• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.22-30
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• 2011

Plane detection is very important information for mission-critical of robot in 3D environment. A representative method of plane detection is Hough-transformation. Hough-transformation is robust to noise and makes the accurate plane detection possible. But it demands excessive memory and takes too much processing time. Iterative randomized Hough-transformation has been proposed to overcome these shortcomings. This method doesn't vote all data. It votes only one value of the randomly selected data into the Hough parameter space. This value calculated the value of the parameter of the shape that we want to extract. In Hough parameters space, it is possible to detect accurate plane through detection of repetitive maximum value. A common problem in these methods is that it requires too much computational cost and large number of memory space to find the distribution of mixed multiple planes in parameter space. In this paper, we detect multiple planes only via data sampling using Self Organizing Map method. It does not use conventional methods that include transforming to Hough parameter space, voting and repetitive plane extraction. And it improves the reliability of plane detection through division area searching and planarity evaluation. The proposed method is more accurate and faster than the conventional methods which is demonstrated the experiments in various conditions.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.575-581
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• 2007

Though the Hough transform is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the Hough transform, the one-to-many mapping from an image space to a Hough space, causes the innate problem, the sensitivity to noise. To remedy this problem, Edge Strength Hough Transform (ESHT) was proposed and proved to reduce the noise sensitivity. However the performance of ESHT depends on the size of a Hough space and image and some other parameters which should be decided experimentally. In this paper, we derived formulae to decide 2 parameter values; decreasing parameter and broadening parameter, which play an important role in ESHT. Using the derived formulae, 2 parameter values can be decided only with the pre-determined values, the size of a Hough space and an image, which make it possible to decide them automatically. The experiments with different parameter values also support the result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.728-731
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• 2007

Though the Hough transform is a well-known method for detecting analytical shape represented by a number of free parameters, the basic property of the Hough transform, the one-to-many mapping from an image space to a Hough space, causes the innate problem, the sensitivity to noise. To remedy this problem, Edge Strength Hough Transform (ESHT) was proposed and proved to reduce the noise sensitivity. However the performance of ESHT depends on the size of a Hough space and image and some other parameters, which play an important role in ESHT and should be decided experimentally. In this paper, we derived a formula to decide decreasing parameter. Using the derived formulae, the decreasing parameter value can be decided only with the pre-determined values, the size of a Hough space and an image, which make it possible to decide them automatically.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2055-2061
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• 2006

The detection of geometric primitives from a digital image is one of the basic tasks in computer vision area and the Hough transform is a well-known method for detecting analytical shape represented by a number of free parameters. However the basic property of the Hough transform, the one-to-many mapping from an image space to a Hough space, causes the innate problem, the sensitivity to noise. In this paper, we proposed Edge Strength Hough Transform which uses edge strength to reduce the sensitivity to noise and proved the insensitivity using the ratio of peaks in a Mough space. We also experimented the proposed method on lines and got small number of peaks in a Hough space compared to traditional Hough transform, which supports the noise insensitivity of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.4
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• pp.455-461
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• 2002

This paper presents a new parametric log line equation of polar form for Hough transform in log-polar plane, in which it can remove the well-known unboundedness problem of Hough parameters. Bolduc's method is used to generate a log-polar image dividing the fovea and periphery from a Cartesian image. Edges of the fovea and periphery are detected by using the Sobel mask and the proposed space-variant gradient mask, and are combined in the log-polar plane. The sampled points that might constitute a log line are quite sparse in a deep peripheral region due to severe under-sampling, which is an inherent property of LPM. To cope with such under-sampling, we determine the values of cumulative cells in Hough space by using the space-variant weighting. In our experiments, the proposed method demonstrates its validity of detecting not only the lines passing through both the fovea and periphery but also the lines in a deep periphery.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.11
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• pp.92-102
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• 1990

A new method for determining the motion parameters (scale, rotation, translation) of 2-D image is introduced. It employs Hough transform that maps the straight lines in the input image to the points in the Hough space (HS). This method makes use of the relations between the motion of an object in input image and the translations of peak points in the HS and thus derives relating equations about motion parameters especially when scale changes are involved. The derived equations make is efficient and simple to estimate motion parameters of input image, even if the scale parameter of input image is varied. Performance of this approach on an aircraft image is provided in detail in the presence of noise.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2234-2236
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• 1998

In this paper, we propose a new algorithm based on multi-resolution application of the parameter space to the Hough transform technique. The existing Hough transform technique employs mapping of fixed parameter space in order to extract straight lines from image. One of the difficulties of the existing Hough transform technique lies in the detection of multiple adjacent lines for only one line. Increasing the parameter space from the low level resolution to the high level resolution, our algorithm detects straight line in a stable and efficient fashion. Experimental results are included to verity the performance of proposed algorithm.

• Proceedings of the Optical Society of Korea Conference
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• 1990.02a
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• pp.121-125
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• 1990

Optical Hough transform technique is introduced to obtain the straight line features in parallel from the input scene images. Experimental results are also provided to demonstrate the advantage of such optical parallel processor over the digital one. Peaks in optical Hough space are free from quantization noise and thus easy to detect.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2086-2094
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• 2008

Finding a planar surface on 3D space is very important for efficient and safe operation of a mobile robot. In this paper, we propose a method using a plane detection cell (PDC) and iterative randomized Hough transform (IRHT) for finding the planar region from a 3D range image. First, the local planar region is detected by a PDC from the target area of the range image. Each plane is then segmented by analyzing the accumulated peaks from voting the local direction and position information of the local PDC in Hough space to reduce effect of noises and outliers and improve the efficiency of the HT. When segmenting each plane region, the IRHT repeatedly decreases the size of the planar region used for voting in the Hough parameter space in order to reduce the effect of noise and solve the local maxima problem in the parameter space. In general, range images have many planes of different normal directions. Hence, we first detected the largest plane region and then the remained region is again processed. Through this procedure, we can segment all planar regions of interest in the range image.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.9
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• pp.1171-1180
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• 1995

In this paper, we present a 3-D object recognition system in which the 3-D Hough transform domain is employed to represent the 3-D objects. In object modeling step, the features for recognition are extracted from the CAD models of objects to be recognized. Since the approach is based on the CAD models, the accuracy and flexibility are greatly improved. In matching stage, the sensed image is compared with the stored model, which is assumed to yield a distortion (location and orientation) in the 3-D Hough transform domain. The high dimensional (6-D) parameter space, which defines the distortion, is decomposed into the low dimensional space for an efficient recognition. At first we decompose the distortion parameter into the rotation parameter and the translation parameter, and the rotation parameter is further decomposed into the viewing direction and the rotational angle. Since we use the 3-D Hough transform domain of the input images directly, the sensitivity to the noise and the high computational complexity could be significantly alleviated. The results show that the proposed 3-D object recognition system provides a satisfactory performance on the real range images.