Modeling the Histogram of the Halftone Image to Determine the Area Fraction of Ink Yat-Ming Wong May 8,1998 Advisor: Dr. Jonathan Arney.

Slides:



Advertisements
Similar presentations
Applications of one-class classification
Advertisements

Announcements •Homework due Tuesday. •Office hours Monday 1-2 instead of Wed. 2-3.
Robust statistical method for background extraction in image segmentation Doug Keen March 29, 2001.
Transforming images to images
ABSTRACT Title Authors Department/College Address This is a template for creating a poster to be printed on the 42” printer. If printed at 100% size, the.
Eyes for Relighting Extracting environment maps for use in integrating and relighting scenes (Noshino and Nayar)
Histograms Analysis of the Microstructure of Halftone Images J.S. Arney & Y.M. Wong Center for Imaging Science, RIT Given by Linh V. Tran ITN, Campus Norrköping,
Computer Vision Detecting the existence, pose and position of known objects within an image Michael Horne, Philip Sterne (Supervisor)
A Short Introduction to Curve Fitting and Regression by Brad Morantz
AOSC 634 Air Sampling and Analysis Lecture 1 Measurement Theory Performance Characteristics of instruments Nomenclature and static response Copyright Brock.
Uncertainty Representation. Gaussian Distribution variance Standard deviation.
Image Segmentation Region growing & Contour following Hyeun-gu Choi Advisor: Dr. Harvey Rhody Center for Imaging Science.
Thresholding Otsu’s Thresholding Method Threshold Detection Methods Optimal Thresholding Multi-Spectral Thresholding 6.2. Edge-based.
An Evaluation of the Current State of Digital Photography Charles Dickinson Advisor: Jeff Pelz.
Lappeenranta University of Technology (Finland)
1 Diffusion Distance for Histogram Comparison, CVPR06. Haibin Ling, Kazunori Okada Group Meeting Presented by Wyman 3/14/2006.
Highlights Lecture on the image part (10) Automatic Perception 16
1 Image filtering Images by Pawan SinhaPawan Sinha.
The Wavelength Dependence of the Yule-Nielsen Factor Joseph M. Janiak* and Dr. Jon Arney Rochester Institute of Technology.
Active Appearance Models Suppose we have a statistical appearance model –Trained from sets of examples How do we use it to interpret new images? Use an.
Automatic Camera Calibration for Image Sequences of a Football Match Flávio Szenberg (PUC-Rio) Paulo Cezar P. Carvalho (IMPA) Marcelo Gattass (PUC-Rio)
As with averages, researchers need to transform data into a form conducive to interpretation, comparisons, and statistical analysis measures of dispersion.
3D CT Image Data Visualize Whole lung tissues Using VTK 8 mm
Simple Linear Regression. Introduction In Chapters 17 to 19, we examine the relationship between interval variables via a mathematical equation. The motivation.
Pixels, PPI, DPI, and LPI for Scanning, Printing, and Web Publishing
Spectral contrast enhancement
Edge Detection Hao Huy Tran Computer Graphics and Image Processing CIS 581 – Fall 2002 Professor: Dr. Longin Jan Latecki.
1 Three dimensional mosaics with variable- sized tiles Visual Comput 2008 報告者 : 丁琨桓.
Overview of Graphic Communications
Graphical Analysis. Why Graph Data? Graphical methods Require very little training Easy to use Massive amounts of data can be presented more readily Can.
© The Catholic University of America Dept of Biomedical Engineering ENGR 104: Lecture 2 Statistical Analysis Using Matlab Lecturers: Dr. Binh Tran.
Mobile Robotics Laboratory Institute of Systems and Robotics ISR – Coimbra 3D Hand Trajectory Segmentation by Curvatures and Hand Orientation for Classification.
Applications The General Linear Model. Transformations.
Stat 155, Section 2, Last Time Numerical Summaries of Data: –Center: Mean, Medial –Spread: Range, Variance, S.D., IQR 5 Number Summary & Outlier Rule Transformation.
Chapter 6 The Standard Deviation as a Ruler and the Normal Model.
2.01D Investigate graphic image design. Image Resolution.
Chapter 21 Basic Statistics.
Copyright © 2009 Pearson Education, Inc. Chapter 6 The Standard Deviation as a Ruler and the Normal Model.
The Standard Deviation as a Ruler and the Normal Model
Video Segmentation Prepared By M. Alburbar Supervised By: Mr. Nael Abu Ras University of Palestine Interactive Multimedia Application Development.
Lecture 3 The Digital Image – Part I - Single Channel Data 12 September
Image Segmentation and Edge Detection Digital Image Processing Instructor: Dr. Cheng-Chien LiuCheng-Chien Liu Department of Earth Sciences National Cheng.
Bo QIN, Zongshun MA, Zhenghua FANG, Shengke WANG Computer-Aided Design and Computer Graphics, th IEEE International Conference on, p Presenter.
Non-Photorealistic Rendering and Content- Based Image Retrieval Yuan-Hao Lai Pacific Graphics (2003)
Computer Graphics and Image Processing (CIS-601).
23 November Md. Tanvir Al Amin (Presenter) Anupam Bhattacharjee Department of Computer Science and Engineering,
09/17/02 (C) 2002, University of Wisconsin, CS 559 Last Time Color Spaces File formats.
Slide 1 NATO UNCLASSIFIEDMeeting title – Location - Date Satellite Inter-calibration of MODIS and VIIRS sensors Preliminary results A. Alvarez, G. Pennucci,
Orientable Textures for Image- Based Pen-And-Ink Illustration Michael P. Salisbury Michael T. Wong John F. Hughes David A. Salesin SIGGRAPH 1997 Andrea.
02/05/2002 (C) University of Wisconsin 2002, CS 559 Last Time Color Quantization Mach Banding –Humans exaggerate sharp boundaries, but not fuzzy ones.
1 The Math of Printing & Imaging Copyright © Texas Education Agency, All rights reserved. Images and other multimedia content used with permission.
2.4 Measures of Variation Coach Bridges NOTES. What you should learn…. How to find the range of a data set How to find the range of a data set How to.
Chapter 11: The ANalysis Of Variance (ANOVA)
Descriptive Statistics for one Variable. Variables and measurements A variable is a characteristic of an individual or object in which the researcher.
Copyright © 2008 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 6 The Standard Deviation as a Ruler and the Normal Model.
Last lecture summary Types of statistics Measures of central tendency Measures of variability Bias, Bessel's correction MAD Normal distribution Empirical.
1 Collecting and Interpreting Quantitative Data Deborah K. van Alphen and Robert W. Lingard California State University, Northridge.
1 of 32 Computer Graphics Color. 2 of 32 Basics Of Color elements of color:
Capturing Size Effect in Unidirectional Fiber Composites Employing Stochastic Finite Element Simulations By Thomas Bilodeau Advisor: Dr. Fertig May 2,
OCR Reading.
- photometric aspects of image formation gray level images
Presented by :Yuting Bao
ECE 692 – Advanced Topics in Computer Vision
Analyzing Reliability and Validity in Outcomes Assessment Part 1
Regression Computer Print Out
Chapter 11: The ANalysis Of Variance (ANOVA)
Surreal Digital vs. Drawing
Common Core Math I Unit 1: One-Variable Statistics Boxplots, Interquartile Range, and Outliers; Choosing Appropriate Measures.
X-RAY COMPUTED TOMOGRAPHY FOR THE CHARACTERIZATON OF THE
Collecting and Interpreting Quantitative Data
Presentation transcript:

