Sparse estimation of backscattered echoes from underwater object using integrated dictionaries

2018 ◽  
Vol 144 (6) ◽  
pp. 3475-3484
Author(s):  
Xiangxia Meng ◽  
Xiukun Li ◽  
Andreas Jakobsson ◽  
Yahui Lei
Keyword(s):  
Sparse Estimation ◽  
Underwater Object

IMPLEMENTASI METODE RETINEX UNTUK MENINGKATKAN KUALITAS CITRA UNDERWATER

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Bainun Harahap
Keyword(s):  
Water Environment ◽  
Repair Process ◽  
Image Process ◽  
Two Dimensional ◽  
Two Dimensional Image ◽  
Underwater Image ◽  
Object Search ◽  
Image Improvement ◽  
Underwater Object

Digital imagery is a two-dimensional image process through a digital computer that is used to manipulate and modify images in various ways. Photos are examples of two-dimensional images that can be processed easily. Each photo in the form of a digital image can be processed through certain software devices. In the water environment, light factors greatly influence the results of image quality obtained. With the deepening of underwater shooting, the results obtained will be the darker the quality of the underwater image. Underwater imagery is widely used as an object in various activities such as underwater habitat mapping, underwater environment monitoring, underwater object search. Uneven lighting and colors that tend to be bluish and runny. One of the factors that influence the recognition results in pattern recognition is the quality of the image that is inputted. The image acquired from the source does not always have good quality. The process of improvement in digital images that experience interference in lighting and exposure to sunlight. The lighting repair process uses the retinex method and will compare the quality of the two methods later. Until later the results of both methods can be seen which is better. Retinex method can produce image improvement with high performance.Keywords: Digital Cintra, Underwater, Matlab Retinex Method

scholarly journals Reactive underwater object inspection based on artificial electric sense

2016 ◽  
Vol 11 (4) ◽  
pp. 045003 ◽  
Author(s):  
Vincent Lebastard ◽  
Frédéric Boyer ◽  
Sylvain Lanneau
Keyword(s):  
Underwater Object ◽  
Object Inspection

scholarly journals A Novel Detection Scheme with Multiple Observations for Sparse Signal Based on Likelihood Ratio Test with Sparse Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-14
Author(s):  
Hongbo Zhao ◽  
Lei Chen ◽  
Wenquan Feng ◽  
Chuan Lei
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Prior Information ◽  
Finite Size ◽  
Sparse Signals ◽  
Ratio Test ◽  
Sparse Estimation ◽  
Detection Scheme ◽  
Error Exponent ◽  
Multiple Observations

Recently, the problem of detecting unknown and arbitrary sparse signals has attracted much attention from researchers in various fields. However, there remains a peck of difficulties and challenges as the key information is only contained in a small fraction of the signal and due to the absence of prior information. In this paper, we consider a more general and practical scenario of multiple observations with no prior information except for the sparsity of the signal. A new detection scheme referred to as the likelihood ratio test with sparse estimation (LRT-SE) is presented. Under the Neyman-Pearson testing framework, LRT-SE estimates the unknown signal by employing thel1-minimization technique from compressive sensing theory. The detection performance of LRT-SE is preliminarily analyzed in terms of error probabilities in finite size and Chernoff consistency in high dimensional condition. The error exponent is introduced to describe the decay rate of the error probability as observations number grows. Finally, these properties of LRT-SE are demonstrated based on the experimental results of synthetic sparse signals and sparse signals from real satellite telemetry data. It could be concluded that the proposed detection scheme performs very close to the optimal detector.

Underwater Object Recognition for Offshore Wind Farm Environmental Impact Assessment

2021 ◽  
Author(s):  
Chun-Chih Lo ◽  
Yi-Ray Tseng ◽  
Chien-Chou Shih ◽  
Shu-Wei Guo ◽  
Chin-Shiuh Shieh ◽  
...  
Keyword(s):  
Object Recognition ◽  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Underwater Object
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