Image Processing For Target Detection Using Data From A Staring Mosaic Ir Sensor In Geosynchronous Orbit

1984 ◽  
Author(s):  
Tim J. Patterson ◽  
Douglas M. Chabries ◽  
Richard W. Christiansen
Keyword(s):  
Image Processing ◽  
Target Detection ◽  
Geosynchronous Orbit ◽  
Ir Sensor ◽  
Using Data

Image Processing For Target Detection Using Data From A Staring Mosaic Infrared Sensor In Geosynchronous Orbit

1986 ◽  
Vol 25 (1) ◽  
pp. 251166 ◽  
Author(s):  
Tim J. Patterson ◽  
Douglas M. Chabries ◽  
Richard W. Christiansen
Keyword(s):  
Image Processing ◽  
Target Detection ◽  
Geosynchronous Orbit ◽  
Infrared Sensor ◽  
Using Data

scholarly journals Automatic Acoustic Target Detecting and Tracking on the Azimuth Recording Diagram with Image Processing Methods

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5391
Author(s):  
Fan Yin ◽  
Chao Li ◽  
Haibin Wang ◽  
Fan Yang
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Target Detection ◽  
Ambient Noise ◽  
Arrival Direction ◽  
Processing Methods ◽  
Acoustic Target ◽  
Real Time Tracking ◽  
Passive Acoustic ◽  
Target Detecting

Passive acoustic target detection has been a hot research topic for a few decades. Azimuth recording diagram is one of the most promising techniques to estimate the arrival direction of the interested signal by visualizing the sound wave information. However, this method is challenged by the random ambient noise, resulting in low reliability and short effective distance. This paper presents a real-time postprocessing framework for passive acoustic target detection modalities by using a sonar array, in which image processing methods are used to automate the target detecting and tracking on the azimuth recording diagram. The simulation results demonstrate that the proposed approach can provide a higher reliability compared with the conventional ones, and is suitable for the constraints of real-time tracking.

scholarly journals Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Jordan Ubbens ◽  
Mikolaj Cieslak ◽  
Przemyslaw Prusinkiewicz ◽  
Isobel Parkin ◽  
Jana Ebersbach ◽  
...  
Keyword(s):  
Image Processing ◽  
Association Mapping ◽  
Stress Responses ◽  
Interspecific Cross ◽  
Response To Treatment ◽  
Brassica Napus L ◽  
Nested Association Mapping ◽  
Latent Space ◽  
Using Data ◽  
Candidate Loci

Association mapping studies have enabled researchers to identify candidate loci for many important environmental tolerance factors, including agronomically relevant tolerance traits in plants. However, traditional genome-by-environment studies such as these require a phenotyping pipeline which is capable of accurately measuring stress responses, typically in an automated high-throughput context using image processing. In this work, we present Latent Space Phenotyping (LSP), a novel phenotyping method which is able to automatically detect and quantify response-to-treatment directly from images. We demonstrate example applications using data from an interspecific cross of the model C4 grass Setaria, a diversity panel of sorghum (S. bicolor), and the founder panel for a nested association mapping population of canola (Brassica napus L.). Using two synthetically generated image datasets, we then show that LSP is able to successfully recover the simulated QTL in both simple and complex synthetic imagery. We propose LSP as an alternative to traditional image analysis methods for phenotyping, enabling the phenotyping of arbitrary and potentially complex response traits without the need for engineering-complicated image-processing pipelines.

Decision-level fusion of SAR and IR sensor information for automatic target detection

2017 ◽  
Author(s):  
Young-Rae Cho ◽  
Sung-Hyuk Yim ◽  
Hyun-Woong Cho ◽  
Jin-Ju Won ◽  
Woo-Jin Song ◽  
...  
Keyword(s):  
Target Detection ◽  
Automatic Target Detection ◽  
Decision Level ◽  
Ir Sensor ◽  
Sensor Information ◽  
Decision Level Fusion ◽  
Level Fusion
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