• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.79-84
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• 2005

In this paper, we developed an omni-directional surveillance and motion detection method. The fish-eye lens provides a wide field of view image. Using this image, the equi-distance model for the fish-eye lens is applied to get the perspective and panorama images. Generally, we must consider the trade-off between resolution and field of view of an image from a camera. To enhance the resolution of the result images, some kind of interpolation methods are applied. Also the moving edge method is used to detect moving objects for the object tracking.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.6
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• pp.935-941
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• 2017

This paper proposes a method for estimating the distance betweeen vehicles in a moving situation using the image ratio of the distance between the tail lamps of a front vehicle. The actual distance between the tail lamps of a front vehicle was transmitted by LED tail lamps using visible light communication. As the distance between the front vehicle and the rear vehicle changes, it calculates the ratio of the pixel width between the tail lamps of the front vehicle projected on the image. The calculated values are used to derive a distance-mapping function through non-linear regression technique. Then, the distance between vehicles in the moving situation is estimated based on this function.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.10a
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• pp.95-97
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• 2010

Recently, CAN network technology and MOST network are introduced in vehicle to control many electronic devices and to provide entertainment service. Many interconnected devices operate in MOST network which has ring topology such as CD-ROM(DVD), AMP, VIDEO CAMERA, VIDEO DISPLAY, GPS NAVIGATION and so on. In this paper, The input image of CAMERA in the MOST network is used for determining the movement direction of vehicle. Even though the position information was received from GPS, it is difficult to directly determine the direction of moving vehicle in certain areas such as the parallel road structure. This paper designs and implements the method to determine vehicle's direction by real-time matching between CAMERA image and object image base on image DB.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.2
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• pp.177-184
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• 2013

This paper proposes an enhanced Simultaneous Localization and Mapping (SLAM) algorithm based on matching laser image and Extended Kalman Filter (EKF). In general, laser information is one of the most efficient data for localization of mobile robots and is more accurate than encoder data. For localization of a mobile robot, moving distance information of a robot is often obtained by encoders and distance information from the robot to landmarks is estimated by various sensors. Though encoder has high resolution, it is difficult to estimate current position of a robot precisely because of encoder error caused by slip and backlash of wheels. In this paper, the position and angle of the robot are estimated by comparing laser images obtained from laser scanner with high accuracy. In addition, Speeded Up Robust Features (SURF) is used for extracting feature points at previous laser image and current laser image by comparing feature points. As a result, the moving distance and heading angle are obtained based on information of available points. The experimental results using the proposed laser slam algorithm show effectiveness for the SLAM of robot.

• The Journal of the Korea Contents Association
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• v.4 no.3
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• pp.91-97
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• 2004

Difference image scheme is widely used for motion estimation in moving object tracking system. This scheme contains a binarization step which segments image into background and moving object regions, referring to threshold value. In this paper, we propose a decision algorithm of tracking the threshold value with a differential image. The key idea is analyzing the histogram of the differential image. In addition we evaluate the performance of this method in comparison with conventional scheme. As an experimental result with 60 images, it is found that threshold by the proposed algorithm is very close to optimal threshold selected manually.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.142-148
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• 2014

A descriptor which is suitable for motion analysis by using the motion features of moving objects from the real time image sequence is proposed. To segment moving objects from the background, the background learning is performed. We extract motion trajectories of individual objects by using the sequence of the 1st order moment of moving objects. The center points of each object are managed by linked list. The descriptor includes the 1st order coordinates of moving object belong to neighbor of the pre-defined position in grid pattern, The start frame number which a moving object appeared in the scene and the end frame number which it disappeared. A video retrieval by the proposed descriptor combining global and local feature is more effective than conventional methods which adopt a single feature among global and local features.

• Journal of the Korean Society of Radiology
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• v.9 no.7
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• pp.515-521
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• 2015

This study assessed the degradation of image quality caused by grid artifacts and $moir{\acute{e}}$ pattern artifacts in a stationary grid, and the degradation of image quality caused by cut off artifacts in a moving grid. X-ray images were acquired in a stationary grid and a moving grid with X-ray exposure conditions of 100 cm, 80 kVp, and 30 mA using a CDRAD phantom and a 24 cm thickness acrylic phantom. Observer's perception of X-ray imaging using CDRAD Analyzer was mean 49.36, standard deviation 3.76, maximum 55.56, and minimum 38.67 in the stationary grid, and 47.04, 12.69, 55.56, and 20.89, respectively, in the moving grid. The stationary grid was superior to the moving grid in terms of the mean and standard deviation of observer's perception.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.898-902
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• 2012

A descriptor which is suitable for motion analysis by using the motion features of moving objects from the real time image sequence is proposed. To segment moving objects from the background, the background learning is performed. We extract motion trajectories of individual objects by using the sequence of the $1^{st}$ order moment of moving objects. The center points of each object are managed by linked list. The descriptor includes the $1^{st}$ order coordinates of moving object belong to neighbor of the per-defined position in grid pattern, the start frame number which a moving object appeared in the scene and the end frame number which it disappeared. A video retrieval by the proposed descriptor combining global and local feature is more effective than conventional methods which adopt a single feature among global and local features.

• Journal of the Korea Society of Computer and Information
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• v.17 no.4
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• pp.41-49
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• 2012

This paper proposes a detection method of wipes including horizontal wipes with linear moving borders, such as horizontal or vertical wipes, Barn Doors, and Iris Rounds with circular moving borders. The suggested method first obtains a difference image between two adjacent frames, and extracts lines and circles by applying Hough transformation to the extracted difference image. Then, we detect wipe transitions by employing an evaluation function that analyzes the number of moving trajectories of lines or circles, their moving direction and magnitude. To evaluate the performance of the suggested algorithm, experimental results show that the proposed method can effectively detect wipe transitions with linear and circular moving borders rather than some existing methods.