• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.1
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• pp.30-38
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• 2014

The purpose of this study is to build and manage environment models with line segments from sonar range data on obstacles in unknown and varied environments. The proposed method therefore employs a two-stage data-transform process in order to extract environmental line segments from range data on obstacles. In the first stage, the occupancy grid extracted from the range data is accumulated to form a two-dimensional local histogram grid. In the second stage, a line histogram extracted from a local histogram grid is based on a Hough transform, and matching serves as a means of comparing each of the segments on a global line segments map against the line segments to detect the degree of similarity in the overlap, orientation, and arrangement. Each of these tests is formulated by comparing one of the parameters in the segment representation. After the tests, new line segments can be found at maximum-density cells in the line histogram, and they are composed onto the global line segment map. The proposed technique is demonstrated in experiments in an indoor environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.45-50
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• 2009

The environment exploration is an essential process for indoor robots such as clean robot and security robot. Apartment house and office building has common frame structure, but internal arrangement of each room may be slightly different. So, it is more convenient to use a common frame map than to build a new map at every time the arrangement is changed. In this case, it is important to recognize invariant features such as wall, door and window. In this paper, an indoor mobile robot is implemented, and by using the laser scanner data and line segment histogram with respect to segment orientation and distance, an environment exploration method is presented and tested. This robot is fitted with a laser scanner, gyro sensor, ultra sonic sensor and IR sensor, and programed with C language.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.783-789
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• 2001

The purpose of this study is to build and to manage environment models with line segments from the sonar range data on obstacles in unknown and varied environments. The proposed method subsequently employs a two-stage data-transform process in order to extract environmental line segments from the range data on obstacles. In the first stage, the occupancy grid extracted from the range data is accumulated to a two-dimensional local histogram grid. In the second stage, a line histogram extracted from an local histogram gird is based on a Hough transform, and matching is a process of comparing each of the segments in the global line segments map against the line segments to detect similarity in overlap, orientation, and arrangement. Each of these tests is made by comparing one of the parameters in the segment representation. After the tests, new line segments are composed to the global line segments map. The proposed technique is illustrated by experiments in an indoor environment.

• Research in Mathematical Education
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• v.6 no.1
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• pp.97-106
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• 2002

Educators have found that the concept of randomness is often misunderstood by students. Chance recently pointed out that students should be introduced to the concept of randomness through the use of simulations. In this article, we studied various aspects of the probability distribution off linear random path in a circle and introduce some related simulations to guide student exploration and discovery. Consider a random line segment that crosses a circle with a certain radius. Perhaps it can be considered to be a path that an airplane shows up and flies into a random direction in a monitor. What is the expected amount of flying distance through the monitor, and the expected variation\ulcorner Are we monitoring what we see scientifically\ulcorner This article studies the probability distribution and some related aspects of a linear random path within a circular monitor. Some simulative activity is also introduced which can be used in a statistics or probability classes.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.401-408
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• 2006

In this paper, we propose a new class of segmentation technique for feature extraction based on the statistical and regional classification at each vertical or horizontal line of digital image data. Data is processed and clustered at each line, different from the point or space process. They are designed to segment gray-scale sectional images using a horizontal and vertical line process due to their statistical and property differences, and to extract the feature. The techniques presented here show efficient results in case of the gray level overlap and not having threshold image. Such images are also not easy to be segmented by the global or local threshold methods. Line pixels inform us the sectionable data, and can be set according to cluster quality due to the differences of histogram and statistical data. The total segmentation on line clusters can be obtained by adaptive extension onto the horizontal axis. Each processed region has its own pixel value, resulting in feature extraction. The advantage and effectiveness of the line-cluster approach are both shown theoretically and demonstrated through the region-segmental carotid artery medical image processing.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.81-88
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• 2007

We implement automobile license plates recognition system. These days automobile license plate recognition systems are widely used for tracing stolen cars. managing parking facilities, ticketing speeding cars, and so on. Recognition systems largely consist of three parts plates extraction, segments extraction, and segment recognition. For plates extraction, we measure the degree of inclination of plate. We use filters that extract only the horizontal components of the front of an automobile to measure the degree of inclination. For segment extraction, we trace the change of the number of blocks that consist solely of foreground pixels or background pixels as the horizontal scanning line moves along upward. For recognition of each individual letter or digit, we devise a variant of template matching method, called comparative template matching. Through experiments, we show that comparative template matching is less prone misled by noises and exhibits higher performance compared to the traditional method of template matching or histogram based recognition.