The effect of spatial noise of LCD displays on the detection capabilities of the human observer

2005 ◽  
Author(s):  
Kunal A. Gandhi ◽  
Jiahua Fan ◽  
Hans Roehrig ◽  
Malur K. Sundareshan ◽  
Elizabeth A. Krupinski
Keyword(s):  
Human Observer ◽  
Spatial Noise ◽  
Detection Capabilities ◽  
Lcd Displays

LCD spatial noise and human observer performance

2005 ◽  
Vol 1281 ◽  
pp. 92-97
Author(s):  
Hans Roehrig ◽  
Jiahua Fan ◽  
Kunal Gandhi ◽  
Elizabeth Krupinksi
Keyword(s):  
Observer Performance ◽  
Human Observer ◽  
Spatial Noise

scholarly journals Solution for Nonuniformities and Spatial Noise in Medical LCD Displays by Using Pixel-Based Correction

2005 ◽  
Vol 18 (3) ◽  
pp. 209-218 ◽  
Author(s):  
Tom Kimpe ◽  
Albert Xthona ◽  
Paul Matthijs ◽  
Lode De Paepe
Keyword(s):  
Spatial Noise ◽  
Lcd Displays

Spatial noise and threshold contrasts in LCD displays

2003 ◽  
Author(s):  
Hans Roehrig ◽  
Elizabeth A. Krupinski ◽  
Amarpreet S. Chawla ◽  
Jiahua Fan ◽  
Kunal Gandhi
Keyword(s):  
Spatial Noise ◽  
Lcd Displays

Spatial noise suppression for LCD displays

2009 ◽  
Author(s):  
William J. Dallas ◽  
Hans Roehrig ◽  
Jiahua Fan ◽  
Elizabeth A. Krupinski ◽  
Jeffrey P. Johnson
Keyword(s):  
Noise Suppression ◽  
Spatial Noise ◽  
Lcd Displays

scholarly journals Detection and Model of Thermal Traces Left after Aggressive Behavior of Laboratory Rodents

2021 ◽  
Vol 11 (14) ◽  
pp. 6644
Author(s):  
Magdalena Mazur-Milecka ◽  
Jacek Ruminski ◽  
Wojciech Glac ◽  
Natalia Glowacka
Keyword(s):  
Social Behavior ◽  
Aggressive Behavior ◽  
Human Observer ◽  
Temperature Changes ◽  
Aggressive Action ◽  
Laboratory Rodents ◽  
Detection Capabilities ◽  
Exact Moment ◽  
Original Frame ◽  
Complex Social Behavior

Automation of complex social behavior analysis of experimental animals would allow for faster, more accurate and reliable research results in many biological, pharmacological, and medical fields. However, there are behaviors that are not only difficult to detect for the computer, but also for the human observer. Here, we present an analysis of the method for identifying aggressive behavior in thermal images by detecting traces of saliva left on the animals’ fur after a bite, nape attack, or grooming. We have checked the detection capabilities using simulations of social test conditions inspired by real observations and measurements. Detection of simulated traces different in size and temperature on single original frame revealed the dependence of the parameters of commonly used corner detectors (R score, ranking) on the parameters of the traces. We have also simulated temperature of saliva changes in time and proved that the detection time does not affect the correctness of the approximation of the observed process. Furthermore, tracking the dynamics of temperature changes of these traces allows to conclude about the exact moment of the aggressive action. In conclusion, the proposed algorithm together with thermal imaging provides additional data necessary to automate the analysis of social behavior in rodents.

Spatial noise suppression for LCD displays: noise contrast

2010 ◽  
Author(s):  
William J. Dallas ◽  
Hans Roehrig ◽  
Jiahua Fan ◽  
Elizabeth A. Krupinski ◽  
Jeffrey P. Johnson
Keyword(s):  
Noise Suppression ◽  
Spatial Noise ◽  
Lcd Displays

Spatial noise suppression for LCD displays

2009 ◽  
Author(s):  
William J. Dallas ◽  
Hans Roehrig ◽  
Jiahua Fan ◽  
Elizabeth A. Krupinski ◽  
Jeffrey Johnson
Keyword(s):  
Noise Suppression ◽  
Spatial Noise ◽  
Lcd Displays

Read, Understand, Learn, & Excel: Development and Testing of an Automated Reading Strategy Detection Algorithm for Postsecondary Students

2019 ◽  
Vol 28 (3) ◽  
pp. 1257-1267 ◽  
Author(s):  
Priya Kucheria ◽  
McKay Moore Sohlberg ◽  
Jason Prideaux ◽  
Stephen Fickas
Keyword(s):  
Academic Success ◽  
Undergraduate Students ◽  
Expository Text ◽  
Reading Strategy ◽  
Detection Algorithm ◽  
Expository Texts ◽  
Human Observer ◽  
Behavioral Strategies ◽  
Periodic Review ◽  
Wide Range

PurposeAn important predictor of postsecondary academic success is an individual's reading comprehension skills. Postsecondary readers apply a wide range of behavioral strategies to process text for learning purposes. Currently, no tools exist to detect a reader's use of strategies. The primary aim of this study was to develop Read, Understand, Learn, & Excel, an automated tool designed to detect reading strategy use and explore its accuracy in detecting strategies when students read digital, expository text.MethodAn iterative design was used to develop the computer algorithm for detecting 9 reading strategies. Twelve undergraduate students read 2 expository texts that were equated for length and complexity. A human observer documented the strategies employed by each reader, whereas the computer used digital sequences to detect the same strategies. Data were then coded and analyzed to determine agreement between the 2 sources of strategy detection (i.e., the computer and the observer).ResultsAgreement between the computer- and human-coded strategies was 75% or higher for 6 out of the 9 strategies. Only 3 out of the 9 strategies–previewing content, evaluating amount of remaining text, and periodic review and/or iterative summarizing–had less than 60% agreement.ConclusionRead, Understand, Learn, & Excel provides proof of concept that a reader's approach to engaging with academic text can be objectively and automatically captured. Clinical implications and suggestions to improve the sensitivity of the code are discussed.Supplemental Materialhttps://doi.org/10.23641/asha.8204786

Using watermark visibility measurements to select an optimized pair of spot colors for use in a binary watermark

2020 ◽  
Vol 2020 (15) ◽  
pp. 197-1-197-7
Author(s):  
Alastair Reed ◽  
Vlado Kitanovski ◽  
Kristyn Falkenstern ◽  
Marius Pedersen
Keyword(s):  
Food Packaging ◽  
Red Light ◽  
Human Observer ◽  
Viewing Distance ◽  
Point Of Sale ◽  
Complementary Color ◽  
Low Visibility ◽  
Packaging Industry ◽  
Spot Color ◽  
Selection Of

Spot colors are widely used in the food packaging industry. We wish to add a watermark signal within a spot color that is readable by a Point Of Sale (POS) barcode scanner which typically has red illumination. Some spot colors such as blue, black and green reflect very little red light and are difficult to modulate with a watermark at low visibility to a human observer. The visibility measurements that have been made with the Digimarc watermark enables the selection of a complementary color to the base color which can be detected by a POS barcode scanner but is imperceptible at normal viewing distance.

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