We have started to collect a list of literature featuring research on spectral sensitivities of photoreceptors in a variety of organisms. Please let us know if you are aware of research that should be featured on this site. This list
Adding a Luminance Channel
The QCPA framework uses both chromatic (colour) and achromatic (luminance/brightness) differences to analyse images. The colour component is provided by “cone catch” images, where each receptor has its own channel, and these are compared in the receptor noise limited colour
The Receptor Noise Limited Model
In 1998 Vorobyev & Osorio published the influential paper on ‘Receptor noise as a determinant of colour thresholds’. In that paper the authors formally introduce the Receptor Noise Limited (RNL) model. The model states that, given a range of assumptions
The Receptor Noise Limited Colour Space
Perceptual contrast in the Receptor Noise Limited (RNL) model is calculated as the response of opponent processes which themselves are ‘fed’ by the stimulation of each receptor channel in relation to the channel specific noise (or Weber fraction in the
Cone Ratios & Receptor Noise
The noise in an individual photoreceptor cell (its firing rate despite lacking actual stimulation) in combination with the abundance of a given class of photoreceptors fundamentally determines an animal’s ability to discriminate colour and luminance contrast (Vorobyev & Osorio, 1998).
Running the QCPA Framework
The Quantitative Colour and Pattern Analysis (QCPA) framework is an image processing workflow which takes an “animal vision” cone-catch image, applies spatial acuity and viewing distance correction, and then performs a series of sophisticated colour and pattern analysis procedures. Historically
List of Animal Spatial Acuities
This page lists the spatial acuity values measured for a large range of species (currently over 60 species). If you are aware of any other species to be added please get in touch via the forum. The “cycles-per-degree” values listed
Gaussian Acuity Control
Acuity correction is an important step in modelling receiver vision, and in the QCPA framework, controlling for the receiver’s spatial acuity, and the viewing distance. This method uses a Gaussian convolution to eliminate spatial information from an image to simulate
Naive Bayes Clustering
The naive Bayes classifier is a simple but effective classification algorithm which can be used for image segmentation/clustering. We have created a custom naive Bayes classifier and integrated it into the QCPA framework. This method is suitable only when you
Particle Analysis
Particle analysis provides information on the spatial properties of clusters/patches in an image, such as the shape, size angle and distribution of particles within a given cluster/patch. This is typically run within the QCPA framework, but can be used in