![2
What is an image? - The bitmap
representation
Also called “raster or pixel maps”
representation
An image is broken up into a grid
pixel
Gray level
Original picture Digital image
f(x, y) I[i, j] or I[x, y]
x
y](https://image.slidesharecdn.com/lecture01intro-140720201203-phpapp01/85/Lecture01-intro-ece-2-320.jpg)
![28
// Test code to show how to read and write an image
#include "Image.h" // need to include the image library header
#include "Dip.h"
#include <iostream>
#include <cstdlib>
using namespace std;
#define Usage "./readwrite input-img output-img n"
int main(int argc, char **argv)
{
Image img1, img2;
int nr, nc, ntype, nchan, i, j, k;
if (argc < 3) {
cout << Usage;
exit(3);
}
img1 = readImage(argv[1]); // readImage is a member func in the Image lib
nr = img1.getRow(); // obtain the nr of rows and col
nc = img1.getCol();
ntype = img1.getType(); // obtain the type of the image
nchan = img1.getChannel(); // obtain the nr of channels of the image
img2.createImage(nr, nc, ntype); // write it to the output image
for (i=0; i<nr; i++) {
for (j=0; j<nc; j++) {
for (k=0; k<nchan; k++)
img2(i, j, k) = img1(i, j, k);
}
}
writeImage(img2, argv[2]);
return 0;](https://image.slidesharecdn.com/lecture01intro-140720201203-phpapp01/85/Lecture01-intro-ece-28-320.jpg)
Lecture01 intro ece
- 1. 1 ECE 472/572 - Digital Image Processing Lecture 1 - Introduction 08/18/11
- 2. 2 What is an image? - The bitmap representation Also called “raster or pixel maps” representation An image is broken up into a grid pixel Gray level Original picture Digital image f(x, y) I[i, j] or I[x, y] x y
- 3. 3 What is an image? - The vector representation Object-oriented representation Does not show information of individual pixel, but information of an object (circle, line, square, etc.) Circle(100, 20, 20) Line(xa1, ya1, xa2, ya2) Line(xb1, yb1, xb2, yb2) Line(xc1, yc1, xc2, yc2) Line(xd1, yd1, xd2, yd2)
- 4. 4 Comparison Bitmap – Can represent images with complex variations in colors, shades, shapes. – Larger image size – Fixed resolution – Easier to implement Vector – Can only represent simple line drawings (CAD), shapes, shadings, etc. – Efficient – Flexible – Difficult to implement
- 5. 5 How did it start? Early 1960s NASA’s Jet Propulsion Laboratory (JPL) Process video images from spacecraft (Ranger) IBM 360 Computer Images from H. Andrews and B. Hunt, Digital Image Restoration, Prentice-Hall, 1977.
- 6. 6 Why image processing? Application – Fingerprint retrieval – Automatic target recognition – Industrial inspection – Medical imaging – and more … Can commercial software do all the work?
- 7. 7 Histogram Equalization GLG in HSI space – better than Photoshop resultGLG in RGB space Photoshop “Auto Contrast” result1.42=APDG 1.12=TEN 4.28=APDG 0.22=TEN 9.72=APDG 8.20=TEN 3.76=APDG 0.27=TEN 6.78=APDG 6.26=TENGLG-RGB GLG-HSI Photoshop Original image of Mars and its moon From Zhiyu Chen’s preliminary proposal defense, January 2009
- 8. 8 Some clarification Image & Graphics Image processing & Computer vision Image processing & Image understanding Image processing & Pattern recognition – Image Processing: ECE472, ECE572 – Pattern Recognition: ECE471, ECE571 – Computer Vision: ECE573 – Computer Graphics: CS494, CS594
- 9. 9 Goals of image processing Image improvement – Improving the visual appearance of images to a human viewer Image analysis – Preparing images for measurement of the features and structures present
- 10. 10 What to learn? Image Acquisition Image Enhancement Image Restoration Image Compression Image Segmentation Representation & Description Recognition & Interpretation Knowledge Base Preprocessing – low level Image Improvement Image Coding Morphological Image Processing Wavelet Analysis High-level IP Image Analysis
- 11. 11 Image acquisition Video camera Infrared camera Range camera Line-scan camera Hyperspectral camera Omni-directional camera and more …
- 16. 16 Image correction Geometric correction Radiometric correction
- 17. Image warping – geometric transformation
- 18. 18 Image warping – another example From Joey Howell and Cory McKay, ECE472, Fall 2000
- 20. 20 Image description OCR – optical character recognition, license plate recognition
- 21. 21 Beyond Content-based image retrieval Human identification Multi-sensor data fusion Hexagonal pixel Steganography
- 22. 22 Image processing for fine arts
- 24. 24 How to address pixels of an image? int i, j, k; int nr, // number of rows nc, // number of columns nchan;// number of channels nr = 128; nc = 128; nchan = 3; for (i=0; i<nr; i++) { for (j=0; j<nc; j++) { for (k=0; k<nchan; j++) { do the processing on (i,j,k); ……… } } }
- 25. 25 j (i, j) (i, j+1)(i, j-1) (i-1, j) (i-1, j+1) (i+1, j+1)(i+1, j) (i-1, j-1) (i+1, j-1) i (row) (column) 4-neighborhood 8-neighborhood Types of neighborhoods Neighbors of a pixel
- 27. 27 The Image library /include: the header file – Image.h – Dip.h /lib: image processing routines – Image.cpp – colorProcessing.cpp – imageIO.cpp – matrixProcessing.cpp – cs.cpp – Makefile /test: the test code
- 28. 28 // Test code to show how to read and write an image #include "Image.h" // need to include the image library header #include "Dip.h" #include <iostream> #include <cstdlib> using namespace std; #define Usage "./readwrite input-img output-img n" int main(int argc, char **argv) { Image img1, img2; int nr, nc, ntype, nchan, i, j, k; if (argc < 3) { cout << Usage; exit(3); } img1 = readImage(argv[1]); // readImage is a member func in the Image lib nr = img1.getRow(); // obtain the nr of rows and col nc = img1.getCol(); ntype = img1.getType(); // obtain the type of the image nchan = img1.getChannel(); // obtain the nr of channels of the image img2.createImage(nr, nc, ntype); // write it to the output image for (i=0; i<nr; i++) { for (j=0; j<nc; j++) { for (k=0; k<nchan; k++) img2(i, j, k) = img1(i, j, k); } } writeImage(img2, argv[2]); return 0;
- 29. 29 The course website http://web.eecs.utk.edu/~qi/ece472-572 Course information Official language: C++ Pre-homework assignment – Subscribe to mailing list, dip@aicip.ece.utk.edu Grading policy: 72 late hour rule
- 30. 30 What to do? Subscribe to the mailing list – dip@aicip.ece.utk.edu Apply for an account in FH417 Get started on project 1 – Start early and finish early