An image-dependent model of veiling glare effects on detection performance in large-luminance-range displays

2012 ◽  
Author(s):  
Mina Choi ◽  
Luigi Albani ◽  
Aldo Badano
Keyword(s):  
Detection Performance ◽  
Dependent Model ◽  
Luminance Range

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.

Communication: Photoinduced Carbon Dioxide Binding with Surface-Functionalized Silicon Quantum Dots

2018 ◽  
Author(s):  
Oscar A. Douglas-Gallardo ◽  
Cristián Gabriel Sánchez ◽  
Esteban Vöhringer-Martinez
Keyword(s):  
Carbon Dioxide ◽  
Quantum Dots ◽  
Atmospheric Carbon Dioxide ◽  
Density Functional ◽  
Tight Binding ◽  
Carbon Dioxide Concentration ◽  
Research Area ◽  
Silicon Quantum Dots ◽  
Dependent Model ◽  
Photoinduced Charge

<div> <div> <div> <p>Nowadays, the search of efficient methods able to reduce the high atmospheric carbon dioxide concentration has turned into a very dynamic research area. Several environmental problems have been closely associated with the high atmospheric level of this greenhouse gas. Here, a novel system based on the use of surface-functionalized silicon quantum dots (sf -SiQDs) is theoretically proposed as a versatile device to bind carbon dioxide. Within this approach, carbon dioxide trapping is modulated by a photoinduced charge redistribution between the capping molecule and the silicon quantum dots (SiQDs). Chemical and electronic properties of the proposed SiQDs have been studied with Density Functional Theory (DFT) and Density Functional Tight-Binding (DFTB) approach along with a Time-Dependent model based on the DFTB (TD-DFTB) framework. To the best of our knowledge, this is the first report that proposes and explores the potential application of a versatile and friendly device based on the use of sf -SiQDs for photochemically activated carbon dioxide fixation. </p> </div> </div> </div>

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

Towards a Trajectory-Dependent Model of (Human) Rational Agency

2018 ◽  
Author(s):  
Karen Jones
Keyword(s):  
Regulatory Role ◽  
Rational Agency ◽  
Dependent Model ◽  
Reliable Path

This chapter addresses the question, “What is the role and authority of conscious deliberation and judgment in human rational agency?” Anti-rationalists claim that the rationalist account of its role and authority is mistaken: conscious deliberation and judgment plays a relatively small part in our practical lives, can be used in the service of rationalizing bullshit, and is not the only or necessarily the most reliable path of access to our reasons. Against the anti-rationalist, the chapter argues that their critique rests on an analogy between the authority of judgment and the authority of an expert, when the rationalist models judgment’s authority on that of a judge. Against the traditional rationalist, the chapter argues the judge model fails. The chapter explores a third model—the monitor model—which, like rationalism, gives our reflective capacities a significant regulatory role, but accommodates the anti-rationalist emphasis on emotion and fast non-deliberative action.

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