• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.40-47
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• 2010

This paper presents a simultaneous localization and mapping (SLAM) of an indoor hallway environment using Rao-Blackwellized particle filter (RBPF) along with a line segment as a landmark. Based on the fact that fluent line features can be extracted around the ceiling and side walls of hallway using vision sensor, a horizontal line segment is extracted from an edge image using Hough transform and is also tracked continuously by an optical flow method. A successive observation of a line segment gives initial state of the line in 3D space. For data association, registered feature and observed feature are matched in image space through a degree of overlap, an orientation of line, and a distance between two lines. Experiments show that a compact environmental map can be constructed with small number of horizontal line features in real-time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.822-828
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• 2002

Optical flow estimation based on multi constraint approaches is frequently used for recognition of moving objects. However, the use have been confined because of OF estimation time as well as error problem. This paper shows a new method form effectively extracting movement information using the multi-constraint base approaches with sobel edge detection. The moving objects anr extraced in the input image sequence using edge detection and segmentation. Edge detection and difference of the two input image sequence gives us the moving objects in the images. The process of thresholding removes the moving objects detected due to noise. After thresholding the real moving objects, we applied the Combinatorial Hough Transform (CHT) and voting accumulation to find the optimal constraint lines for optical flow estimation. The moving objects found in the two consecutive images by using edge detection and segmentation greatly reduces the time for comutation of CHT. The voting based CHT avoids the errors associated with least squares methods. Calculation of a large number of points along the constraint line is also avoided by using the transformed slope-intercept parameter domain. The simulation results show that the proposed method is very effective for extracting optical flow vectors and hence recognizing moving objects in the images.

• The Journal of Natural Sciences
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• v.5 no.1
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• pp.87-93
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• 1992

A robotically assisted field material handling system designed for loading and unloading of a planar pallet with a forklift in unstructured field environment is presented. The system uses combined acoustic/visual sensing data to define the position/orientation of the pallet and to determine the specific locations of the two slots of the pallet, so that the forklift can move close to the slot and engage it for transport. In order to reduce the complexity of the material handling operation, we have developed a method based on the integration of 2-D range data of Poraloid ultrasonic sensor along with 2-D visual data of an optical camera. Data obtained from the two separate sources complements each other and is used in an efficient algorithm to control this robotically assisted field material handling system . Range data obtained from two linear scannings is used to determine the pan and tilt angles of a pallet using least mean square method. Then 2-D visual data is used to determine the swing angle and engagement location of a pallet by using edge detection and Hough transform techniques. The limitations of the pan and tilt orientation to be determined arc discussed. The system developed is evaluated through the hardware and software implementation. The experimental results are presented.

• Journal of Periodontal and Implant Science
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• v.47 no.1
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• pp.41-50
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• 2017

Purpose: The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging. Methods: We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically. Results: The detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than $100{\mu}m$. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform. Conclusions: We were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

• Journal of the Korea Computer Graphics Society
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• v.23 no.4
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• pp.31-40
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• 2017

As 3D cell culture has recently become possible, it has been able to observe a 3D shape of cell and volume. Generally, 3D information of a cell should be observed with a special microscope such as a confocal microscope or an electron microscope. However, a confocal microscope is more expensive than a conventional microscope and takes longer time to capture images. Therefore, there is a need for a method that can reconstruct the 3D shape of cells using a common microscope. In this paper, we propose a method of reconstructing 3D cells using the focus estimator value from multi-focal fluorescence images of cells. Initially, 3D cultured cells are captured with an optical microscope by changing the focus. Then the approximate position of the cells is assigned as ROI (Region Of Interest) using the circular Hough transform in the images. The MSBF (Modified Sliding Band Filter) is applied to the obtained ROI to extract the outlines of the cell clusters, and the focus estimator values are computed based on the extracted outlines. Using the computed focus estimator values and the numerical aperture (NA) of the microscope, we extract the outline of the cell cluster considering the depth and reconstruct the cells into 3D based on the extracted outline. The reconstruction results are examined by comparing with the combined in-focus portions of the cell images.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.132-139
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• 2015

Visual SLAM(Simultaneous Localization And Mapping) has been used widely to estimate a mobile robot's location. Visual SLAM estimates relative motions with static visual features over image sequence. Because visual SLAM methods assume generally static features in the environment, we cannot obtain precise results in dynamic situation including many moving objects: cars and human beings. This paper presents a stereo vision based SLAM method in dynamic environment. First, we extract disparity map with stereo vision and compute optical flow. We then compute disparity change that is the estimated flow field between stereo views. After examining the disparity change value, we detect ROIs(Region Of Interest) in disparity space to determine dynamic scene objects. In indoor environment, many structural planes like walls may be determined as false dynamic elements. To solve this problem, we segment the scene into planar structure. More specifically, disparity values by the stereo vision are projected to X-Z plane and we employ Hough transform to determine planes. In final step, we remove ROIs nearby the walls and discriminate static scene elements in indoor environment. The experimental results show that the proposed method can obtain stable performance in dynamic environment.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.111-125
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• 2015

In this study, the target detection using both high-resolution satellite SAR and Elecro-Optical (EO) images such as TerraSAR-X and WorldView-2 is performed, considering the characteristics of targets. The targets of our interest are featured by being stationary and appearing as cluster targets. After the target detection of SAR image by using Constant False Alarm Rate (CFAR) algorithm, a series of processes is performed in order to reduce false alarms, including pixel clustering, network clustering and coherence analysis. We extend further our algorithm by adopting the fast and effective ellipse detection in EO image using randomized hough transform, which is significantly reducing the number of false alarms. The performance of proposed algorithm has been tested and analyzed on TerraSAR-X SAR and WordView-2 EO images. As a result, the average false alarm for group of targets is 1.8 groups/$64km^2$ and the false alarms of single target range from 0.03 to 0.3 targets/$km^2$. The results show that groups of targets are successfully identified with very low false alarms.

• Journal of Practical Engineering Education
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• v.16 no.3_spc
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• pp.391-399
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• 2024

All highway toll gates in Korea use low-speed WIM(Weight-In-Motion) to block overloaded cargo vehicles from entering the main highway, but some cargo vehicle owners are illegally modifying vehicles to operate variable axles and evading crackdowns by manipulating the axles. In previous studies detect all tires of a running vehicle were detected to determine whether there is axle manipulation. However, because the vehicle entry area at the highway toll gate checkpoint is very narrow, there is a problem that it is realistically difficult to film all tires of the entering vehicle in one video frame. In this paper, we proposed a system that can determine whether the axle is being operated through changes in the height of the vehicle's cargo box rather than by detecting tires. To detect changes in the height of a cargo box, we propose a method to extract the representative line of the cargo box using Hough transform and then measure the change in height of the representative line to detect the change in height of the cargo box. In addition, we propose a method to detect changes in the vertical height of a cargo box by accumulating motion vectors of pixels within a certain area of the image using optical flow. And the two methods were compared and their advantages and disadvantages were analyzed and presented.