Lecturer:
Conf. dr. ing. Mihaela GORDAN
Communications Department
e-mail: Mihaela.Gordan@com.utcluj.ro
Office phone: 0264-401309
Office address: Multimedia (CTMED)
laboratory, Str. C. Daicoviciu Nr. 15
Digital Image Processing
Lecture notes – fall 2008
Digital Image Processing
Lecture 1 – Introductory
Lecture 1
• Introduction
• Course description
• Exam grade information
Digital Image Processing
Lecture 1 – Introductory
Introduction (1)
Digital image processing:
• deals with digital images = digital representation of the
visual scenes
• Note that: visual perception can be static (scene content unchanged
in time) or dynamic (scene content changes in time); the latest case =
video sequence;
• Typically, visual scene = a static image, a “snap shot”
• tries to:
• “implement” in digital (algorithmic) form various human vision
processes => image analysis & understanding, pattern recognition
• “improve” image appearance for human visualization => image
enhancement, de-noising;  BASIC IMAGE PROCESSING
• store and transmit images efficiently => image compression
Digital Image Processing
Lecture 1 – Introductory
Introduction (2)
Applications of digital image processing?
… virtually, everywhere!
• Industry: inspection/sorting; manufacturing (robot vision)
• Environment: strategic surveillance (hydro-dams, forests, forest fires,
mine galleries) by surveillance cameras, autonomous robots
• Medicine: medical imaging (ultrasound, MRI, CT, visible)
• Culture: digital libraries; cultural heritage preservation (storage,
restoration, analysis – indexing)
• Television: broadcasting, video editing, efficient storage
• Education & tourism: multi-modal, intelligent human-computer
interfaces, with emotion recognition components
• Security/authentication (iris recognition, signature verification)
… etc…
Digital Image Processing
Lecture 1 – Introductory
Introduction (3)
• Industrial inspection
(industrial vision systems):
Digital Image Processing
Lecture 1 – Introductory
Introduction (4)
Water sources inspection:
• Environment surveillance/monitoring:
Forest fire monitoring
Hydro sites surveillance
Digital Image Processing
Lecture 1 – Introductory
Introduction (5)
• Medical imaging applications:
Color image segmentation &
Cells counting
Ultrasound image analysis/quantification
Digital Image Processing
Lecture 1 – Introductory
Course description (1)
… Obviously, digital image processing is a very wide field,
sooo…
…What will we study in 1 semester…?
• Just
the basics you need to develop & implement image
processing & analysis algorithms from all the categories above!
• Simplification:
- only grey level images
- only basic processing methods,
without their combination
Digital Image Processing
Lecture 1 – Introductory
Course description (2)
•
I.
Course chapters:
Grey level digital image representation. Basic math concepts for
digital image processing algorithms
II. Grey level image digitization:
II. 1. Image sampling
II. 2. Image quantization
III. Image transforms: digital image representation in frequency
domains; applications: noise filtering, compression, recognition
III. 1. Basic properties
III. 2. Sinusoidal transforms
III. 3. Rectangular transforms
III. 4. Eigenvector-based transforms
Digital Image Processing
Lecture 1 – Introductory
Course description (3)
IV. Image enhancement:
IV. 1. Point operations
IV. 2. Grey level histogram; histogram-based enhancement
IV. 3. Spatial operations
IV. 4. Transform-based operations
IV. 5. Color image enhancement & pseudo-coloring
V. Image analysis & understanding:
V.1. Regions of interest; features; feature extraction
V. 2. Edge detection, boundary extraction & representation
V. 3. Regions detection, extraction & representation
V. 4. Binary object structure analysis & representation: median
axis transforms; binary morphology
Digital Image Processing
Lecture 1 – Introductory
Course description (4)
V. 5. Shape descriptors
V. 6. Texture representation; texture descriptors
V. 7. Region-based image segmentation
VI. Image compression & coding:
VI. 1. Introduction
VI. 2. Pixel coding
VI. 3. Predictive coding of still images
VI. 4. Transform coding of still images
VI. 5. Video sequence (inter-frame) coding
… all with practical examples given – in the lectures & lab!
Digital Image Processing
Lecture 1 – Introductory
Exam grade information
•
The grade components:
1) Written exam – quiz: => max. 3.5 pts
- 6 questions from theory
- 6 questions from problems/exercises
2) Written exam – classic: => max. 6.5 pts
- 5 short theoretic subjects (max. ½ page answer)
- 5 problems/exercises
=> Written exam grade E=1…10
3) Laboratory work evaluation: => grade L=1…10
4) Lecture participation/discussions: => grade LD=1…10
5) Project evaluation: => grade P=1…10
____________________________________________________________________
The grade = 0.75(0.7E+0.2L+0.1LD)+0.25P
To pass the exam: must have E≥ 5, L≥ 5.