CP467 Image Processing and Pattern Recognition

Transcription

1 CP467 Image Processing and Pattern Recognition Instructor: Hongbing Fan Introduction About DIP & PR About this course Lecture 1: an overview of DIP DIP&PR show

2 What is Digital Image? We use digital image processing in daily life we might not even notice it Image processing functions are build in most cell phone and digital cameras, computers. Image and digital image The term image has a wide range of meanings, sound, photos, etc. The mean we concern is a picture or artefact t that t appears in a 2- demensional layout media and that represents something, or intensity or grey level f(x, y) at point (x, y), where x and y are spatial coordinates A digital image is a representation of image by binary values for each pixel of the image.

3 What is Digital Image Processing Digital image processing is the use of computer and computer algorithms to perform image processing on digital images to improve image quality for human perception and/or computer interpretation. Image Image Better processing image/understanding g

4 What is Pattern Recognition Reading involves the character recognition Learning pattern and recognition Write the next two terms of the sequence: 17, 33, 49, 65, 81,, Pattern recognition is concerned primarily with the description and classification of measurements taken from physical or mental processes How to make computer to recognize patterns?

5 Digital Image Pattern Recognition Example: Given an image, the computer tells what s inside, or classify the objects in the image

6 Handwriting recognition

7 The relation between DIP and PR

8 DIP vs Digital Signal Processing (DSP) DIP is a subclass of digital signal processing concerned specifically with pictures DIP does the processing using computers DSP also deals with other types signals such as acoustic signal DSP processes signals by either computers or some special hardware devices. More in the domain of electronic computer engineering.

9 DIP&PR and other fields Computer Graphics (CG) CG focus on the creation of digital images by modeling and rendering of 2D/3D objects DIP&PR focuses on enhancing given images and further recognizing the objects in the image Computer Vision (CV) CV deals with the analysis of image content scene reconstruction, ction event ent detection, tracking, object recognition, learning, indexing, ego-motion and image restoration. Artificial Intelligent (AI) A significant part of AI deals with autonomous planning or deliberation for systems which can perform mechanical actions such as moving a robot through some environment. That uses DIP, CV and PR.

10 DIP&PR relations to other fields

11 Applications of DIP & PR Why DIP&PR Improvement of pictorial information for human interpretation Processing of image data for storage, transmission, and representation for autonomous machine perception Wide range of image source for DIP&PR Radiation from the Electromagnetic spectrum Acoustic Ultrasonic Electronic (in the form of electron beams used in electron microscopy) Computer (synthetic images used for modeling and visualization)

12 Images obtained by analog communication channel

13

14 Radiation from EM spectrum EM waves = a stream of massless (proton) particles, each traveling in a wavelike pattern and moving at the speed of light. Spectral bands are grouped by energy per photon Gamma rays, X-rays, Ultraviolet, Visible, Infrared, Microwaves, Radio waves

15 a b c d Nuclear Image (a) Bone scan (b) PET (Positron emission tomography) image Astronomical Observations. (c) Cygnus Loop Nuclear Reaction (d) Gamma radiation from a reactor valve

16

17

18

19

20

21

22

23

24

25 Magnetic resonance imaging

26

27

28

29 Electron Microscope Images, up to 10,000x

30

31 Computer generated Images vs real images Virtual LA (SGI) Photo of l LA

32 Three levels of DIP&PR Low-level : input, output are images Primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening Photo editing and manipulation software Mid-level : inputs may be images, outputs are attributes extracted from those images Segmentation Description of objects Classification of individual objects High-level l : Image analysis Object recognition DIP PR show

33 Objectives of this course To learn and practice principles, methods and algorithms in DIP and PR, for solving problems in computer vision, image processing and pattern recognition To gain the fundamental skills for developing DIP and PR related hardware and software

34 What to be covered 1. Digital images fundamentals Image acquisition, sampling, and digitization Image representation, compressing, and storage 2. Image Enhancement Intensity transformations and spatial filtering Discrete Fourier transformation to frequency domain Wavelet transformation Image restoration ti and reconstruction ti 3. Pattern Recognition Image segmentation, representation and description Feature extraction and feature selection Classification Clustering

35 How to make progress Grading Assignments (40%) Project (15%) Final (40%) Class participation and contribution (5%) Hardware/Software Lab: N2085 Software: MATLAB

36 Teaching materials Textbook Digital Image Processing 3/e by Gonzalez and Woods Pattern Recognition, 4/e by Sergios Theodoridis and Konstantinos Koutroumbas Course webpage: Classes 23 Lectures cover theory of DIP & PR Office hours: 4:00-5:00 pm or by appointment Q & A