Color Calibration of Scanners for Scanner‐Independent Grain Grading

Abstract

Cereal Chem. 80(3):285-289Scanner technology is emerging as a cost-effective and robust imaging alternative to camera-based systems in many applications. However, scanner technology is changing so fast that image quality can vary from model to model. It is critical that images scanned with different scanners be brought to a common basis for processing and measurement through a calibration process that eliminates scanner-to-scanner variability. The focus of this research was to investigate scanner-to-scanner variability and develop color correction or mapping functions to allow for machineindependent grain inspection. Various makes and models of scanners were compared for optical and color characteristics. Three different color correction methods were evaluated: grayscale (GS) transformation, redgreen-blue (RGB) transformation, and histogram matching. All three models of color correction worked within satisfactory tolerance for a multicolor Q60 chart. However, for grain samples of a limited color range, the histogram matching approach performed better than GS and RGB transformations for scanner calibration. The color-corrected test images matched the reference images within 3 grey values. Differences between the three models of color correction are discussed.

Characterization of the lentil landrace Santo Stefano di Sessanio from Abruzzo, Italy

“…Size, shape and colour components of seed images were analyzed by the image analysis software KS-400 V3.0 (Carl Zeiss, Vision, Oberkochem, Germany). Before image acquisition, the scanner was calibrated with a Q60 Kodak colour chart (scanned at 200 dpi) for colour matching, following the protocol of Shahin and Symons (2003b). For each seed set only intact seeds were used, singularly placed on the flatbed scanner in order to obtain a 512 9 512 pixel digital image at a resolution of 200 dpi.…”

Identification of Sicilian landraces and Canadian cultivars of lentil using an image analysis system

“…A robust, A convenient image analysis system was developed (Shahin & Symons, 2001a) for lentil colour classification using a flatbed scanner. The adaptability of the flatbed scanner was checked as a qualitative image analysis instrument to minimize colour variations (Shahin & Symons, 2000;Shahin & Symons, 2003b). The image characteristics that indicate the lentil colour grade were measured and the numerical results evaluated using statistical classifiers and neural networks (Shahin & Symons, 1999).…”

In-situ colour correction for digital images acquired under non-standard lighting conditions

“…As can be seen in some applications, measurement of different colour patches is important in making decisions in a large number of studies (O'Neal et al, 2002). Shahin and Symons (2003) developed an algorithm to adjust the colours recorded by digital scanners in order to establish uniform interpretation of colours in the event of using more than one scanner for scanning grains for research purposes. Chang and Reid (1996) presented a method for correcting for the variations in RGB values caused by vision system components used in acquiring the image.…”