COLORLAB, is a color computation and visualization toolbox to be used in the MATLAB environment. COLORLAB is intended to deal with color in general-purpose quantitative colorimetric applications as color image processing and psychophysical experimentation. It includes classical Tristimulus representations (e.g. CIE XYZ 1931), current Color Appearance Models (CIE Lab 76 or CIECAM2000) and a variety of calibrated visualization and color processing tools.
CSFTools is a Matlab Toolbox with the spatial Contrast Sensitivity Functions of the ModelFest Standard Spatial Observer (Watson&Malo 2002). We include chromatic RG and YB CSFs from Mullen 85.
The Spatio-Temporal CSF both in terms of frequency (Kelly 79) and speed (Daly 00) can be found in the BasicVideoTools library.
DNTools is a Matlab Toolbox with a spatio-chromatic model of texture perception based on Orthonormal Wavelets and Divisive Normalization. This V1 cortex model was tuned to reproduce image quality opinions (see the wavelet version of VistaQualityTools) and found to perform a sort of non-linear ICA on natural images.
LinearNoisyTools is a Matlab Toolbox to model texture perception alternative to Divisive Normalization. Here the lower sensitivity for higher contrasts arises from considering Poisson noise in the V1 sensors. The toolbox includes achromatic CSF filtering, steerable wavelets with optimized architecture, and psychophysically measured Poisson noise. The selected transform architecture (receptive field shapes) is the one that simultaneously minimizes representation error and energy cost.
VirtualNeuroLabs is a series of computational tools for Matlab either to simulate physiological / psychophysical experiments or to illustrate the behavior of neural models when facing complex stimuli. These are appropriate learning-by-playing educative tools fot the Visual Neuroscience students at Masters or PhD levels. Specific exercises in VirtualNeuroLabs require the BasicVideoTools library.
The Human Camera could be seen as a new Brain Machine Interface for image transmission in which the nontrivial feature extraction and dimensionality reduction stage is done by a human brain instead of by the conventional compression algorithm. The input signal should be reconstructed from the neural signals received at the decoder. This toolbox shows different decoding algorithms assuming a simple model of V1 cortex.