Image statistics and the perception of surface qualities

By Isamu Motoyoshi, Shin'ya Nishida, Lavanya Sharan, Edward H. Adelson. Nature, 2007

600 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/6b08071d42bbf818affdc8ff2cc14f9eb944451b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Lavanya Sharan
2. Ruth Rosenholtz
3. Ce Liu
4. Guangtao Zhai
5. Ke Gu
6. Weisi Lin
7. Edward H. Adelson
8. Zhou Wang

In Cognitive Neuroscience

1. Wieland Brendel
2. Matthias Bethge
3. Peyman Milanfar
4. Lavanya Sharan
5. Ce Liu
6. Ruth Rosenholtz
7. Claudio Michaelis
8. Guangtao Zhai

In Computational Mechanics

1. Peyman Milanfar
2. Priyam Chatterjee
3. Edward H. Adelson
4. Ce Liu
5. Matthias Bethge
6. Wieland Brendel
7. Michael Elad
8. Philip Bachman

Citations in the corpus

• Recognizing Materials Using Perceptually Inspired Features (2013, methodology): “Nishida and Shinya (1998) and others (Motoyoshi et al. 2007; Sharan et al. 2008) have shown that image-based information like the shape of the luminance histogram is correlated with judgments of diffuse and specular reflectance.”
• Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, unknown): “is hypothesized that the ideal histogram of properly enhanced images is towards having uniform PDF, close to the original histogram, and of positively skewed statistics to elevate the surface quality [64]. Based on this hypothesis, an optimization function was established: ~h = minimize h kh h ik+ ”
• No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics (2015, methodology): “Inspired by the wide use of moment features in image contrast research [10], [11], [12], [13] and the entropy in image processing applications, we extract moment and entropy features from images, and build NSS models upon them using a large-scale image database.”
• Controlling low-level image properties: The SHINE toolbox (2010, background): “Exact histogram matching addresses the finding that certain neural mechanisms are sensitive to luminance histogram skewness (Olman, Boyaci, Fang, & Doerschner, 2008)—in particular, mechanisms involved in estimating surface properties ( Motoyoshi, Nishida, Sharan, & Adelson, 2007 ) or texture discrim”
• A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network (2007, unknown): “We are also trying to use other features having psychology proof that is useful for human to recognize things like the leaf, such as the surface qualities [30].”
• Is Denoising Dead? (2010, background): “Skewness is very good indicator of reflectance properties of surfaces such as albedo and gloss [47], [48].”
• One-shot Texture Segmentation (2018, background): “There is evidence that certain areas of the human brain are building texture representations to perform low-level visual inference [45, 27].”
• The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement (2016, background): “[35] provided an interesting surface perception”
• Visual perception and the statistical properties of natural scenes. (2008, unknown): “For example, Sharan et al. (2005) and Motoyoshi et al. (2007) show 186 Geisler A nn u.”