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

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 that 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.

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 Imageprocessing

Better image/understanding

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?

Digital Image Pattern Recognition

Example:

Given an image, the computer tells what’s inside, or classify the objects in the image

Handwriting recognition

The relation between DIP and PR

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.

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, event 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.

DIP&PR relations to other fields

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)

Images obtained by analog communication channel

• 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 photonGamma rays, X-rays, Ultraviolet, Visible, Infrared, Microwaves, Radio waves

Radiation from EM spectrum

a bc d

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

Magnetic resonance imaging

Electron Microscope Images, up to 10,000x

Computer generated Images vs real images

Virtual LA (SGI)

Photo of l LA

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 :– Image analysis– Object recognition

DIP PR show

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

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 and reconstruction

3. Pattern Recognition– Image segmentation, representation and description– Feature extraction and feature selection– Classification– Clustering

How to make progress

• Grading– Assignments (40%)– Project (15%)– Final (40%)– Class participation and contribution (5%)

• Hardware/Software– Lab: N2085– Software: MATLAB

Teaching materials

• TextbookDigital Image Processing 3/e by Gonzalez and WoodsPattern Recognition, 4/e  by Sergios Theodoridis and Konstantinos Koutroumbas

• Course webpage: http://bohr.wlu.ca/hfan/cp467

• Classes– 23 Lectures cover theory of DIP & PR

• Office hours: 4:00 - 5:00 pm  or by appointment

• Q & A