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Contrast Sensitivity

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Letter identification and the neural image classifier.

Journal of vision
Letter identification is an important visual task for both practical and theoretical reasons. To extend and test existing models, we have reviewed published data for contrast sensitivity for letter identification as a function of size and have also c...

Modeling second-order boundary perception: A machine learning approach.

PLoS computational biology
Visual pattern detection and discrimination are essential first steps for scene analysis. Numerous human psychophysical studies have modeled visual pattern detection and discrimination by estimating linear templates for classifying noisy stimuli defi...

The quantization error in a Self-Organizing Map as a contrast and colour specific indicator of single-pixel change in large random patterns.

Neural networks : the official journal of the International Neural Network Society
The quantization error in a fixed-size Self-Organizing Map (SOM) with unsupervised winner-take-all learning has previously been used successfully to detect, in minimal computation time, highly meaningful changes across images in medical time series a...

Deciphering image contrast in object classification deep networks.

Vision research
The ultimate goal of neuroscience is to explain how complex behaviour arises from neuronal activity. A comparable level of complexity also emerges in deep neural networks (DNNs) while exhibiting human-level performance in demanding visual tasks. Unli...

Deep neural networks capture texture sensitivity in V2.

Journal of vision
Deep convolutional neural networks (CNNs) trained on visual objects have shown intriguing ability to predict some response properties of visual cortical neurons. However, the factors (e.g., if the model is trained or not, receptive field size) and co...

A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects.

IEEE transactions on cybernetics
Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biological visual neural networks, which have evolved over millions of years in nature and are working perfe...

Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning.

Investigative ophthalmology & visual science
PURPOSE: Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal...

Contrast sensitivity functions in autoencoders.

Journal of vision
Three decades ago, Atick et al. suggested that human frequency sensitivity may emerge from the enhancement required for a more efficient analysis of retinal images. Here we reassess the relevance of low-level vision tasks in the explanation of the co...

Distinguishing shadows from surface boundaries using local achromatic cues.

PLoS computational biology
In order to accurately parse the visual scene into distinct surfaces, it is essential to determine whether a local luminance edge is caused by a boundary between two surfaces or a shadow cast across a single surface. Previous studies have demonstrate...

A Novel Artificial Intelligence-Based Classification of Highly Myopic Eyes Based on Visual Function and Fundus Features.

Translational vision science & technology
PURPOSE: To develop a novel classification of highly myopic eyes using artificial intelligence (AI) and investigate its relationship with contrast sensitivity function (CSF) and fundus features.