http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
송대영(Dae-Young Song),엄기문(Gi-Mun Um),이희경(Heekyung Lee),임성용(Sung Yong Lim),서정일(Jeongil Seo),조동현(Donghyeon Cho) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.11
Parallax distortion by depth difference occurs frequently when two images are stitched, as objects and backgrounds are usually not located at a uniform distance from the camera. To solve these problems, existing methods tried to obtain multiple homography for each region, then combine them based on certain energy functions. However, these methods are inefficient and complex because they are performed by a series of stages. In this paper, we introduce an end-to-end by a deep learning network for image stitching method, which is robust against distortion by depth difference. In order to train our end-to-end deep convolutional neural network (CNN), we construct a dataset by using CARLA simulator. Our dataset consists of a pair of left and right images with a narrow field of view as inputs, and a center image with a wide field of view. Thus, the proposed network takes left and right images as input and directly generates images with a wide field of view. We show the excellence of the proposed dataset and method through various experiments.