Dr. Ofer Yaron Profile

Dr. Ofer Yaron

at Rafael Advanced Defense Systems Ltd

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Proceedings Article | 20 September 2020 Presentation + Paper

Range prediction for color imagers toward a joint TRM and human perceptual model

Ephi Pinsky, Ofer Yaron

Proceedings Volume 11538, 115380G (2020) https://doi.org/10.1117/12.2571247

KEYWORDS: Imaging systems, Performance modeling, Color difference, Color imaging, Reflectivity, Data modeling, Atmospheric modeling, Cameras, Image processing, Spatial frequencies

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An outline for an Extended Color Acquisition joint Model (ECAM) that combines the European TRM4/Visible- Reflectivity Category model with a human perception color difference metrics, enables the Detection, Recognition, Identification (DRI) range performance prediction of color imagers. The prediction of objects acquisition ranges is enabled by applying the TRM model for a color imaging system combined with human observer perceived difference technique. The new concept suggests objects' DRI ranges prediction model of a color image that are treated with two different successive approaches: A physical/hardware 'zone' that includes a colored 4-bar scheme that represents the target and background object, the atmosphere, an imaging color camera and a display in which TRM4 – reflective mode, is applied independently for each of the 3 primary colors. The computation steps in this section are very similar to TRM4-reflective mode described in the Fraunhofer IOSB Technical report 2016/09 -TRM4.v2. A human perception 'zone' where the eye/brain system is involved: the emerged photons from the display are absorbed and processed by a human observer and an image color difference metrics is applied. This work applies a technique for comparing original image and its reproduction to evaluate the difference between target and background represented by a two color standard 4-bar shape, observed at a given range, as captured and presented by a color camera with a color display. The S-SCIELAB metrics that reflects the spatial frequency response to different colors combined with CIEDE2000 difference technique are applied to calculate the perceived difference between a target and a background at each range.

Proceedings Article | 18 October 2016 Paper

Prediction of object detection, recognition, and identification [DRI] ranges at color scene images based on quantifying human color contrast perception

Ephi Pinsky, Ilia Levin, Ofer Yaron

Proceedings Volume 10004, 1000423 (2016) https://doi.org/10.1117/12.2240918

KEYWORDS: Color imaging, Imaging systems, Cameras, Spatial frequencies, Nonlinear response, Eye, Atmospheric modeling, Object recognition, Scattering, Thermography

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We propose a novel approach to predict, for specified color imaging system and for objects with known characteristics, their detection, recognition, identification (DRI) ranges in a colored dynamic scene, based on quantifying the human color contrast perception.

The method refers to the well established L*a*b*, 3D color space. The nonlinear relations of this space are intended to mimic the nonlinear response of the human eye. The metrics of L*a*b* color space is such that the Euclidian distance between any two colors in this space is approximately proportional to the color contrast as perceived by the human eye/brain. The result of this metrics leads to the outcome that color contrast of any two points is always greater (or equal) than their equivalent grey scale contrast. This meets our sense that looking on a colored image, contrast is superior to the gray scale contrast of the same image. Yet, color loss by scattering at very long ranges should be considered as well.

The color contrast derived from the distance between the colored object pixels and to the nearby colored background pixels, as derived from the L*a*b* color space metrics, is expressed in terms of gray scale contrast. This contrast replaces the original standard gray scale contrast component of that image. As expected, the resulted DRI ranges are, in most cases, larger than those predicted by the standard gray scale image. Upon further elaboration and validation of this method, it may be combined with the next versions of the well accepted TRM codes for DRI predictions.

Consistent prediction of DRI ranges implies a careful evaluation of the object and background color contrast reduction along the range. Clearly, additional processing for reconstructing the objects and background true colors and hence the color contrast along the range, will further increase the DRI ranges.

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