• 2008

Visual surveillance is an active topic recently in Computer Vision. Event detection and recognition is one important and useful application of visual surveillance system. In this paper, we propose a new method to recognize the entering and exiting events based on the human's movement feature and the door's state. Without sensors, the proposed approach is based on novel and simple vision method as a combination of edge detection, motion history image and geometrical characteristic of the human shape. The proposed method includes several applications such as access control in visual surveillance and computer vision fields.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.520-522
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• 1998

A development and application of machine vision system is introduced, which automatically recognizes the serial number of nuclear fuel tube. For the recognition, a indirect back light illuminating system is designed and a pattern matching algorithm based on neural network is applied. The various operation and management functions are also developed, on a PC under windows OS, for easy operation and data management, respectively. By the successful application of the vision system the productivity of the nuclear fuel tube recognition process is highly improved.

• Journal of the Korean Society of Industry Convergence
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• v.21 no.6
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• pp.369-378
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• 2018

This study proposes a new approach to real time implimemtation of shape recognition technology of die casting and forging parts based on robot vision for smart factory. The proposed shape recognition and inspection technology for forging and die casting parts is very useful for manufacturing process automatiom and smart factory including external form's automatic inspection of machanical or electronic panrs for the precision verification. The reliabiblity of proposed technology Ihas been illustrated through experiments.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.587-591
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• 2013

Measuring the process of construction operations for productivity improvement remains a difficult task for most construction companies due to the manual effort required in most activity measurement methods. There are many ways to measuring the process. But past measurement methods was inefficient. Because they needed a lot of manpower and time. So, this article focus on the vision-based object recognition and tracking methods for automated construction. These methods have the advantage of efficient that human intervention was reduced. Therefore, this article is analyzed the performance of vision-based methods in the construction sites and is expected to contribute to selection of vision-based methods.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.2
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• pp.60-67
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• 2007

This paper describes the development of on-line computer vision laboratories to teach the detailed image processing and pattern recognition techniques. The computer vision laboratories include distant image acquisition method, basic image processing and pattern recognition methods lens and light, and communication. This study introduces a case study that teaches computer vision in distance learning. environment. It shows a schematic of a distant teaming workstation and contents of laboratories with image processing examples. The study focus more on the contents of the vision Labs rather than internet application method. The study proposes the ways to improve the on-line computer vision laboratories and includes the further research perspectives.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.2
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• pp.121-129
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• 2015

This paper presents a multiple vehicle recognition algorithm based on radar and vision sensor fusion for lane change assistance. To determine whether the lane change is possible, it is necessary to recognize not only a primary vehicle which is located in-lane, but also other adjacent vehicles in the left and/or right lanes. With the given sensor configuration, two challenging problems are considered. One is that the guardrail detected by the front radar might be recognized as a left or right vehicle due to its genetic characteristics. This problem can be solved by a guardrail recognition algorithm based on motion and shape attributes. The other problem is that the recognition of rear vehicles in the left or right lanes might be wrong, especially on curved roads due to the low accuracy of the lateral position measured by rear radars, as well as due to a lack of knowledge of road curvature in the backward direction. In order to solve this problem, it is proposed that the road curvature measured by the front vision sensor is used to derive the road curvature toward the rear direction. Finally, the proposed algorithm for multiple vehicle recognition is validated via field test data on real roads.