• Journal of KIISE:Software and Applications
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• v.33 no.4
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• pp.402-410
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• 2006

This paper presents a method for feature based block motion estimation that uses multi -resolution image sequences to obtain the panoramic images in the continuous ultrasound image sequences. In the conventional block motion estimation method, the centers of motion estimation blocks are set at the predetermined and equally spaced locations. This requires the large blocks to include at least one feature, which inevitably requires long estimation time. In this paper, we propose an adaptive method which locates the center of the motion estimation blocks at the feature points. This make it possible to reduce the block size while keeping the motion estimation accuracy The Harris-Stephen corner detector is used to get the feature points. The comer points tend to group together, which cause the error in the global motion estimation. In order to distribute the feature points as evenly as Possible, the image is firstly divided into regular subregions, and a strongest corner point is selected as a feature in each subregion. The ultrasound Images contain speckle patterns and noise. In order to reduce the noise artifact and reduce the computational time, the proposed method use the multi-resolution image sequences. The first algorithm estimates the motion in the smoothed low resolution image, and the estimated motion is prolongated to the next higher resolution image. By this way the size of search region can be reduced in the higher resolution image. Experiments were performed on three types of ultrasound image sequences. These were shown that the proposed method reduces both the computational time (from 77ms to 44ms) and the displaced frame difference (from 66.02 to 58.08).

• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.361-373
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• 2013

The monitoring of urban area, target detection and building reconstruction have been actively studied and investigated since high resolution X-band SAR images could be acquired by airborne and/or satellite SAR systems. This paper describes an efficient approach to reconstruct artificial structures (e.g. apartment, building and house) in urban area using high resolution X-band SAR images. Building footprint was first extracted from 1:25,000 digital topographic map and then a corner line of building was detected by an automatic detecting algorithm. With SAR amplitude images, an initial building height was calculated by the length of layover estimated using KS-test (Kolmogorov-Smirnov test) from the corner line. The interferometric SAR phases were simulated depending on SAR geometry and changable building heights ranging from -10 m to +10 m of the initial building height. With an interferogram from real SAR data set, the simulation results were compared using the method of the phase consistency. One of results can be finally defined as the reconstructed building height. The developed algorithm was applied to repeat-pass TerraSAR-X spotlight mode data set over an apartment complex in Daejeon city, Korea. The final building heights were validated against reference heights extracted from LiDAR DSM, with an RMSE (Root Mean Square Error) of about 1~2m.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.9
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• pp.35-42
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• 1998

Finding a technique to achieve high machining precision and high productivity is an important issue for CNC machining. One of the solutions to meet better performance of machining is feedrate control. In this paper we present an adaptive feedrate neuro-control method for high precision and high speed machining. The adaptive neuro-control architecture consists of a neural network identifier(NNI) and an iterative learning control algorithm with inversion of the NNI. The NNI is an identifier for the nonlinear characteristics of feedrate and contour error, which is utilized in iterative learning for adaptive feedrate control with specified contour error tolerance. The proposed neuro-control method has been successfully evaluated for machining circular, corner and involute contours by computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.11
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• pp.1068-1073
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• 2010

When a robot navigates in an outdoor environment, a curb or a sidewalk separated from the road can be used as a robust feature. However, most algorithms could detect the curb only in the straight road, and could not detect highly curved corners, ramps, and so on. This paper proposes an algorithm which enables the robot to detect and follow the curbs in various types of roads. In the proposed method, the robot tilts a laser scanner and computes the error between the predicted and the measured distances to the road in front of the robot. Based on this error, the curbs at corners and curves can be classified. It is also difficult to detect a curb near a ramp because of its low height. In this case, the robot also tilts a laser scanner to detect the curb beyond the ramp. Once the robot classifies the road into the curve, corner, ramp, the robot selects the proper navigation strategies depending on the classified road types and is able to continue to detect and follow the curb. The results of a series of experiments show that the robot can stably detect and follows the curb in curves, corners and ramps as well as the straight road.

