Search Results for author:
Found 9 papers, 3 papers with code
Dense Extreme Inception Network for Edge Detection
It also generalizes well to other datasets without any fine-tuning.
Ranked #1 on
Edge Detection
on MDBD
Learning Representation in Colour Conversion
Our results show, with respect to the baseline network (whose input and output are RGB) 5-10% higher classification accuracy is obtained with decorrelating ColourConvNets.
Deep Neural Models for color discrimination and color constancy
High levels of color constancy were achieved with different DNN architectures.
The Utility of Decorrelating Colour Spaces in Vector Quantised Variational Autoencoders
We also observed that the learnt embedding space is easier to interpret in colour opponent models.
Paradox in Deep Neural Networks: Similar yet Different while Different yet Similar
Machine learning is advancing towards a data-science approach, implying a necessity to a line of investigation to divulge the knowledge learnt by deep neuronal networks.
Manifestation of Image Contrast in Deep Networks
Contrast is subject to dramatic changes across the visual field, depending on the source of light and scene configurations.
How is Contrast Encoded in Deep Neural Networks?
Contrast is a crucial factor in visual information processing.
Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism
Our experiments on three colour constancy benchmark datasets suggest that previous results can significantly improve by adopting a contrast-variant-pooling mechanism.
Colour Terms: a Categorisation Model Inspired by Visual Cortex Neurons
Although the neural substrate for this phenomenon is unknown, a recent study of cortical colour processing has discovered a set of neurons that are isoresponsive to stimuli in the shape of 3D-ellipsoidal surfaces in colour-opponent space.
