Blackshot: An Unexpected Dimension of Human Sensitivity to Contrast Michael S. Landy New York University Charles Chubb University of California, Irvine.

Slides:



Advertisements
Similar presentations
Chapter 5: Space and Form Form & Pattern Perception: Humans are second to none in processing visual form and pattern information. Our ability to see patterns.
Advertisements

Image Processing Lecture 4
HISTOGRAM TRANSFORMATION IN IMAGE PROCESSING AND ITS APPLICATIONS Attila Kuba University of Szeged.
Image Processing IB Paper 8 – Part A Ognjen Arandjelović Ognjen Arandjelović
Chih-Hsing Lin, Jia-Shiuan Tsai, and Ching-Te Chiu
Image Enhancement To process an image so that the result is more suitable than the original image for a specific application. Spatial domain methods and.
Apparent Greyscale: A Simple and Fast Conversion to Perceptually Accurate Images and Video Kaleigh SmithPierre-Edouard Landes Joelle Thollot Karol Myszkowski.
May 2004SFS1 Shape from shading Surface brightness and Surface Orientation --> Reflectance map READING: Nalwa Chapter 5. BKP Horn, Chapter 10.
SAC’06 April 23-27, 2006, Dijon, France On the Use of Spectral Filtering for Privacy Preserving Data Mining Songtao Guo UNC Charlotte Xintao Wu UNC Charlotte.
Adaptation for Discrimination Light reflected = r * I Adaptation for Discrimination.
High Dynamic Range Imaging: Spatially Varying Pixel Exposures Shree K. Nayar, Tomoo Mitsunaga CPSC 643 Presentation # 2 Brien Flewelling March 4 th, 2009.
Display Issues Ed Angel Professor of Computer Science, Electrical and Computer Engineering, and Media Arts University of New Mexico.
Texture perception Lavanya Sharan February 23rd, 2011.
Perceived video quality measurement Muhammad Saqib Ilyas CS 584 Spring 2005.
VINCENT URIAS, CURTIS HASH Detection of Humans in Images Using Skin-tone Analysis and Face Detection.
A Full Frequency Masking Vocoder for Legal Eavesdropping Conversation Recording R. F. B. Sotero Filho, H. M. de Oliveira (qPGOM), R. Campello de Souza.
General Linear Model & Classical Inference
Use of Quantile Functions in Data Analysis. In general, Quantile Functions (sometimes referred to as Inverse Density Functions or Percent Point Functions)
Spectral contrast enhancement
Introduction to Image Processing Grass Sky Tree ? ? Review.
A VOICE ACTIVITY DETECTOR USING THE CHI-SQUARE TEST
ENDA MOLLOY, ELECTRONIC ENG. FINAL PRESENTATION, 31/03/09. Automated Image Analysis Techniques for Screening of Mammography Images.
Research Methods in Psychology
Reconfigurable Communication System Design
IDL GUI for Digital Halftoning Final Project for SIMG-726 Computing For Imaging Science Changmeng Liu
MODEGAT Chalmers University of Technology Use of Latent Variables in the Parameter Estimation Process Jonas Sjöblom Energy and Environment Chalmers.
Alignment and Matching
Introduction to Visible Watermarking IPR Course: TA Lecture 2002/12/18 NTU CSIE R105.
Computer Vision – Fundamentals of Human Vision Hanyang University Jong-Il Park.
Doc.: IEEE /0090r0 SubmissionMartin Jacob, TU Braunschweig January 2010 Slide 1 Modeling the Dynamical Human Blockage for 60 GHz WLAN Channel.
Human perception and recognition of metric changes of part-based dynamic novel objects Quoc C. Vuong, Johannes Schultz, & Lewis Chuang Max Planck Institute.
SOURCES OF ASYMMETRY IN PROCESSING OF DARK AND LIGHT STIMULI Qasim Zaidi, Stanley Jose Komban, & Jose-Manuel Alonso Graduate Center for Vision Research.
1 Introduction to Computer Graphics with WebGL Ed Angel Professor Emeritus of Computer Science Founding Director, Arts, Research, Technology and Science.
Automatic Minirhizotron Root Image Analysis Using Two-Dimensional Matched Filtering and Local Entropy Thresholding Presented by Guang Zeng.
03/05/03© 2003 University of Wisconsin Last Time Tone Reproduction If you don’t use perceptual info, some people call it contrast reduction.
Efficient computation of Robust Low-Rank Matrix Approximations in the Presence of Missing Data using the L 1 Norm Anders Eriksson and Anton van den Hengel.
ESTUARY WETLAND DETECTION IN SAR IMAGES Presented By Yu-Chang Tzeng.
Just Noticeable Difference Estimation For Images with Structural Uncertainty WU Jinjian Xidian University.
Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/10/15 “Empire of Light”, Magritte.
Power Aware Mobile Displays Ali Iranli Wonbok Lee Massoud Pedram July 26, 2006 Department of Electrical Engineering University of Southern California.
Autonomous Robots Vision © Manfred Huber 2014.
Digital Image Processing EEE415 Lecture 3
Relevance-Based Language Models Victor Lavrenko and W.Bruce Croft Department of Computer Science University of Massachusetts, Amherst, MA SIGIR 2001.
Speech Lab, ECE, State University of New York at Binghamton  Classification accuracies of neural network (left) and MXL (right) classifiers with various.
JOHN DOE PERIOD 8. AT LEAST 80% OF HUMANITY LIVES ON LESS THAN $10 A DAY.
© 2009 Robert Hecht-Nielsen. All rights reserved. 1 Andrew Smith University of California, San Diego Building a Visual Hierarchy.
Learning Photographic Global Tonal Adjustment with a Database of Input / Output Image Pairs.
03/03/03© 2003 University of Wisconsin Last Time Subsurface scattering models Sky models.
Non-Linear Transformations Michael J. Watts
Shape-Dependent Gloss Correction
Effects of Grayscale Window/Level on Breast Lesion Detectability Jeffrey Johnson, PhD a John Nafziger, PhD a Elizabeth Krupinski, PhD b Hans Roehrig, PhD.
PREWRITE: STEP 1: Graphic Organizer Describes who the person is Personality Characteristics.
1 Embedded Signal Processing Laboratory The University of Texas at Austin Austin, TX USA 1 Mr. Vishal Monga,
Neural Codes. Neuronal codes Spiking models: Hodgkin Huxley Model (brief repetition) Reduction of the HH-Model to two dimensions (general) FitzHugh-Nagumo.
Statistics and probability Dr. Khaled Ismael Almghari Phone No:
Journal of Vision. 2011;11(3):6. doi: / Figure Legend:
The general linear model and Statistical Parametric Mapping
Tone Dependent Color Error Diffusion
Content-based Image Retrieval
Tone Dependent Color Error Diffusion
CSC 381/481 Quarter: Fall 03/04 Daniela Stan Raicu
Phone Number BINGO!!!.
Minami Ito, Gerald Westheimer, Charles D Gilbert  Neuron 
creating your outline (due on the 5th of December, 2016)
The general linear model and Statistical Parametric Mapping
Volume 111, Issue 2, Pages (July 2016)
Integration Trumps Selection in Object Recognition
How to find the nth rule for a linear sequence
Surround integration and suppression in the direction domain.
Review and Importance CS 111.
Presentation transcript:

