• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.142-152
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• 2019

There is growing interest in ultra-high-resolution content that gives a more realistic sense of presence than existing broadcast content. However, in order to provide ultra-high-resolution contents in existing broadcast services, there are limitations in view angle and resolution of the image acquisition device. In order to solve this problem, many researches on stitching, which is an image synthesis method using a plurality of input devices, have been conducted. In this paper, we propose method of dynamic object based video stitching using minimal error seam in order to overcome the temporal invariance degradation of moving objects in the stitching process of horizontally oriented videos.

• ETRI Journal
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• v.44 no.1
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• pp.62-72
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• 2022

In this study, we propose a multi-GPU-based 8KVR stitching system that operates in real time on both local and cloud machine environments. The proposed system first obtains multiple 4 K video inputs, decodes them, and generates a stitched 8KVR video stream in real time. The generated 8KVR video stream can be downloaded and rendered omnidirectionally in player apps on smartphones, tablets, and head-mounted displays. To speed up processing, we adopt group-of-pictures-based distributed decoding/encoding and buffering with the NV12 format, along with multi-GPU-based parallel processing. Furthermore, we develop several algorithms such as equirectangular projection-based color correction, real-time CG overlay, and object motion-based seam estimation and correction, to improve the stitching quality. From experiments in both local and cloud machine environments, we confirm the feasibility of the proposed 8KVR stitching system with stitching speed of up to 83.7 fps for six-channel and 62.7 fps for eight-channel inputs. In addition, in an 8KVR live streaming test on the 5G MEC/cloud, the proposed system achieves stable performances with 8 K@30 fps in both indoor and outdoor environments, even during motion.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.10
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• pp.957-966
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• 2014

Video Stabilization For Vehicular Applications Is An Important Method Of Removing Unwanted Shaky Motions From Unstable Videos. In This Paper, An Improved Video Stabilization Method With Image Stitching Has Been Proposed. Scale Invariant Feature Transform (Sift) Matching Is Used To Calculate The New Position Of The Points In Next Frame. Image Stitching Is Done In Every Frame To Get Stabilized Frames To Provide Stable Video As Well As A Better Understanding Of The Previous Frame'S Position And Show The Surrounding Objects Together. The Computational Complexity Of Sift (Scale-Invariant Feature Transform) Is Reduced By Reducing The Sift Descriptors Size And Resticting The Number Of Keypints To Be Extracted. Also, A Modified Matching Procedure Is Proposed To Improve The Accuracy Of The Stabilization.

• Journal of information and communication convergence engineering
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• v.9 no.6
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• pp.747-752
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• 2011

The position detection of overlapping area in the interframe for image stitching using auto and cross correlation function (ACCF) and compounding one image with the stitching algorithm is presented in this paper. ACCF is used by autocorrelation to the featured area to extract the filter mask in the reference (previous) image and the comparing (current) image is used by crosscorrelation. The stitching is detected by the position of high correlation, and aligns and stitches the image in shifting the current image based on the moving vector. The ACCF technique results in a few computations and simplicity because the filter mask is given by the featuring block, and the position is enabled to detect a bit movement. Input image captured from CMOS is used to be compared with the performance between the ACCF and the window correlation. The results of ACCF show that there is no seam and distortion at the joint parts in the stitched image, and the detection performance of the moving vector is improved to 12% in comparison with the window correlation method.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.2
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• pp.118-126
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• 2014

In this paper, a new method is proposed for the five-degree-of-freedom precision alignment and stitching of three-dimensional surface-profile data sets. The control parameters for correcting thealignment error are calculated from the surface profile data for overlapped areas among the adjacent measuring areas by using the "least squares method" and "maximum lag position of cross correlation function." To ensure the alignment and stitching reliability, the relationships betweenthe alignment uncertainty, overlapped area, and signal-to-noise level of the measured profile data are investigated. Based on the results of this uncertainty analysis, an appropriate size is proposed for the overlapped area according to the specimen's surface texture and noise level.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.705-711
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• 2000

This paper presents a fast 3D mesh generation method using a surface based method with a stitching algorithm. This method uses the surface based method since the volume based method that uses 3D Delaunay triangulation can hardly deal with a large scale of scanned points. To reduce the processing time, this method also uses a stitching algorithm: after dividing the whole point data into several sections and performing mesh generation on individual sections, the meshes from several sections are stitched into one mesh. Stitching method prevents the surface based method from increasing the processing time exponentially as the number of the points increases. This method works well with different types of scanned points: a scattered type points from a conventional 3D scanner and a cross-sectional type from CT or MRI.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.26-32
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• 2011

This paper proposes a new method to process panoramic image stitching using SURF(Speeded Up Robust Features). Panoramic image stitching is considered a problem of the correspondence matching. In computer vision, it is difficult to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. However, SURF algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform). In this work, we also describe an efficient approach to decreasing computation time through the homography estimation using RANSAC(random sample consensus). RANSAC is a robust estimation procedure that uses a minimal set of randomly sampled correspondences to estimate image transformation parameters. Experimental results show that our method is robust to rotation, zoom, Gaussian noise and illumination change of the input images and computation time is greatly reduced.

• Journal of Multimedia Information System
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• v.6 no.1
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• pp.7-14
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• 2019

This work presents a virtual prototyping design approach for an area-based image stitching hardware. The virtual hardware obtained from virtual prototyping is equivalent to the conceptual algorithm, yet the conceptual blocks are linked to the actual circuit components including the memory, logic gates, and arithmetic units. Through the proposed method, the overall structure, size, and computation speed of the actual hardware can be estimated in the early design stage. As a result, the optimized virtual hardware facilitates the hardware implementation by eliminating trail design and redundant simulation steps to optimize the hardware performance. In order to verify the feasibility of the proposed method, the virtual hardware of an image stitching platform has been realized, where it required 10,522,368 clock cycles to stitch two $1280{\times}1024$ sized images. Furthermore, with a clock frequency of 250MHz, the estimated computation time of the proposed virtual hardware is 0.877sec, which is 10x faster than the software-based image stitch platform using MATLAB.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.507-508
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• 2022

현재 코로나-19로 인해 많은 사람들이 실내외 사진 촬영에 대한 불편함을 호소하며, 2022년 5월 기준 야외 마스크에 대한 의무 착용이 해제되었지만, 여전히 야외에서 마스크를 착용하는 사람들이 대다수 존재한다. 또한 코로나 발생 후 약 2, 3년간 소중한 가족들 및 지인들과의 찍은 중요한 사진들이 마스크를 착용한 채 그대로 남아있다. 본 논문에서는 파노라마 기술인 Image Stitching 기술을 활용하여 마스크 제거 사진 생성 연구에 대해 기술한다. 본 연구를 통해 사용자들의 실내외 마스크 착용 시 촬영에 대한 불편함을 해소하고 이전 마스크 착용 사진들을 복원함으로써 만족감을 높일 수 있을 것으로 기대한다.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.56-60
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• 2024

In this paper, we propose a SIFT feature-based dynamic stitching algorithm for image calibration and correction of a 360-degree surround view system. The existing surround view system requires a lot of processing time and money because in the process of image calibration and correction. The traditional marker patterns are placed around the vehicle and correction is performed manually. Therefore, in this study, images captured with four fisheye cameras mounted on the surround view system were distorted and then matched with the same feature points in adjacent images through SIFT-based feature point extraction to enable image stitching without a fixed marker pattern.