Introduction to Image Processing Lab 1 PDF

Introduction to Image Processing (23CSCI23H) Week 2: Introduction to Image Processing Lab 1 Teaching assistant: Nayera Youness o [email protected] o Office: H-007 (TAs room-ground...

Introduction to Image Processing (23CSCI23H) Week 2: Introduction to Image Processing Lab 1 Teaching assistant: Nayera Youness o [email protected] o Office: H-007 (TAs room-ground floor) Zeina Swilam o [email protected] o Office: H-007 (TAs room-ground floor) Module assessment: Coursework: 40% O Lab Test → 15% (week 6) O Assignment → 25% (week 10) Final unseen exam: 60% Labs content: Lab 1 (week 2): Introduction to Image Processing Lab 2 (week 3): Introduction to MATLAB & matrices Lab 3 (week 4): Image processing with MATLAB Lab 4 (week 5): Histogram Lab 5 (week 6): Bit plane & point transformation Lab 6 (week 7): Noises & Filters Lab 7 (week 8): Edge detection Lab 8 (week 9): Feature extraction (spatial domain) Lab 9 (week 10): Feature extraction (frequency domain) Lab 10 (week 11): image compression Module objectives: By the end of the semester, you should be: Familiar with the concepts of image processing Familiar with the MATLAB as a programming tool Familiar with the matrices’ mathematical operations Able to use the image processing MATLAB toolbox. Able to import, analyze a dataset and manipulate an image. Familiar with RGB and gray scale images 1  Introduction to Image Processing (23CSCI23H) Aware of the different types of noises and filters Able to choose the suitable type of filter according to the type of noise. Familiar with the histogram concepts Aware of the feature extraction in both spatial and frequency domains Familiar to the basics of image compression Lab 1: introduction to image processing 1. What is an image? 2. What is processing? 3. Now, what is image processing? 4. Why do we need to process an image? 5. How can we process an image? Image A digital image is a representation of a two-dimensional image as a finite set of digital values, called picture elements or pixels. Image processing  Used in: Checking for presence. Object detection and localization. Measurement. Identification and verification.  Steps: Importing the image with optical scanner or by digital photography. Analyzing and manipulating the image which includes data compression and image enhancement and spotting patterns that are not to human eyes like satellite photographs. Output is the last stage in which result can be transformed image or report that is based on image analysis  Purpose: Visualization - Observe the objects that are not visible (features). Image sharpening and restoration - To create a better image. Image retrieval - Seek for the image of interest. Measurement of pattern – Measure various objects in image. Image Recognition – Distinguish the objects in an image. 2  Introduction to Image Processing (23CSCI23H) N.B: images are not processed with a camera but preprocessed first.  Example for preprocessing: Noise reduction. Brightness and contrast enhancement. N.B: The better the preprocessing applied on an image, the better the result of the final image. Image types: Original image binary image histogram image edge image (Grey Scale Image)  Example: Correlation pattern matching looking for similar pieces by using pre-defined templates. Geometric pattern matching 3  Introduction to Image Processing (23CSCI23H) Applications for Identification Optical character recognition Bar Code 2D matrix code reading (OCR) o Automatic number plate recognition (anpr). 4  Introduction to Image Processing (23CSCI23H)  RGB The colors in real live different from the colors in the screen The screen consists of three different colors red, green, and blue. Overlapping those colors can produce any color  Grayscale image is what we going to deal with in this course 5  Introduction to Image Processing (23CSCI23H)  There are different methods can be used to convert image into grayscale.  The value of images in each pixel varies between 0 to 255. 6