&lt;title&gt;Automatic characterization of electro-optical sensors with image processing using the triangle orientation discrimination (TOD) method&lt;/title&gt;

Lange, Dirk-Jan de; Valeton, J. M.; Bijl, Piet

doi:10.1117/12.391770

Cited by 15 publications

(12 citation statements)

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“…This can be understood afterwards but might have been unnoticed for a long time without doing a test. Third, a test may be used to optimize or tune the parameters of the algorithm for specific sensor, preferably in combination with a vision model 18,19 . In our case, we may use the recordings from this study over and over again to test new algorithms.…”

Section: Discussionmentioning

confidence: 99%

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Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Bijl¹,

Schutte²,

Hogervorst³

2006

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVII

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Current end-to-end sensor performance measures such as the TOD, MRT, DMRT and MTDP were developed to describe Target Acquisition performance for static imaging. Recent developments in sensor technology (e.g. microscan) and image enhancement techniques (e.g. Super Resolution and Scene-Based Non-Uniformity Correction) require that a sensor performance measure can be applied to dynamic imaging as well. We evaluated the above-mentioned measures using static, dynamic (moving) and different types of enhanced imagery of thermal 4-bar and triangle tests patterns. Both theoretical and empirical evidence is provided that the bar-pattern based methods are not suited for dynamic imaging. On the other hand, the TOD method can be applied easily without adaptation to any of the abovementioned conditions, and the resulting TOD data are in correspondence with the expectations. We conclude that the TOD is the only current end-to-end measure that is able to quantify sensor performance for dynamic imaging and dynamic image enhancement techniques.

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Section: Discussionmentioning

confidence: 99%

“…Each block contained the same set of sequences in random order except that the test plate orientations are different (these were also randomly divided over the four blocks). The block presentation order was different for each observer according to a 4 by 4 Latin square design 17,18 .…”

Section: Tod Image Selection and Presentationmentioning

confidence: 99%

Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Bijl¹,

Schutte²,

Hogervorst³

2006

Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVII

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“…A TOD curve can measured with real or a simulated sensor, and with a human observer or a vision model 5,14 . Figure 1 The test pattern or stimulus in the TOD method is an equilateral triangle with one of four possible orientations: apex Up, Down, Left or Right.…”

Section: Tod Sensor Performance Characterizationmentioning

confidence: 99%

Sensor performance as a function of sampling (d) and optical blur (Fλ)

Bijl¹,

Hogervorst²

2009

SPIE Proceedings

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Detector sampling and optical blur are two major factors affecting Target Acquisition (TA) performance with modern EO and IR systems. In order to quantify their relative significance, we simulated five realistic LWIR and MWIR sensors from very under-sampled (detector pitch d >> diffraction blur Fλ) to well-sampled (Fλ >> d). Next, we measured their TOD (Triangle Orientation Discrimination) sensor performance curve. The results show a region that is clearly detectorlimited, a region that is clearly diffraction-limited, and a transition area. For a high contrast target, threshold size T FPA on the sensor focal plane can mathematically be described with a simple linear expression: T FPA =1.5·d ·w(d/Fλ) + 0.95· Fλ·w(Fλ/d), w being a steep weighting function between 0 and 1. Next, tacticle vehicle identification range predictions with the TOD TA model and TTP (Targeting Task Performance) model where compared to measured ranges with human observers. The TOD excellently predicts performance for both well-sampled and under-sampled sensors. While earlier TTP versions (2001, 2005) showed a pronounced difference in the relative weight of sampling and blur to range, the predictions with the newest (2008) TTP version that considers in-band aliasing are remarkably close to the TOD. In conclusion, the TOD methodology now provides a solid laboratory sensor performance test, a Monte Carlo simulation model to assess performance from sensor physics, a Target Acquisition range prediction model and a simple analytical expression to quickly predict sensor performance as a function of sampling and blur. TTP approaches TOD with respect to field performance prediction.

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“…This resembles the cone spacing in the fovea and determines the highest frequency that can be detected by the system. It is derived from the model by Geisler & Davila 10,11 , in which a densely packed triangular array of cones was assumed. (The Nyquist frequency of this model is 57 cycles/deg.…”

Section: Inputmentioning

confidence: 99%

“…The sample size is determined by the sample size of the vision model. We used a sample size of 0.153 mrad (visual angle), which resembles the spacing of the cones in the fovea 10,11 .…”

Section: General Model Descriptionmentioning

confidence: 99%

<title>Capturing the sampling effects: a TOD sensor performance model</title>

2001

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The standard way to characterize sensor performance is by means of the Minimum Resolvable Temperature Difference (MRTD) and Minimum Resolvable Contrast (MRC) methods. These methods are based on Fourier analysis and work reasonably well for linear (analogue) systems. However, nonlinear effects, such as sampling, are not properly accounted for. As an alternative, the Triangle Orientation Discrimination (TOD) method has been proposed, based on 4 oriented triangles, that can handle nonlinear effects.Here, we present a model that predicts the TOD-sensor performance characterization curve from the system parameters. It consists of i) a sensor model and ii) a model of the visual system of the observer. The sensor model generates display images which are fed into the visual system model. The visual system is modeled by a bank of band-pass filters which mimic the pattern of neural activity in the visual cortex (using a common stack model). The neural activity is calculated and internal noise is added. Finally, a decision is made based on a correlation with the expected neural activity of the 4 possible inputs.The model has been validated with two human observer experiments in which the TOD-curve 1) of the naked eye, and 2) of a simulated thermal staring sensor system were measured. An internal noise level could be found for which the TOD of the naked eye of the two observers could be predicted. The model gives reasonable (but somewhat optimistic) predictions of the TOD-sensor performance curve of the simulated staring camera. Although more tests and modifications are required, these preliminary results suggest that the model can be developed into a model which predicts the TOD for all kinds of sensor systems, which may include sampling effects, noise, blur and (local) image enhancement methods.

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<title>Automatic characterization of electro-optical sensors with image processing using the triangle orientation discrimination (TOD) method</title>

Cited by 15 publications

References 1 publication

Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Applicability of TOD, MTDP, MRT and DMRT for dynamic image enhancement techniques

Sensor performance as a function of sampling (d) and optical blur (Fλ)

<title>Capturing the sampling effects: a TOD sensor performance model</title>

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