Blackshot: An Unexpected Dimension of Human Sensitivity to Contrast Michael S. Landy New York University Charles Chubb University of California, Irvine John Econopouly

Outline Back pocket model of texture segregation Investigating a nonlinearity: IID textures Filling in the missing information Result: Blackshot

Texture Segregation Luminance EdgeTexture Edge

The Back Pocket Model

Application of the Back Pocket Model InputVertically filtered Squared2 nd -Order filtered

The Back Pocket Model

How to Measure the Nonlinearity Simple case: Independent, Identically Distributed (IID) textures Appearance indicates at least two channels or nonlinearities: perceived brightness and contrast Technique: Histogram Contrast Analysis (Chubb, Econopouly & Landy, 1994) Result: a third perceptual dimension

IID Textures: The Uniform Histogram

IID Textures: 1 st -Order Modulator = Brightness

IID Textures: 2 nd -Order Modulator = Contrast

Histogram Modulators

N th -Order, Orthogonal Histogram Modulators Contrast Response

Histogram Contrast Thresholds

The Result (Chubb et al., 1994): f CEL f CEL : 3 rd - through 7 th -order components

The New Technique: Tradeoff of 1 or 2 with f CEL 1 or 2 Amplitude f CEL Amplitude or

Experimental Details 3 Subjects Carefully linearized lookup table 200 ms display

Task: 4-AFC Shape Discrimination

Results: Three Thresholds

Results: Reconstructed Nonlinearity

Conclusions Full measurement of a novel nonlinearity Blackshot: Exquisite sensitivity to the darkest texels Thus, 3 dimensions of IID texture appearance: brightness, contrast and blackshot A striking precedent: Whittle (1986)