Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Neural Networks - Special issue: automatic target recognition
A Variational Framework for Retinex
International Journal of Computer Vision
Properties of a Center/Surround Retinex: Part 2. Surround Design
Properties of a Center/Surround Retinex: Part 2. Surround Design
A new neural network for solving nonlinear projection equations
Neural Networks
Pushing it to the limit: adaptation with dynamically switching gain control
EURASIP Journal on Applied Signal Processing
Color-texture image segmentation and recognition through a biologically-inspired architecture
Pattern Recognition and Image Analysis
Hi-index | 0.00 |
This study develops a neuromorphic model of human lightness perception that is inspired by how the mammalian visual system is designed for this function. It is known that biological visual representations can adapt to a billion-fold change in luminance. How such a system determines absolute lightness under varying illumination conditions to generate a consistent interpretation of surface lightness remains an unsolved problem. Such a process, called 'anchoring' of lightness, has properties including articulation, insulation, configuration, and area effects. The model quantitatively simulates such psychophysical lightness data, as well as other data such as discounting the illuminant, and lightness constancy and contrast effects. The model retina embodies gain control at retinal photoreceptors, and spatial contrast adaptation at the negative feedback circuit between mechanisms that model the inner segment of photoreceptors and interacting horizontal cells. The model can thereby adjust its sensitivity to input intensities ranging from dim moonlight to dazzling sunlight. A new anchoring mechanism, called the Blurred-Highest-Luminance-As-White rule, helps simulate how surface lightness becomes sensitive to the spatial scale of objects in a scene. The model is also able to process natural color images under variable lighting conditions, and is compared with the popular RETINEX model.