The ICE Tool Feng Wen Qi Yuan Kin Wah Leung. Presentation Overview  Project goal  Interactive GUI  Introduce image enhancement techniques  Integration.

Slides:



Advertisements
Similar presentations
Linear Filtering – Part I Selim Aksoy Department of Computer Engineering Bilkent University
Advertisements

Image Processing Lecture 4
Image Filtering. Outline Outline Concept of image filter  Focus on spatial image filter Various types of image filter  Smoothing, noise reductions 
Image Enhancement in the Frequency Domain Part III
EE 4780 Image Enhancement. Bahadir K. Gunturk2 Image Enhancement The objective of image enhancement is to process an image so that the result is more.
1Ellen L. Walker ImageJ Java image processing tool from NIH Reads / writes a large variety of images Many image processing operations.
Image Enhancement Digital Image Processing Instructor: Dr. Cheng-Chien LiuCheng-Chien Liu Department of Earth Sciences National Cheng Kung University Last.
Digital Image Processing In The Name Of God Digital Image Processing Lecture3: Image enhancement M. Ghelich Oghli By: M. Ghelich Oghli
A graphical user interface (GUI) is a pictorial interface to a program. A good GUI can make programs easier to use by providing them with a consistent.
1 © 2010 Cengage Learning Engineering. All Rights Reserved. 1 Introduction to Digital Image Processing with MATLAB ® Asia Edition McAndrew ‧ Wang ‧ Tseng.
6/9/2015Digital Image Processing1. 2 Example Histogram.
2007Theo Schouten1 Enhancements Techniques for editing an image such that it is more suitable for a specific application than the original image. Spatial.
Content-Based Image Retrieval (CBIR) Student: Mihaela David Professor: Michael Eckmann Most of the database images in this presentation are from the Annotated.
Intro to ArcMap Customization with Visual Basic  Create your own toolbars, buttons, interactive tools, and programs  Runs behind the scenes in ArcMap.
1 Vladimir Botchko Lecture 4. Image Enhancement Lappeenranta University of Technology (Finland)
DIP Realized by IDL Author: Ying Li Course: computer for imaging science.
Median Image Filter David Newman Nick Govier. Overview Purpose of Filter Implementation Demo Questions ??
DrawingPaint What is DrawingPaint ? DrawingPaint operations. What are benefits of DrawingPaint ? Jason Hoang June 2, 2005.
Image Enhancement.
About the Presentations The presentations cover the objectives found in the opening of each chapter. All chapter objectives are listed in the beginning.
Image Filtering. Problem! Noise is a problem, even in images! Gaussian NoiseSalt and Pepper Noise.
Working with Graphics. Objectives Understand bitmap and vector graphics Place a graphic into a frame Work with the content indicator Transform frame contents.
Chapter 3 (cont).  In this section several basic concepts are introduced underlying the use of spatial filters for image processing.  Mainly spatial.
.PDF = Portable Document Format  Providing a common interface for opening documents sourced in a wide range of applications  Collaborating with others.
Chapter 2 Build Your First Project A Step-by-Step Approach 2 Exploring Microsoft Visual Basic 6.0 Copyright © 1999 Prentice-Hall, Inc. By Carlotta Eaton.
1 Integrated Development Environment Building Your First Project (A Step-By-Step Approach)
Chapter 3 Working with Symbols and Interactivity.
Presentation Image Filters
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
1 Chapter 8: Image Restoration 8.1 Introduction Image restoration concerns the removal or reduction of degradations that have occurred during the acquisition.
NOTE: To change the image on this slide, select the picture and delete it. Then click the Pictures icon in the placeholder to insert your own image. WEB.
IE 411/511: Visual Programming for Industrial Applications
Medical Image Analysis Image Enhancement Figures come from the textbook: Medical Image Analysis, by Atam P. Dhawan, IEEE Press, 2003.
© 2011 Delmar, Cengage Learning Chapter 3 Working with Symbols and Interactivity.
Working with Symbols and Interactivity
Lesson 1 What is Camtasia?. Lesson 2 Editing Objectives After completing the lesson, the student will be able to: Edit a basic recording Camtasia file.
FotoGazmic Software (From left to right: Chad Zbinden, Josey Baker, Rob Mills, Myra Bergman, Tinate Dejtiranukul)
Under Supervision of Dr. Kamel A. Arram Eng. Lamiaa Said Wed
Digital Image Processing Lecture 5: Neighborhood Processing: Spatial Filtering Prof. Charlene Tsai.
Guide to Programming with Python Chapter One Getting Started: The Game Over Program.
Image Processing is replacing Original Pixels by new Pixels using a Transform rst uvw xyz Origin x y Image f (x, y) e processed = v *e + r *a + s *b +
School of Computer Science Queen’s University Belfast Practical TULIP lecture next Tues 12th Feb. Wed 13th Feb 11-1 am. Thurs 14th Feb am. Practical.
Applets Yong Choi School of Business CSU, Bakersfield.
Digital Image Processing (Digitaalinen kuvankäsittely) Exercise 2
Image Processing Part II. 2 Classes of Digital Filters global filters transform each pixel uniformly according to the function regardless of its location.
Lecture 5 Mask/Filter Transformation 1.The concept of mask/filters 2.Mathematical model of filtering Correlation, convolution 3.Smoother filters 4.Filter.
Machine Vision ENT 273 Image Filters Hema C.R. Lecture 5.
Image Restoration Fasih ur Rehman. –Goal of restoration: improve image quality –Is an objective process compared to image enhancement –Restoration attempts.
8-1 Chapter 8: Image Restoration Image enhancement: Overlook degradation processes, deal with images intuitively Image restoration: Known degradation processes;
Image Subtraction Mask mode radiography h(x,y) is the mask.
Logarithmic Image Processing (LIP) By Ben Weisenbeck Oiki Wong.
Intelligent Vision Systems ENT 496 Image Filtering and Enhancement Hema C.R. Lecture 4.
Lecture # 19 Image Processing II. 2 Classes of Digital Filters Global filters transform each pixel uniformly according to the function regardless of.
Sejong Univ. CH3. Area Processes Convolutions Blurring Sharpening Averaging vs. Median Filtering.
NET 222: COMMUNICATIONS AND NETWORKS FUNDAMENTALS ( NET 222: COMMUNICATIONS AND NETWORKS FUNDAMENTALS (PRACTICAL PART) Tutorial 2 : Matlab - Getting Started.
Chapter – 8 Software Tools.
EE 7730 Image Enhancement. Bahadir K. Gunturk2 Image Enhancement The objective of image enhancement is to process an image so that the result is more.
Project Information Abstract Project Objectives The objective of this project is to: Create a visual designer that will allow inexperienced end- users.
IE 411/511: Visual Programming for Industrial Applications Lecture Notes #2 Introduction to the Visual Basic Express 2010 Integrated Development Environment.
Visual Basic.NET Comprehensive Concepts and Techniques Chapter 2 The Visual Basic.NET Integrated Development Environment.
 2002 Prentice Hall. All rights reserved. 1 Introduction to the Visual Studio.NET IDE Outline Introduction Visual Studio.NET Integrated Development Environment.
CMPT 275 TEAM DIRECTORIES. One Sentence Summary The Study Buddy is: a tool to help users study to improve their grades by simulating a multiple choice.
Image Subtraction Mask mode radiography h(x,y) is the mask.
Chapter 1 Introduction to Computers, Programs, and Java
Image Pre-Processing in the Spatial and Frequent Domain
The Chinese University of Hong Kong
Chapter 8, Exploring the Digital Domain
Department of Computer Engineering
Image Enhancement in the Spatial Domain
Presentation transcript:

