scholarly journals Visualizing the lightning flashes over the Republic of Azerbaijan by analyzing the lightning imaging sensor data

2021 ◽  
Author(s):  
R. Mammadov ◽  
◽  
A.A. Rasouli ◽  
S. Safarov ◽  
E. Safarov ◽  
...  
Keyword(s):  
Sensor Data ◽  
The Republic ◽  
Imaging Sensor

Source Camera Identification Based on Sensor Readout Noise

2010 ◽  
Vol 2 (3) ◽  
pp. 28-42 ◽  
Author(s):  
H. R. Chennamma ◽  
Lalitha Rangarajan
Keyword(s):  
Computer Software ◽  
Sensor Data ◽  
Processing Parameter ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Approach ◽  
Deviation Plot ◽  
Readout Noise ◽  
The One ◽  
Imaging Sensor

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

scholarly journals Target-Tracking based on Fusion of Unsynchronized Sensor Data from Vision System and Thermal Imaging Sensor

2012 ◽  
Vol 25 (11) ◽  
pp. 323-327 ◽  
Author(s):  
Tomohiro Umetani ◽  
Naomichi Kuga ◽  
Masahiro Tanaka ◽  
Masahiro Wada ◽  
Minoru Ito
Keyword(s):  
Target Tracking ◽  
Thermal Imaging ◽  
Vision System ◽  
Sensor Data ◽  
Imaging Sensor

Source Camera Identification Based on Sensor Readout Noise

2012 ◽  
pp. 21-34
Author(s):  
H. R. Chennamma ◽  
Lalitha Rangarajan
Keyword(s):  
Computer Software ◽  
Sensor Data ◽  
Processing Parameter ◽  
Source Camera Identification ◽  
Camera Identification ◽  
Novel Approach ◽  
Deviation Plot ◽  
Readout Noise ◽  
The One ◽  
Imaging Sensor

A digitally developed image is a viewable image (TIFF/JPG) produced by a camera’s sensor data (raw image) using computer software tools. Such images might use different colour space, demosaicing algorithms or by different post processing parameter settings which are not the one coded in the source camera. In this regard, the most reliable method of source camera identification is linking the given image with the sensor of camera. In this paper, the authors propose a novel approach for camera identification based on sensor’s readout noise. Readout noise is an important intrinsic characteristic of a digital imaging sensor (CCD or CMOS) and it cannot be removed. This paper quantitatively measures readout noise of the sensor from an image using the mean-standard deviation plot, while in order to evaluate the performance of the proposed approach, the authors tested against the images captured at two different exposure levels. Results show datasets containing 1200 images acquired from six different cameras of three different brands. The success of proposed method is corroborated through experiments.

Integration of imaging sensor data into a synthetic vision display

2005 ◽  
Author(s):  
E. Theunissen ◽  
F.D. Roefs ◽  
G.J.M. Koeners ◽  
R.M. Rademaker ◽  
T.J. Etherington
Keyword(s):  
Sensor Data ◽  
Synthetic Vision ◽  
Imaging Sensor

Deep activity recognition on imaging sensor data

2019 ◽  
Vol 55 (17) ◽  
pp. 928-931
Author(s):  
Feri Setiawan ◽  
Bernardo Nugroho Yahya ◽  
Seok‐Lyong Lee
Keyword(s):  
Activity Recognition ◽  
Sensor Data ◽  
Deep Activity ◽  
Imaging Sensor

scholarly journals Features and applications of a field imaging chlorophyll fluorometer to measure stress in agricultural plants

2020 ◽  
Author(s):  
Alexander I. Linn ◽  
Alexander K. Zeller ◽  
Erhard E. Pfündel ◽  
Roland Gerhards
Keyword(s):  
Chlorophyll Fluorescence ◽  
Plant Stress ◽  
Pulse Amplitude ◽  
Sensor Data ◽  
Stress Detection ◽  
Herbicide Resistant ◽  
Primary Stress ◽  
Field Estimation ◽  
Agricultural Plants ◽  
Imaging Sensor

Abstract Most non-destructive methods for plant stress detection do not measure the primary stress response but reactions of processes downstream of primary events. For instance, the chlorophyll fluorescence ratio Fv/Fm, which indicates the maximum quantum yield of photosystem II, can be employed to monitor stress originating elsewhere in the plant cell. This article describes the properties of a sensor to quantify herbicide and pathogen stress in agricultural plants for field applications by the Fv/Fm parameter. This dedicated sensor is highly mobile and measures images of pulse amplitude modulated (PAM) chlorophyll fluorescence. Special physical properties of the sensor are reported, and the range of its field applications is defined. In addition, detection of herbicide resistant weeds by employing an Fv/Fm-based classifier is described. The PAM-imaging sensor introduced here can provide in-field estimation of herbicide sensitivity in crops and weeds after herbicide treatment before any damage becomes visible. Limitations of the system and the use of a classifier to differentiate between stressed and non-stressed plants based on sensor data are presented. It is concluded that stress detection by the Fv/Fm parameter is suitable as an expert tool for decision making in crop management.

Modeling and fusion of radar and imaging sensor data for target tracking

1996 ◽  
Vol 35 (3) ◽  
pp. 659 ◽  
Author(s):  
Jay Brent Romine
Keyword(s):  
Target Tracking ◽  
Sensor Data ◽  
Imaging Sensor

Simulation of Meteosat Third Generation-Lightning Imager through tropical rainfall measuring mission: Lightning Imaging Sensor data

2008 ◽  
Author(s):  
Daniele Biron ◽  
Luigi De Leonibus ◽  
Paolo Laquale ◽  
Demetrio Labate ◽  
Francesco Zauli ◽  
...  
Keyword(s):  
Tropical Rainfall Measuring Mission ◽  
Sensor Data ◽  
Third Generation ◽  
Tropical Rainfall ◽  
Imaging Sensor
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