Target-acquisition performance in undersampled infrared imagers: static imagery to motion video

Krapels, Keith A.; Driggers, Ronald G.; Teaney, Brian P.

doi:10.1364/ao.44.007055

Cited by 14 publications

(12 citation statements)

References 5 publications

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“…The results are in agreement with the general finding [3][4][5][6][7][8] that motion with under-sampled cameras can significantly increase observer performance and at the same time provide a quantitative explanation for the marginal performance difference between the identification of hand-held objects in static and dynamic imagery reported by Beintema et al 9 As explained, the latter is the only study performed with an uncooled thermal imager moving at a speed of 0.57 pixel∕ frame, hence the results are considerably affected by detector smear.…”

Section: Introductionsupporting

confidence: 93%

Effectiveness assessment of signal processing in the presence of smear

Bijl

2012

Opt. Eng

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Abstract. Moving imagery from a static scene was recorded with an uncooled thermal imager at nine different angular velocities ranging from 0 (static) to 1 pixel∕frame (3.75 deg ∕s) using a tilted rotating mirror. The scene contained a thermal acuity test chart with triangular test patterns based on the triangle orientation discrimination (TOD) test method. The imagery was processed with different types of image enhancement: dynamic super-resolution (DSR), local adaptive contrast enhancement (LACE), and combinations. DSR shows a significant performance improvement at low velocities, a moderate improvement at medium velocities where smear becomes apparent, and no benefit at high speed. Performance with LACE is close to optimized gain and level setting by hand. Static performance and dynamic performance at 0.57 pixel∕frame containing significant smear were compared with earlier published identification performance data for two-hand held systems collected under a variety of signal processing conditions. It shows that the ratio M 75 between the 75% correct threshold size for the two-hand held objects and the TOD triangle is preserved under all conditions measured. Thus, TA range prediction based on the TOD is robust against a complex combination of conditions, including motion, smear, and the types of image enhancement applied.

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Section: Introductionsupporting

confidence: 93%

Effectiveness assessment of signal processing in the presence of smear

Bijl

2012

Opt. Eng

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“…Indeed, some motion smear was visible on individual images. Krapels 25 used a similar speed but with a cooled MWIR camera that did not induce smear. We found little influence of playing back motion at lower speed, suggesting that motion smear on the recordings may have been the primary factor.…”

Section: Discussionmentioning

confidence: 99%

“…Thus strictly these data may not be used as a validation of NVThermIP. Effects of motion and DSR on under-sampled uncooled thermal imagery are studied in both laboratory and field 25,26,27 , but most are on the TOD triangle laboratory test pattern and not on realistic targets. Recently, one study has been reported on IR system field performance with super-resolution 28 , using a tank target set.…”

Section: Introductionmentioning

confidence: 99%

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Target acquisition performance: effects of target aspect angle, dynamic imaging, and signal processing

Beintema¹,

Bijl²,

Hogervorst³

et al. 2008

SPIE Proceedings

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In an extensive Target Acquisition (TA) performance study, we recorded static and dynamic imagery of a set of military and civilian two-handheld objects at a range of distances and aspect angles with an under-sampled uncooled thermal imager. Next, we applied signal processing techniques including DSR (Dynamic Super Resolution) and LACE (Local Adaptive Contrast Enhancement) to the imagery. In a perception experiment, we determined identification (ID) and threat/non-threat discrimination performance as a function of target range for a variety of conditions. The experiment was performed to validate and extend current TA models. In addition, range predictions were performed with two TA models: the TOD model and NVThermIP. The results of the study are: i) target orientation has a strong effect on performance, ii) the effect of target orientation is well predicted by the two TA models, iii) absolute identification range is close the range predicted with the two models using the recommended criteria for two-handheld objects, iv) there was no positive effect of sensor motion on performance, and this was against the expectations based on earlier studies, v) the benefit of DSR was smaller than expected on the basis of the model predictions, and vi) performance with LACE was similar to performance on an image optimized manually, indicating that LACE can be used to optimize the contrast automatically. The relatively poor results with motion and DSR are probably due to motion smear induced by a higher camera speed than used in earlier studies. Camera motion magnitude and smear are not yet implemented in TA models.

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“…Our approach avoids any explicit reconstruction and relies on perceptual processes to ensure detail enhancement. Krapels et al [2005] as well as Bijl et al [2006] reported better object discrimination for subpixel camera panning than for corresponding static frames. Object discrimination improved regardless of the subpixel sensor motion rate, except for critical velocities [Tekalp 1995, C. 13] such as a one-pixel shift.…”

Section: Optimal Reconstructionmentioning

confidence: 93%

Apparent resolution enhancement for animations

Templin

Didyk

Ritschel

et al. 2011

Proceedings of the 27th Spring Conference on Computer Graphics

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Figure 1: Highly detailed animations can be difficult to reproduce on current display devices. Here, four frames from animations used to evaluate our apparent resolution enhancement are shown: (a) rendering of a ball textured with text; (b) rendering of a detailed assembly of fibers; (c) a rendered short film ("Big Buck Bunny" c by Blender Foundation); (d) high-resolution content captured with a video camera. AbstractPresenting the variety of high resolution images captured by highquality devices, or generated on the computer, is challenging due to the limited resolution of current display devices. Our recent work addressed this problem by taking into account human perception. By applying a specific motion to a high-resolution image shown on a low-resolution display device, human eye tracking and integration could be exploited to achieve apparent resolution enhancement. To this end, the high-resolution image is decomposed into a sequence of temporally varying low-resolution images that are displayed at high refresh rates. However, this approach is limited to a specific class of simple or constant movements, i. e. "panning". In this work, we generalize this idea to arbitrary motions, as well as to videos with arbitrary motion flow. The resulting image sequences are compared to a range of other down-sampling methods.

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Target-acquisition performance in undersampled infrared imagers: static imagery to motion video

Cited by 14 publications

References 5 publications

Effectiveness assessment of signal processing in the presence of smear

Effectiveness assessment of signal processing in the presence of smear

Target acquisition performance: effects of target aspect angle, dynamic imaging, and signal processing

Apparent resolution enhancement for animations

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