1. Digital Image Processing Lecture notes – fall 2010 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

2. Digital Image Processing Lecture 1 Introduction Introduction Course description Course description Examination grade information Examination grade information Lecture 1 – Introductory

3. Digital Image Processing Introduction (1) Digital image processing: deals with digital images = digital representation of the visual scenes Note that: 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 Lecture 1 – Introductory

4. Digital Image Processing Introduction (2) Applications of digital image processing? everywhere! … 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… Lecture 1 – Introductory

5. Digital Image Processing Introduction (3) Industrial inspection Industrial inspection (industrial vision systems): Lecture 1 – Introductory

6. Digital Image Processing Introduction (4) Environment surveillance/monitoring: Lecture 1 – Introductory Forest fire monitoring Hydro sites surveillance Water sources inspection:

7. Digital Image Processing Introduction (5) Medical imaging applications: Lecture 1 – Introductory Ultrasound image analysis/quantification Color image segmentation & Cells counting

8. Digital Image Processing 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 Lecture 1 – Introductory

9. Digital Image Processing Course description (2) Course chapters:Course chapters: I.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 Lecture 1 – Introductory

10. Digital Image Processing 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 Lecture 1 – Introductory

11. Digital Image Processing 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! Lecture 1 – Introductory

12. Digital Image Processing Examination grade information The grade components: 1) Written test – quiz: => max. 3.5 pts - 6 questions from theory - 6 questions from problems/exercises 2) Written test – classic: => max. 6.5 pts - 5 short theoretic subjects (max. ½ page answer) - 5 problems/exercises => Written test grade T=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(0.7T+0.2L+0.1LD)+0.25P To pass: must have T≥ 4.5, L≥ 5. Lecture 1 – Introductory

13. References A) Lecture: A.Vlaicu – Prelucrarea imaginilor digitale. Editura Microinformatica, Cluj- N., 1997 Lecture slides – available online B) Laboratory: Will be soon available online (as pdf); also online images, some sample applications/code C) Exercises, written test samples: Available online The official DIP course site: http://193.226.17.10/sites/pni Digital Image Processing Lecture 1 – Introductory

14. