Negative Values in Cone Catch Images

When creating a cone-catch image it is fairly common to have some negative values, which are highlighted in red overlay (plus the log lists the frequency of negative values in each image). Negative values can be caused by two main

Spectral Sensitivity-Based Cone Catch Model

This is the standard old method (see Troscianko & Stevens 2015). It requires knowing the spectral sensitivities of the camera, which in practice are extremely difficult to measure. You will need a source of monochromated light (e.g. monochromator, or bandpass

Chart-Based Cone-Catch Model

Using a standard colour chart it is possible to create a cone-catch model. This method requires: A diffuse colour chart with precisely known spectral reflectance curves (the toolbox provides these curves for an X-Rite colorChecker passport). A .csv file containing

Linearisation Modelling

We recommend using RAW photographs wherever possible because they preserve a linear relationship between the radiance of light hitting the sensor and the pixel values. However, in many circumstances it is desirable to use non-RAW images for objective image analysis.

Scale Bar

Pattern analysis and acuity control require all images be scaled to a uniform number of pixels per unit length – a number that will vary with every study. Photographing different samples from slightly different distances can be controlled for with

Creating Cone-Catch Models

If your hypotheses depend on the appearance of the object to a specific visual system/model species, or absolute measures of colour are required then cone-catch images are recommended. This conversion produces images based on the spectral sensitivities of a given

Creating a calibrated mspec image

When making objective measurements from any digital image it is critical to calibrate the image to control for various sources of error. This is one of the core functions of the micaToolbox. Typical digital images are non-linear, meaning pixel values