The ICE Tool Feng Wen Qi Yuan Kin Wah Leung

Presentation Overview  Project goal  Interactive GUI  Introduce image enhancement techniques  Integration with Matlab™  Implementation of image enhancement techniques  Potential advancement of ICE tool

ICE Tool What is ICE tool?  ICE = Image Contrast Enhancement  Capable of executing various image enhancement techniques  Provides easy to use interface  Can be altered according to desire needs if necessary

Project Goal  To implement an interactive GUI capable of enhancing images  Research image enhancement techniques  Programming an interactive GUI  Integrating with Matlab™ libraries  Implementing image enhancement functions  make sure functions performed correctly

Interactive GUI  The Interactive GUI (graphic user interface)  As user friendly as possible  Created using Java™ - A programming language from Sun Microsystems - Provides great system portability  Created as a Java™ frame application  GUI features  Ability to load and save desired images  Displays original and modified image on the same panel  Easy menu bar browsing

GUI Features  Ability to load and save image  Ability to display both original and modified image on same screen

GUI Features (cont…)  Easy toolbar browsing  Combines simple image enhancement methods

Image Enhancement Techniques  Contrast Enhancement  Histogram equalization - Image quality can be improved by altering histogram - Calculates the ideal transformation from the histogram of the image - All gray levels used has a tendency to enhance image contrast Transformation Function: T(f ) can be calculated from the following relation:

Image Enhancement Techniques (cont…)  Noise Removal Filter – removes dots or speckles on image (equivalent of low-pass filtering)  Average Filter (Mean) - Replace each pixel by the average of the window area pixels - Has the effect of smoothing image - Larger window size removes noise more effectively while - At the expense of blurring the details  Median Filter - Replace each pixel by the median of the window area pixels - More effective against impulse noise (aka salt and pepper) - Can retain details and edges better than averaging filter

Image Enhancement Techniques (cont…)  Deblurring  Wiener Deblurring - Generalized inverse filter - Effective when information regarding frequency characteristics are known, at least to a degree  Lucy-Richardson - Effective when the PSF (point-spread function) is know but little information is available for the noise  Sharpening  Enhances details and edges  Line structures can be obtain by applying high-pass filter

Integration With Matlab™  Benefits  Allows the access of the large Matlab™ function library - The Matlab™ math function library - The Matlab™ image processing function library  Integration process  Use of an software engine to link Matlab ™ and Java ™ GUI together  Implement the functions to the appropriate buttons

Integration With Matlab™  Incorporate with JMatLink  A Java engine capable of linking Java ™ applications and Matlab ™ - Use of native methods, no source code need to be changed - Created by Stefan Muller  Edit autoexec.bat to set path to Matlab ™ and Java ™

 Research Matlab ™ code  Must know the codes for executing all of the image enhancement techniques  Ex: for histogram equalization I = imread(‘abc.jpg’); J = histeq( I ); Image Enhancement Implementation  Implement the code to the Java ™ interface buttons  Every component assigned the appropriate Matlab ™ code  Press of buttons send Java ™ code to Matlab ™ for execution

Image Enhancement Implementation (cont…)

Summary  Successfully creating an functional interactive GUI using Java  Java was integrated with Matlab™ through JMatLink  The Matlab™ code was associated with every button in GUI  Additional features and improvements can be made

Future Advancement of ICE Tool  Try to make it a standalone application without Matlab™  Addition of more image enhancement techniques  Addition of more features such as help documentation, zoom, etc  Package into an easy to install application