CS/ECE 545 (Digital Image Processing) Midterm Review
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1 CS/ECE 545 (Digital Image Processing) Midterm Review Prof Emmanuel Agu Computer Science Dept. Worcester Polytechnic Institute (WPI)
2 Exam Overview Wednesday, March 5, 2014 in class Will cover up to lecture 5 (Harris Corner Detection) Includes today s class Can bring: One page cheat sheet, hand written (not typed) Calculator Will test: Theoretical concepts Mathematics Algorithms Programming ImageJ knowledge (program structure and some commands)
3 What am I Really Testing? Understanding of concepts (NOT only programming) programming (pseudocode/syntax) Test that: you can plug in numbers by hand to check your programs you did the projects you understand what you did in projects
4 General Advise Read your projects and refresh memory of what you did Read the slides: worst case if you understand slides, you re more than 50% prepared Focus on Mathematical results, concepts, algorithms Plug numbers: calculate by hand Try to predict subtle changes to algorithm.. What ifs?.. Past exams: One sample midterm is on website All lectures have references. Look at refs to focus reading Do all readings I asked you to do on your own
5 Grading Policy I try to give as much partial credit as possible In time constraints, laying out outline of solution gets you healthy chunk of points Try to write something for each question Many questions will be easy, exponentially harder to score higher in exam
6 Introduction to Image Processing What is an Image? Imaging system (parts) Digital image: an approximation What is image processing? Examples image processing operations: know what each type of operation does Noise removal, contrast adjustment, segmentation, edge detection, image compression, etc Applications of image processing Face recognition, fingerprinting, law enforcement, etc
7 Introduction to Image Processing Relationships with other fields (computer vision, image analysis) The key stages in image processing: know the stages and what each stage does Light, the electromagnetic spectrum & Image processing Structure of the human eye (rods, cones, fovea, etc) Image formation (in the eye & pinhole camera) Brightness adaptation and discrimination
8 Introduction to Image Processing Image acquisition Spatial sampling Image quantization Image as a discrete function Representing images Image resolutions: spatial resolution vs intensity level resolution Saturation & noise Image File formats
9 ImageJ ImageJ parts Key features Interactive tools, plugin mechanism, macro language + interpreter Software architecture Writing plugins
10 Histograms What is a histogram? Uses, interpretation of histograms Image issues easily identified using histogram Histograms: image brightness, contrast and dynamic range Computing histograms and binning
11 Histograms Color histograms Cumulative histograms What is a point operation? Point operations Clamping, inverting images, thresholding, etc Gray level transformations Intensity windowing Contrast adjustment Histogram equalization
12 Operations on Histograms Histogram specification Histogram matching Gamma correction Alpha blending
13 Image Enhancement & Filters What is image enhancement? What is a filter Spatial filtering Smoothing using averaging filters Weighted smoothing filters Dealing with out of range image coordinates Crop, pad, extend, wrap Linear filters vs non linear filters
14 Filters Linear smoothing, gaussian filters Difference filters Convolution Properties, separability, etc Noise What is noise Noise types: speckle noise, salt and pepper noise, etc Best filter types to clean types of noise
15 Filters, Edge Detection Non linear filters: min, max, median, weighted median filters Outlier method for cleaning noise Edge detection What is an edge, characteristics Edge operators Gradient based edge detection Prewitt, Sobel, Roberts, Compass edge detection filters
16 Edge Detection Edge detection using 2 nd derivatives Canny edge detection Contours and edge maps Image sharpening Edge sharpening using Laplace operator Edge sharpening using unsharp masking Harris corner detection
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