Limiting Detection Performance for Random Signals of Unknown Location, Structure, Extent, and Strength.

1995 ◽  
Author(s):  
Albert E. Nuttall
Keyword(s):  
Detection Performance ◽  
Random Signals

Systematic error of LSM-estimation of covariance components of biperiodically correlated random signals

2019 ◽  
Vol 2019 (47) ◽  
pp. 26-33
Author(s):  
I. M. Javorskyj ◽  
◽  
O. Y. Dzeryn ◽  
R. M. Yuzefovych ◽  
◽  
...  
Keyword(s):  
Systematic Error ◽  
Covariance Components ◽  
Random Signals

CNN performance dependence on linear image processing

2020 ◽  
Vol 2020 (10) ◽  
pp. 310-1-310-7
Author(s):  
Khalid Omer ◽  
Luca Caucci ◽  
Meredith Kupinski
Keyword(s):  
Image Processing ◽  
Texture Classification ◽  
Full Rank ◽  
Detection Performance ◽  
Ideal Observer ◽  
Training Data ◽  
Image Texture ◽  
Training Images ◽  
Analytic Expressions ◽  
Linear Compression

This work reports on convolutional neural network (CNN) performance on an image texture classification task as a function of linear image processing and number of training images. Detection performance of single and multi-layer CNNs (sCNN/mCNN) are compared to optimal observers. Performance is quantified by the area under the receiver operating characteristic (ROC) curve, also known as the AUC. For perfect detection AUC = 1.0 and AUC = 0.5 for guessing. The Ideal Observer (IO) maximizes AUC but is prohibitive in practice because it depends on high-dimensional image likelihoods. The IO performance is invariant to any fullrank, invertible linear image processing. This work demonstrates the existence of full-rank, invertible linear transforms that can degrade both sCNN and mCNN even in the limit of large quantities of training data. A subsequent invertible linear transform changes the images’ correlation structure again and can improve this AUC. Stationary textures sampled from zero mean and unequal covariance Gaussian distributions allow closed-form analytic expressions for the IO and optimal linear compression. Linear compression is a mitigation technique for high-dimension low sample size (HDLSS) applications. By definition, compression strictly decreases or maintains IO detection performance. For small quantities of training data, linear image compression prior to the sCNN architecture can increase AUC from 0.56 to 0.93. Results indicate an optimal compression ratio for CNN based on task difficulty, compression method, and number of training images.

Proposed Concept for Specifying Vehicle Detection Performance

2009 ◽  
Vol 2128 (1) ◽  
pp. 161-172 ◽  
Author(s):  
Dan Middleton ◽  
Ryan Longmire ◽  
Darcy M. Bullock ◽  
James R. Sturdevant
Keyword(s):  
Vehicle Detection ◽  
Detection Performance

Echo Modeling and Detection Performance of Wideband VHF Radar

2014 ◽  
Vol 35 (12) ◽  
pp. 2795-2801
Author(s):  
Jun You ◽  
Xian-rong Wan ◽  
Zi-ping Gong ◽  
Feng Cheng ◽  
Heng-yu Ke
Keyword(s):  
Detection Performance ◽  
Vhf Radar
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