2篇
您的检索式：作者名="Qichuan GENG"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Survey of recent progress in semantic image segmentation with CNNs显示文摘In recent years, convolutional neural networks(CNNs) are leading the way in many computer vision tasks, such as image classification, object detection, and face recognition. In order to produce more refined semantic image segmentation, we survey the powerful CNNs and novel elaborate layers, structures and strategies, especially including those that have achieved the state-of-the-art results on the Pascal VOC 2012 semantic segmentation challenge. Moreover, we discuss their different working stages and various mechanisms to utilize the structural and contextual information in the image and feature spaces. Finally, combining some popular underlying referential methods in homologous problems, we propose several possible directions and approaches to incorporate existing effective methods as components to enhance CNNs for the segmentation of specific semantic objects. | Qichuan GENG Zhong ZHOU Xiaochun CAO | 2018 | Science China(Information Sciences)2018，61，5： | 7 |
| 2 | Automatic facade recovery from single nighttime image显示文摘Nighttime images are difficult to process due to insufficient brightness,lots of noise,and lack of details.Therefore,they are always removed from time-lapsed image analysis.It is interesting that nighttime images have a unique and wonderful building features that have robust and salient lighting cues from human activities.Lighting variation depicts both the statistical and individual habitation,and it has an inherent man-made repetitive structure from architectural theory.Inspired by this,we propose an automatic nighttime fa?ade recovery method that exploits the lattice structures of window lighting.First,a simple but efficient classification method is employed to determine the salient bright regions,which may be lit windows.Then we groupwindows into multiple lattice proposals with respect to fa?ades by patch matching,followed by greedily removing overlapping lattices.Using the horizon constraint,we solve the ambiguous proposals problem and obtain the correct orientation.Finally,we complete the generated fa?ades by filling in the missing windows.This method is well suited for use in urban environments,and the results can be used as a good single-view compensation method for daytime images.The method also acts as a semantic input to other learning-based 3D image reconstruction techniques.The experiment demonstrates that our method works well in nighttime image datasets,and we obtain a high lattice detection rate of 82.1%of 82 challenging images with a low mean orientation error of 12.1±4.5 degrees. | Yi ZHOU Qichuan GENG Zhong ZHOU Wei WU | 2020 | Frontiers of Computer Science2020，14，1： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员