• The Journal of the Korea Contents Association
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• v.6 no.1
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• pp.31-40
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• 2006

In this paper, we propose a gait recognition method based on gait silhouette sequences. Features of gait are affected by the variation of gait direction. Therefore, we synthesize silhouettes to canonical form by using planar homography in order to reduce the effect of the variation of gait direction. The planar homography is estimated with only the information which exist within the gait sequences without complicate operations such as camera calibration. Even though gait silhouettes are generated from an individual person, fragments beyond common characteristics exist because of errors caused by inaccuracy of background subtraction algorithm. In this paper, we use the Principal Component Analysis to analyze the deviated characteristics of each individual person. PCA used in this paper, however, is not same as the traditional strategy used in pattern classification. We use PCA as a criterion to analyze the amount of deviation from common characteristic. Experimental results show that the proposed method is robust to the variation of gait direction and improves separability of test-data groups.

• ETRI Journal
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• v.37 no.6
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• pp.1143-1153
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• 2015

In this paper, we propose an efficient architecture for a real-time image stitching engine for vision SoCs found in motor vehicles. To enlarge the obstacle-detection distance and area for safety, we adopt panoramic images from multiple telegraphic cameras. We propose a stitching method based on a fixed homography that is educed from the initial frame of a video sequence and is used to warp all input images without regeneration. Because the fixed homography is generated only once at the initial state, we can calculate it using SW to reduce HW costs. The proposed warping HW engine is based on a linear transform of the pixel positions of warped images and can reduce the computational complexity by 90% or more as compared to a conventional method. A dual-core SW/HW image stitching engine is applied to stitching input frames in parallel to improve the performance by 70% or more as compared to a single-core engine operation. In addition, a dual-core structure is used to detect a failure in state machines using rock-step logic to satisfy the ISO26262 standard. The dual-core SW/HW image stitching engine is fabricated in SoC with 254,968 gate counts using Global Foundry's 65 nm CMOS process. The single-core engine can make panoramic images from three YCbCr 4:2:0 formatted VGA images at 44 frames per second and frequency of 200 MHz without an LCD display.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.1
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• pp.19-26
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• 2022

This paper presents a pre-processing method to facilitate the container's BIC-code recognition. We propose a network that can find ROI(Region Of Interests) containing a BIC-code region and estimate a homography matrix for warping. Taking the structure of STN(Spatial Transformer Networks), the proposed network consists of next 3 steps, ROI detection, homography matrix estimation, and warping using the homography estimated in the previous step. It contributes to improving the accuracy of BIC-code recognition by estimating ROI and matrix using the proposed network and correcting perspective distortion of ROI using the estimated matrix. For performance evaluation, five evaluators evaluated the output image as a perfect score of 5 and received an average of 4.25 points, and when visually checked, 224 out of 312 photos are accurately and perfectly corrected, containing ROI.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.499-508
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• 2023

In this paper, we propose a technique to estimate the coordinates of feature points existing on a 2D planar object in the three dimensional space. The proposed method detects multiple 3D features from the image, and excludes those which are not located on the plane. The proposed technique estimates the planar homography between the planar object in the 3D space and the camera image plane, and computes back-projection error of each feature point on the planar object. Then any feature points which have large error is considered as off-plane points and are excluded from the feature estimation phase. The proposed method is archived on the basis of the planar homography without any additional sensors or optimization algorithms. In the expretiments, it was confirmed that the speed of the proposed method is more than 40 frames per second. In addition, compared to the RGB-D camera, there was no significant difference in processing speed, and it was verified that the frame rate was unaffected even in the situation that the number of detected feature points continuously increased.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.1
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• pp.123-128
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• 2017

This paper deals with performing missions through autonomous navigation using camera and other sensors. Extracting pose of the car is necessary to navigate safely within the given road. Homography is used to find it. Color image is converted into grey image and thresholding and edge is used to find control points. Two control ponits are converted into world coordinates using homography to find the angle and position of the car. Color is used to find traffic signal. It was confirmed that the given tasks performed well through experiments.

• The Journal of Korea Robotics Society
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• v.4 no.3
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.681-686
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• 2003

In this paper, we propose an algorithm to extract valid planar regions from stereo images for visual navigation of mobile robots. The algorithm is based on the difference image between the stereo images obtained by applying Homography matrix between stereo cameras. Illegal planar regions are filtered out by the use of labeling of the difference images and filtering of invalid blobs using the size of each blob. Also, illegal large planar regions such as walls are removed by adopting a weighted low-pass filtering of the difference image using the past difference images. The algorithms are experimented successfully by the use of stereo camera system built in a mobile robot and a PC-based real-time vision system.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.830-833
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• 2014

This paper proposes dynamic constraint parameter to filter out degenerate configurations (i.e. set of collinear or adjacent features) in RANSAC algorithm. We define five different groups of image based on the feature distribution pattern. We apply the same linear and distance constraints for every image, but we use different constraint parameter for every group, which will affect the filtering result. An evaluation is done by comparing the proposed dynamic CS-RANSAC algorithm with the classic RANSAC and regular CS-RANSAC algorithms in the calculation of a homography matrix. The experimental results show that dynamic CS-RANSAC algorithm provides the lowest error rate compared to the other two algorithms.

• Journal of the Semiconductor & Display Technology
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• v.16 no.4
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• pp.30-35
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• 2017

Moving object segmentation from a nonstationary camera is a difficult problem due to the motion of both camera and the object. In this paper, we propose a new confidence-based background subtraction technique from moving camera. The method is based on clustering of motion vectors and generating adaptive multi-homography from a pair of adjacent video frames. The main innovation concerns the use of confidence images for each foreground and background motion groups. Experimental results revealed that our confidence-based approach robustly detect moving targets in sequences taken by a freely moving camera.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.707-713
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• 2021

Symptoms of foot-and-mouth disease include fever and drooling a lot around the hoof, blisters in the mouth, poor appetite, blisters around the hoof, and blisters around the hoof. Research is underway on smart barns that remotely manage these symptoms through cameras. Visible light cameras can measure the condition of livestock such as blisters, but cannot measure body temperature. On the other hand, infrared thermal imaging cameras can measure body temperature, but it is difficult to measure the condition of livestock. In this paper, we propose an object detection system using deep learning-based livestock detection using visible and infrared thermal imaging composite camera modules for preemptive response