• Journal of KIISE
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• v.44 no.11
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• pp.1194-1208
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• 2017

A traditional average smoothing method is designed for smoothing out noise, which, however, unintentionally results in smooth corner points on the curvature accompanied with a shrinkage of curves. In this paper, we propose a novel curve smoothing method via polygonal approximation of the input curve. The proposed method determines the smoothing weight for each point of the input curve based on the angle and approximation error between the approximated polygon and the input curve. The weight constrains a displacement of the point after smoothing not to significantly exceed the average noise error of the region. In the experiment, we observed that the resulting smoothed curve is close to the original curve since the point moves toward the average position of the noise after smoothing. As an application to digital cartography, for the same amount of smoothing, the proposed method yields a less area reduction even on small curve segments than the existing smoothing methods.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.237-243
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• 2012

In this paper, an atmospheric effect of repetitive measurements of X-band (9.65 GHz) arc-rail type GB-SAR (ArcSAR) system was quantitatively analyzed. Four artificial triangular trihedral corner reflectors as stationary targets for getting stable back scattered signal during 43 hours continually. The results of the analysis showed that the phase of those stationary targets had changed maximum of 5 radian (12.4 mm) and total RMS error had was 1.62 radian (4 mm) during 65 repeated measuring time. The refractive index of air which was calculated using the temperature;humidity and pressure of atmosphere showed very close relationship with the phase difference. We could check the atmospheric correction was fulfilled by the correction of an atmospheric effect using refractive index during the selected 16 hours period showed that RMS error was dropped from 1.74 radian (4.3 mm) to 0.10 radian (0.24 mm).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.4
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• pp.165-172
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• 2006

In this paper we present a technique for detecting cross-reference points that allows improving watermark detect-ability. In general, Harris detector is commonly used for finding salient points. Harris detector is a kind of combined corner and edge detector which is based on neighboring image data distribution, therefore it has some limitation to find accurate salient points after watermark embedding or any kinds of digital attacks. The new method proposed in this paper used not data distribution but geometrical structure of a normalized image in order to avoid pointing error caused by the distortion of image data. After normalization, we constructed pre-specified number of virtual lines from top to bottom and left to right, and several of cross points were selected by a random key. These selected points specify almost same positions with the accuracy more than that of Harris detector after digital attacks. These points were arranged by a random key, and blocks centered in these points were formed. A reference watermark is formed by a block and embedded in the next block. Because same alteration is applied to the watermark generated and embedded blocks. the detect-ability of watermark is improved even after digital attacks.

• Transactions of the Korean Society of Automotive Engineers
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• v.16 no.2
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• pp.66-73
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• 2008

In this paper, ultrasonic range system which detects parking lot in parking area is studied. The important part for detecting parking lot accurately is to detect the first and second corners of possible parking lot, and for that, new method using multiple echo function is introduced in this paper. Many probabilistic methods have been used to reduce uncertainties of ultrasonic sensor for distance and location of objects. Method using multiple echo, however, gives accurates results as well as simple algorithm. For experiments in parking space, ultrasonic range system was attached to a Pioneer AT-2 and final parking space map was created in a fusion with position information from wheels of a Pioneer AT-2. We will show the results are compared with error of another methods.

• Journal of IKEEE
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• v.22 no.2
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• pp.350-355
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• 2018

In this paper, a system that can classify the objects in the image according to their sizes using the reference points is proposed. The object is studied with samples. The proposed system recognizes and classifies objects by the size in images acquired using a mobile phone camera. Conventional object recognition systems classify objects using only object size. As the size of the object varies depending on the distance, such systems have the disadvantage that an error may occurs if the image is not acquired with a certain distance. In order to overcome the limitation of the conventional object recognition system, the object recognition system proposed in this paper can classify the object regardless of the distance with comparing the size of the reference point by placing it at the upper left corner of the image.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.547-552
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• 2003

In mold machining, there are many concave machining regions where chatter and tool deflection occur since MRR (material removal rate) increases as curvature increases even though cutting speed and depth of cut are constant. Boolean operation between stock and tool model is widely used to compute MRR in NC milling simulation. In finish cutting, the side step is reduced to about 0.3mm and tool path length is sometimes over 300m. so Boolean operation takes long computation time and includes much error if the resolution of stock and tool model is larger than the side step. In this paper, curvature of CL(cutter location) surface and side step of tool path is used to compute the feedrate for constant MRR machining. The data structure of CL surface is Z-map generated from NC tool path. The algorithm to get local curvature from discrete data was developed and applied to compute local curvature of CL surface. The side step of tool path was computed by point density map which includes cutter location point density at each grid element. The feedrate computed from curvature and side step is inserted to new tool path to regulate MRR. The resultants wire applied to feedrate optimization system which generates new tool path with feedrate from NC codes for finish cutting. The system was applied to speaker mold machining. The finishing time was reduced to 12.6%. tool wear was reduced from 2mm to 1.1mm and chatter marks and over cut on corner were removed.