中国科学院机构知识库网格系统: 检索

Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera 期刊论文 OAI收割
REMOTE SENSING, 2021, 卷号: 13
作者: Wang, Yiren; Liu, Dong; Xie, Wanyi; Yang, Ming; Gao, Zhenyu

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收藏 | 浏览/下载：38/0 | 提交时间：2021/06/15

ground-based cloud observation thermal-infrared camera cloud detection all-sky-view image

Supernovae Detection with Fully Convolutional One-Stage Framework 期刊论文 OAI收割
SENSORS, 2021, 卷号: 21, 期号: 5, 页码: 1926
作者: Yin, Kai; Jia, Juncheng; Gao, Xing; Sun, Tianrui; Zhou, Zhengyin

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收藏 | 浏览/下载：21/0 | 提交时间：2021/04/26

image processing data analysis sky surveys supernova object detection

Sky detection in hazy image 期刊论文 OAI收割
Sensors (Switzerland), 2018, 卷号: 18, 期号: 4, 页码: 1-18
作者: Song YC(宋颖超); Luo HB(罗海波); Chang Z(常铮); Hui B(惠斌); Ma JK(马俊凯)

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收藏 | 浏览/下载：24/0 | 提交时间：2018/06/17

sky detection sky labeling haze-relevant features perceptual hazy density imbalance classifier HazySky

Sky detection in hazy image 期刊论文 OAI收割
SENSORS, 2018, 卷号: 18, 期号: 4, 页码: 1-18
作者: Song YC(宋颖超); Luo HB(罗海波); Ma JK(马俊凯); Hui B(惠斌); Chang Z(常铮)

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收藏 | 浏览/下载：16/0 | 提交时间：2018/06/17

Sky Detection Sky Labeling Haze-relevant Features Perceptual Hazy Density Imbalance Classifier Hazysky

The Automatic Recognition and Detection of Sky-Subtraction Residual Componentin the Stellar Spectra 期刊论文 OAI收割
SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 卷号: 37, 期号: 1, 页码: 273-277
作者: An Ran; Pan Jing-chang; Yi Zhen-ping; Wei Peng

收藏 | 浏览/下载：23/0 | 提交时间：2017/05/25

Sky-substraction Sky-residuals detection Spectral normalization LAMOST

Small Infrared Target Detection Based on Weighted Local Difference Measure 期刊论文 OAI收割
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 卷号: 54, 期号: 7, 页码: 4204-4214
作者: Deng, He; Sun, Xianping; Liu, Maili; Ye, Chaohui; Zhou, Xin

收藏 | 浏览/下载：76/0 | 提交时间：2016/07/12

Cloudy-sky background infrared image small target detection weighted local difference measure (WLDM)

海天背景红外图像舰船目标检测方法研究 学位论文 OAI收割
工学硕士, 中国科学院自动化研究所: 中国科学院大学, 2015
吴芳

收藏 | 浏览/下载：207/0 | 提交时间：2015/09/02

舰船目标检测海天线检测显著性分析红外图像 ship target detection sea-sky line detection saliency analysis infrared images

Sky Region Detection in a Single Image for Autonomous Ground Robot Navigation 期刊论文 OAI收割
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS, 2013, 卷号: 10, 期号: 0
作者: Shen, YH(沈晔湖)

收藏 | 浏览/下载：55/0 | 提交时间：2014/01/15

Sky Detection Gradient Information Energy Function Optimization Robot Navigation

Research on infrared dim-point target detection and tracking under sea-sky-line complex background (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
作者: Dong Y.-X.; Zhang H.-B.; Li Y.; Li Y.; Li Y.

收藏 | 浏览/下载：108/0 | 提交时间：2013/03/25

Target detection and tracking technology in infrared image is an important part of modern military defense system. Infrared dim-point targets detection and recognition under complex background is a difficulty and important strategic value and challenging research topic. The main objects that carrier-borne infrared vigilance system detected are sea-skimming aircrafts and missiles. Due to the characteristics of wide field of view of vigilance system the target is usually under the sea clutter. Detection and recognition of the target will be taken great difficulties.There are some traditional point target detection algorithms such as adaptive background prediction detecting method. When background has dispersion-decreasing structure the traditional target detection algorithms would be more useful. But when the background has large gray gradient such as sea-sky-line sea waves etc.The bigger false-alarm rate will be taken in these local area.It could not obtain satisfactory results. Because dim-point target itself does not have obvious geometry or texture feature in our opinion from the perspective of mathematics the detection of dim-point targets in image is about singular function analysis.And from the perspective image processing analysis the judgment of isolated singularity in the image is key problem. The foregoing points for dim-point targets detection its essence is a separation of target and background of different singularity characteristics.The image from infrared sensor usually accompanied by different kinds of noise. These external noises could be caused by the complicated background or from the sensor itself. The noise might affect target detection and tracking. Therefore the purpose of the image preprocessing is to reduce the effects from noise also to raise the SNR of image and to increase the contrast of target and background. According to the low sea-skimming infrared flying small target characteristics the median filter is used to eliminate noise improve signal-to-noise ratio then the multi-point multi-storey vertical Sobel algorithm will be used to detect the sea-sky-line so that we can segment sea and sky in the image. Finally using centroid tracking method to capture and trace target. This method has been successfully used to trace target under the sea-sky complex background. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

An improved two-dimensional entropy method for star trail tracing in deep sky (EI CONFERENCE) 会议论文 OAI收割
ICO20: Optical Information Processing, August 21, 2005 - August 26, 2005, Changchun, China
作者: Wang Y.-J.; Yao Z.-J.

收藏 | 浏览/下载：30/0 | 提交时间：2013/03/25

The trace of star trail is an important component of deep sky detection. The stars are low contrast targets and their self-rotation will make their brightness change in cycle. Above all the trail trace is vulnerable to the block and disturbance of other stars. Traditional one-dimensional maximum entropy thresholding algorithm is vulnerable to the noise and the calculation of two-dimensional entropy methods is too large and takes too much time. This paper proposes an improved two-dimensional entropy threshold algorithm. We use recursion iteration method to eliminate the redundancy calculation and reduce the size of two-dimensional histogram based on the deep sky stars characteristic such as low contrast fuzziness and the centralized histogram. We also combine our algorithm with the space trail trace model to forecast the star trace. Experiments results show when the star are blocked or they turn dark the method still can well extrapolate the star trace. Our method improves the capability of trailing the ebb and small star and increases the precision of tracing. It is also robust to the noise so there is a good application foreground for the method.