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Dr. Ghassabi z.r.ghassabi@gmail.com Tehran shomal University Spring 2015 z.r.ghassabi@gmail.com Digital Image Processing 1
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Course Link 2 cipcv.ir http://cipcv.ir/lessons/Digital-Image-Processing/Tehran-Shomal-University
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Grades Home work – 30% Exam- 40% Presentation and Participation- 30% If you write a good paper, your grade will become 20 3
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Homework TA: Ali Rahmani, Email: ali110rahmani@gmail.comali110rahmani@gmail.com Format (email(subject)& exercise(name)): – DIP-NAME-Family-Exe1 Deadline: 1 week Group works: More than 1 week 4
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Goals To make the graduate students acquainted with the fundamental concepts of Image Processing and Computer Vision 5
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Paper Presenting a paper – Research paper (2010,…,2014) Comparing results of several papers (an evaluation study) Writing a research paper 6 http://www.matlabsite.com/1127/mvpacw9210-choice-of-topic-and-thesis-writing.html http://www.matlabsite.com/1748/fvacw9304-practical-thesis-preparation-and-academic- paper-publication.html
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How To find a paper? –کنفرانسهای مهم پردازش تصویر و بینایی ماشین CVPR, ICIP, MICCAI,ICPR –سایتهای پردازش تصویر و بینایی ماشین دانشگاهها –مجلات مهم IEEE, Elsevier, springer http://cipcv.ir/related-magazine 7
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CVPR (Computer Vision and Pattern Recognition) http://www.pamitc.org/cvpr14/ 8
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CVPR (Computer Vision and Pattern Recognition) لیست مقالات سال گذشته به همراه پیاده سازی و دیتاست http://www.cvpapers.com/cvpr2013.html 9
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CVPR (Computer Vision and Pattern Recognition) لیست مقالات سال گذشته به همراه پیاده سازی و دیتاست http://www.cvpapers.com/cvpr2013.html 10
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How to find a paper? ICPR (International Conference on Pattern Recognition) – ICPR 2013, 2012, …2010 http://www.icpr2012.org/overview.html 11
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How to find a subject? Scope of subjects: – Face Recognition, Object Recognition, Document analysis, Biometric, remote sensing, Image registration, Image segmentation, etc.
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How to find a Subject? See Scope In “Call Papers” http://www.icpr2012.org/cfp.html 13
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Books – Main books: Digital Image Processing (3rd Edition), Rafael C. Gonzalez, Richard E. Woods. Digital Image Processing using Matlab – Other books: – Image processing toolbox 14
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Outline Introduction Digital Image Fundamentals Intensity Transformations and Spatial Filtering Filtering in the Frequency Domain Image Restoration and Reconstruction Color Image Processing Wavelets and Multi resolution Processing Image Compression Morphological Operation Object representation Object recognition 15
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Introduction An image may be defined as: A two-dimensional function, f(x, y) where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates f(x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities.
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Introduction 17 Digital picture produced in 1921 from a coded tape by a telegraph printer with special type faces. (McFarlane.†) Digital picture made in 1922 from a tape punched after the signals had crossed the Atlantic twice. Some errors are visible. (McFarlane.) Unretouched cable picture of Generals Pershing and Foch, transmitted in 1929 from London to New York by 15-tone equipment. (McFarlane.) The first picture of the moon by a U.S. spacecraft. Ranger 7 took this image on July 31, 1964 at 9 : 09 A.M. EDT, about 17minutes before impacting the lunar surface. (Courtesy of NASA.)
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Introduction
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19 Chest X-Ray X-Ray Imaging
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Introduction KneeSpine Head (MRI: Magnetic Resonance Image) Imaging in Radio Band
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Introduction
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X-rays CT Angiographie MRI UltraSound SPECT PET
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Introduction PET-CT
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Introduction Example – MR to CT Difference – Imaging technique – Patient position – Intensity pattern – Non-overlapping areas But – Same patient – No deformation of tissue
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Introduction Ref. Image: MRITarget Image: SPECTRegistered: MRI + SPECT
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Introduction MRA
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Introduction MRI as a Multi Channels imaging modalities: PD T 1 T 2
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Introduction MRI as a Multi Channels imaging modalities: PD weighted T2 weighted
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Introduction
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30 To Monitoring environmental Conditions on the Planet Nasa’s LandSat : Washington DC Imaging in the Visible and Infrared Bands (Remote Sensing)
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Introduction 31 Imaging in the Visible and Infrared Bands (Industry) A circuit Board : Inspect them for missing part Bottles : Look for bottle that are not filled up to an acceptable level Pill container: Look for missing pills
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Introduction Defective Cookies Non-Defective Cookies Imaging in the Visible and Infrared Bands (Industry)
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ساختن تصاویر Composite Introduction Unimodal Retinal Images
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Introduction Multimodal Retinal Images
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Introduction IRIS
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Introduction Remote Sensing
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Fundamental Steps in DIP
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Image Enhancement Poor Contrast ImageEnhanced Image Original ImageSharpened Image
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Image Enhancement Sharpening an image: (a) original and (b) sharpened. (a)(b)
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Image Restoration Distorted Image Restored Image Geometric Distorted ImageRestored Image
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Image Restoration (a) (b) An example of noise cleaning
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Image Restoration (c) (b)(a) Image restoration (a)Original clean image, (b) Camera motion-induced blurred Image (c) Deblurred Image.
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Image Compression Original image Compressed image(1/10) Compressed image(1/20) Compressed image(1/30)
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Image Engineering Image Understanding Image Analysis Image Processing Image Processing High Layer Middle Layer Low Layer Image Engineering Feature Measurement Object Representation Image Segmentation Data Out Image In
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Image Analysis قطعه بندی تصویر (Image Segmentation)
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Image Analysis Object Representation (descriptors)
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Representation Examples of descriptors
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Object recognition
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Fundamental Steps in DIP
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Image Engineering Image Understanding Image Analysis Image Processing Image Processing High Layer Middle Layer Low Layer Image Engineering Feature Measurement Object Representation Image Segmentation Data Out Image In
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OBJECTS ANIMALS INANIMATE PLANTS MAN-MADENATURAL VERTEBRATE ….. MAMMALS BIRDS GROUSEBOARTAPIR CAMERA Image Understanding
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Image Categorization Training Labels Training Images Classifier Training Training Image Features Testing Test Image Trained Classifier Outdoor Prediction
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Learning a classifier Given some set of features with corresponding labels, learn a function to predict the labels from the features xx x x x x x x o o o o o x2 x1
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Formulation: binary classification Formulation +1 x1x1 x2x2 x3x3 xNxN … … x N+1 x N+2 x N+M ??? … Training data: each image patch is labeled as containing the object or background Test data Features x = Labels y = Where belongs to some family of functions Classification function Minimize misclassification error (Not that simple: we need some guarantees that there will be generalization)
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Many classifiers to choose from SVM Neural networks Naïve Bayes Bayesian network Decision Trees K-nearest neighbor Etc. Which is the best one?
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Summary Framework Feature representation Feature Extraction Image preprocessing Feature Classification Enhancement Restoration, etc. Enhancement Restoration, etc. Segmentation (Color, shape, texture) Segmentation (Color, shape, texture) About six weeks About two weeks About one week
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Computer Vision Robotics Neuroscience Graphics Computational Photography Machine Learning Medical Imaging Human Computer Interaction Optics Image Processing Feature Matching Recognition
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