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Occlusion Filling in Dynamic Programming with Simple Index Treatment
JeongMok HA,Hong JEONG 제어로봇시스템학회 2012 제어로봇시스템학회 국제학술대회 논문집 Vol.2012 No.10
One of the problems in stereo matching algorithm is that it is impossible to estimate the most left positioned pixels with left reference system or the most right positioned pixels with right reference system because of the characteristic of stereo vision. That means in stereo matching algorithm, it cannot estimate disparity in occlusion parts. We used very simple index treatment to solve this problem, to compare original DP algorithm exactly, and to make not complex system. We could see that occlusion areas are filled when we used improved DP. If we add more techniques that are used frequently to other stereo matching algorithm, we could get more exact and reliable results.
Polygonal Symmetry Transform for Detecting Rectangular Traffic Signs
Jea Young Jeon,JeongMok Ha,Sung Yong Jo,Gi Yeong Bae,Hong Jeong 제어로봇시스템학회 2014 제어로봇시스템학회 국제학술대회 논문집 Vol.2014 No.10
We present a new symmetry transform finding rectangles based on a gradient and corner information of images. We succeed the Fast Radial Symmetry Transform (FRST) finding radial objects with low cost and high accuracy. But FRST focused on using image gradient to find small circle shapes so that FRST can not detect larger polygonal shapes well. Our transform used image gradient and image corner to construct polygonal symmetry maps and extract regions of interest from images. We referred the set of polygon features as the Polygonal Symmetry Transform (PST). Then PST has large coverage with the advantages of FRST. To verify PST, we designed a polygon detectors which finds the road traffic sign such as radial and polygonal shapes. Our detector extracted regions of interest from real road images and calculated performances including detection rate for traffic signs and processing time per image in our experiments.
Visual SLAM-based Vehicle Control for Autonomous Valet Parking
Younggon Jo,Seokhyeon Hong,Jeongmok Ha,황성수 대한전자공학회 2022 IEIE Transactions on Smart Processing & Computing Vol.11 No.2
This research proposes an efficient vehicle control method using visual SLAM (Simultaneous Localization And Mapping) for AVP (Autonomous Valet Parking). SLAM technology generates a map of the surrounding environment and localizes the vehicle within the map. It is used to identify the layout of the parking lot and track the vehicle by using camera sensors only. In the proposed system, an autonomous driving vehicle is controlled using the coordinates of the keyframe on the visual SLAM map. The vehicle is driven by determining the keyframe in the movable position during the autonomous driving process. This driving procedure is possible because the coordinates of the vehicle and the keyframe can be estimated through the SLAM map. However, the SLAM map, generated using features of the surrounding environment, is likely to change scale while driving due to feature matching errors. Therefore, the system proposes to update the initial scale using the time the vehicle has moved and the changes in vehicle coordinates on the SLAM map. The tracking success rate of autonomous driving and the success rate of autonomous parking were measured to evaluate the performance of the proposed system. The experimental results indicate that autonomous valet parking can be achieved using visual SLAM.