Modeling the Histogram of the Halftone Image to Determine the Area Fraction of Ink Yat-Ming Wong May 8,1998 Advisor: Dr. Jonathan Arney

Background n Drawing useful information from an image is important in various fields that depend upon them n Tools used to interpret an image need to be good enough to give meaningful data

Histogram n The histogram is a tool that gives a graphical interpretation of an image n It give us an idea of the make up of the image, such as the amount of ink in its composition

Histogram n The image is read pixel by pixel for their reflectance values R 1,9 = 0.1 R 7,10 = 0.9

Histogram

Histogram of halftone dots Ink Population Paper Population

Histogram n Segmentation of the histogram has so far been done by visual approximation n Visual approximation is a highly inaccurate method of measurement in cases where data needs to be in significant figures

Threshold Threshold, R T (?)

Solution Models to segment histogram computationally: Gaussian Model Straight-Edge Model

Gaussian Model Reflectance G1G1 G2G2 G 1 +G 2

Gaussian Model +

f(i) = F*G 1 (R) + (1-F)*G 2 (R) R1R1 R2R2 11 22 F 1-F REFLECTANCE G 1 +G 2

Sum of two gaussians vs. offset lithographic print data PROBLEM REFLECTANCE G 1 +G 2 Data

Sum of two gaussians vs. inkjet “stochastic halftone” data REFLECTANCE G 1 +G 2 Data PROBLEM

Straight Edge Model Halftone dots are a collection of edges

Straight Edge Model Model of the Halftone Reflection Distribution as a Single “Equivalent Edge” H R

Model the Halftone “Equivalent Edge Vary F H R

Model the Halftone “Equivalent Edge” H Change R min or R max R

Model the Halftone “Equivalent Edge” x scan R x where:

R x The Model H R 0 1

The Noise Model R S(R) Add A Noise Metric Assume A Reflectance Variation

H R 0 1 * S(R) The Noise Model R

Straight Edge Model RminRmax  F 1-F a

Straight edge model vs. offset lithographic print data H(R) R

Straight edge model vs. inkjet “stochastic halftone” data H(R) R

Comparison of models in matching offset lithographic print data Sum of two gaussiansStraight Edge vs.

Comparison of models in matching inkjet “stochastic halftone” data Sum of two gaussiansStraight Edge vs.

Automated computation n Program written in Visual Basic n Opens up a data file and automatically find the best computational match by looking for the set of variables that yields the lowest RMS deviation value.

Problems with the straight edge model H(R) R R Expand

Problems with the straight edge model H(R) R R Expand

Conclusion n Model fits well for R i and R p close to each other n For R i and R p widely spaced, a single noise metric is inadequate.

The End