BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20221031T170342Z UID:DC9265A5-80E5-4990-AD29-F3C6E9EA508A DTSTART;TZID=America/New_York:20221031T120000 DTEND;TZID=America/New_York:20221031T130000 DESCRIPTION:In long range imaging applications\, anisoplanatic atmospheric optical turbulence imparts spatially- and temporally-varying blur and geom etric distortions in imagery. The ability to distinguish true scene motion from turbulence warping is important for many image processing and analys is tasks. We present two novel scene-motion detection algorithms specifica lly designed to operate in the presence of anisoplanatic optical turbulenc e.\n\nThe first method is based on modeling background intensity fluctuati ons with a Gaussian mixture model (GMM). The GMM parameters are formed usi ng knowledge of the theoretical turbulence tilt variance statistics derive d from the Fried parameter or refractive index structure function. Thus\, this new method is referred to as the Tilt Variance GMM (TV-GMM) algorithm . While most prior intensity methods use empirical temporal data to estima te a background model\, this approach is based on the theoretical atmosphe ric tilt variance statistics. This technique effectively avoids contaminat ion in the background statistics when true scene motion is present. TV-GMM also considers the application of global image registration as a preproce ssing step to improve performance by employing the recently developed resi dual tilt variance analysis that accounts for image registration.\n\nRathe r than forming its statistical model on the (optionally registered) input imagery directly\, the second method uses a turbulence simulator. Multiple realizations of atmospheric turbulence are applied to a single prototype background image to create a non-contaminated image stack of the scene bac kground. To incorporate the spatial relationship between neighboring pixel s\, each pixel's intensity is treated as an independent variable of a sing le Gaussian distribution. Although full anisoplanatic turbulence simulator s are available\, alternative approaches are sufficient provided that the anisoplanatic warping is accurate. In this work\, a warping simulator base d on tilt field statistics is used to provide a fast and reasonable approx imation. Because this second method uses a warping simulator\, it is refer red to as the Warping Simulator Gaussian Model (WS-GM) algorithm.\n\nBoth quantitative and qualitative performance analyses are conducted\, and the proposed methods are compared against several state-of-the art algorithms using both synthetic and real-world data sets. The three synthetic image d atasets are generated with an anisoplanatic numerical wave-propagation sim ulator that enables per-pixel motion truth. Both TV-GMM and WS-GM outperfo rm the benchmark methods across all turbulence profiles used in this study .\n\nSpeaker(s): Dr. Richard L. Van Hook\, \n\nAgenda: \n12:00-12:05: Intr oduction\n\n12:05-12:35: Technical Presentation by Dr. Van Hook\n\n12:35-1 2:45: Q&A\n\n12:45-1:00: Additional time\, if needed.\n\nVirtual: https:// events.vtools.ieee.org/m/328113 LOCATION:Virtual: https://events.vtools.ieee.org/m/328113 ORGANIZER:michael.callahan.10@us.af.mil SEQUENCE:4 SUMMARY:Tech Talk: Scene Motion Detection in Imagery with Anisoplanatic Opt ical Turbulence URL;VALUE=URI:https://events.vtools.ieee.org/m/328113 X-ALT-DESC:Description: <br /><p>In long range imaging applications\, aniso planatic atmospheric optical turbulence imparts spatially- and temporally- varying blur and geometric distortions in imagery. The ability to distingu ish true scene motion from turbulence warping is important for many image processing and analysis tasks. We present two novel scene-motion detection algorithms specifically designed to operate in the presence of anisoplana tic optical turbulence.</p>\n<p>The first method is based on modeling back ground intensity fluctuations with a Gaussian mixture model (GMM).&nbsp\; The GMM parameters are formed using knowledge of the theoretical turbulenc e tilt variance statistics derived from the Fried parameter or refractive index structure function.&nbsp\; Thus\, this new method is referred to as the <em>Tilt Variance GMM</em> (TV-GMM) algorithm.&nbsp\; While most prior intensity methods use empirical temporal data to estimate a background mo del\, this approach is based on the theoretical atmospheric tilt variance statistics.&nbsp\; This technique effectively avoids contamination in the background statistics when true scene motion is present.&nbsp\; TV-GMM als o considers the application of global image registration as a preprocessin g step to improve performance by employing the recently developed residual tilt variance analysis that accounts for image registration.</p>\n<p>Rath er than forming its statistical model on the (optionally registered) input imagery directly\, the second method uses a turbulence simulator. Multipl e realizations of atmospheric turbulence are applied to a single prototype background image to create a non-contaminated image stack of the scene ba ckground. To incorporate the spatial relationship between neighboring pixe ls\, each pixel's intensity is treated as an independent variable of a sin gle Gaussian distribution. Although full anisoplanatic turbulence simulato rs are available\, alternative approaches are sufficient provided that the anisoplanatic warping is accurate. In this work\, a warping simulator bas ed on tilt field statistics is used to provide a fast and reasonable appro ximation. Because this second method uses a warping simulator\, it is refe rred to as the <em>Warping Simulator Gaussian Model</em> (WS-GM) algorithm .</p>\n<p>Both quantitative and qualitative performance analyses are condu cted\, and the proposed methods are compared against several state-of-the art algorithms using both synthetic and real-world data sets. The three sy nthetic image datasets are generated with an anisoplanatic numerical wave- propagation simulator that enables per-pixel motion truth. Both TV-GMM and WS-GM outperform the benchmark methods across all turbulence profiles use d in this study.</p><br /><br />Agenda: <br /><p>12:00-12:05: Introduction </p>\n<p>12:05-12:35: Technical Presentation by Dr. Van Hook</p>\n<p>12:35 -12:45: Q&amp\;A</p>\n<p>12:45-1:00: Additional time\, if needed.</p> END:VEVENT END:VCALENDAR