scholarly journals A transformation-aware perceptual image metric

2015 ◽  
Author(s):  
Petr Kellnhofer ◽  
Tobias Ritschel ◽  
Karol Myszkowski ◽  
Hans-Peter Seidel
Keyword(s):  
Image Metric

scholarly journals Towards A Semantic Perceptual Image Metric

2018 ◽  
Author(s):  
Troy Chinen ◽  
Johannes Balle ◽  
Chunhui Gu ◽  
Sung Jin Hwang ◽  
Sergey Ioffe ◽  
...  
Keyword(s):  
Image Metric

Image Metric Analysis of Laser Jamming Effect Based on Wavelet Energy and Spot Size

2013 ◽  
Vol 42 (7) ◽  
pp. 832-838 ◽  
Author(s):  
吴云龙 WU Yun-long ◽  
邵立 SHAO Li ◽  
张恺 ZHANG Kai ◽  
李锋 LI Feng ◽  
孙晓泉 SUN Xiao-quan
Keyword(s):  
Spot Size ◽  
Wavelet Energy ◽  
Metric Analysis ◽  
Image Metric ◽  
Jamming Effect

scholarly journals Computationally Efficient Spatial Forecast Verification Using Baddeley’s Delta Image Metric

2008 ◽  
Vol 136 (5) ◽  
pp. 1747-1757 ◽  
Author(s):  
Eric Gilleland ◽  
Thomas C. M. Lee ◽  
John Halley Gotway ◽  
R. G. Bullock ◽  
Barbara G. Brown
Keyword(s):  
Spatial Scales ◽  
Forecast Error ◽  
Test Cases ◽  
Motivating Factors ◽  
Forecast Verification ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Binary Images ◽  
Verification Methods ◽  
Image Metric

Abstract An important focus of research in the forecast verification community is the development of alternative verification approaches for quantitative precipitation forecasts, as well as for other spatial forecasts. The need for information that is meaningful in an operational context and the importance of capturing the specific sources of forecast error at varying spatial scales are two primary motivating factors. In this paper, features of precipitation as identified by a convolution threshold technique are merged within fields and matched across fields in an automatic and computationally efficient manner using Baddeley’s metric for binary images. The method is carried out on 100 test cases, and 4 representative cases are shown in detail. Results of merging and matching objects are generally positive in that they are consistent with how a subjective observer might merge and match features. The results further suggest that the Baddeley metric may be useful as a computationally efficient summary metric giving information about location, shape, and size differences of individual features, which could be employed for other spatial forecast verification methods.

scholarly journals Evaluating Barten image metric for predicting character recognition in people with low vision

2013 ◽  
Vol 50 (10) ◽  
pp. 1343-1352
Author(s):  
Kimberly A. Schoessow ◽  
Lisa M. Mauney ◽  
Mark Uslan ◽  
Ronald A. Schuchard
Keyword(s):  
Character Recognition ◽  
Low Vision ◽  
Image Metric

scholarly journals Diagnosing the Sensitivity of Binary Image Measures to Bias, Location, and Event Frequency within a Forecast Verification Framework

2011 ◽  
Vol 26 (6) ◽  
pp. 1032-1044 ◽  
Author(s):  
Benjamin R. J. Schwedler ◽  
Michael E. Baldwin
Keyword(s):  
Image Processing ◽  
Hausdorff Distance ◽  
Contingency Table ◽  
Spatial Relationship ◽  
Distance Measures ◽  
Forecast Verification ◽  
Event Frequency ◽  
Image Metrics ◽  
Considerable Impact ◽  
Image Metric

Abstract While the use of binary distance measures has a substantial history in the field of image processing, these techniques have only recently been applied in the area of forecast verification. Designed to quantify the distance between two images, these measures can easily be extended for use with paired forecast and observation fields. The behavior of traditional forecast verification metrics based on the dichotomous contingency table continues to be an area of active study, but the sensitivity of image metrics has not yet been analyzed within the framework of forecast verification. Four binary distance measures are presented and the response of each to changes in event frequency, bias, and displacement error is documented. The Hausdorff distance and its derivatives, the modified and partial Hausdorff distances, are shown only to be sensitive to changes in base rate, bias, and displacement between the forecast and observation. In addition to its sensitivity to these three parameters, the Baddeley image metric is also sensitive to additional aspects of the forecast situation. It is shown that the Baddeley metric is dependent not only on the spatial relationship between a forecast and observation but also the location of the events within the domain. This behavior may have considerable impact on the results obtained when using this measure for forecast verification. For ease of comparison, a hypothetical forecast event is presented to quantitatively analyze the various sensitivities of these distance measures.

scholarly journals Reconstruction of the Image Metric of Periodic Structures in an Opto-Digital Angle Measurement System

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4411
Author(s):  
Alexander N. Korolev ◽  
Alexander Ya. Lukin ◽  
Yurii V. Filatov ◽  
Vladimir Yu. Venediktov
Keyword(s):  
Periodic Structures ◽  
Angular Position ◽  
Digital Camera ◽  
Angle Measurement ◽  
Camera System ◽  
Optical Pattern ◽  
Image Metric ◽  
The Ideal ◽  
Camera Sensor

Measurement of the object angular position and its change is one of the important tasks in measurement technique. Our method is based on determination of the angular position of a 2D periodical optical pattern (2D mark) at the object, captured by the sensor of a digital camera. System performance can be frustrated by errors in determination of the spot coordinates on the camera sensor; by the presence of lens aberrations; by deviations from the parallelism of the pattern planes and the camera sensor; and by differences between the actual spots positions and the ideal grid. In the paper we discuss the effect of these errors and the way to correct or eliminate them. We have developed the mathematical routine and the corresponding numerical codes for correction of the said errors. The code and the routine we checked in a real experiment. It has shown that the correction decreases the standard deviation in 15 times.

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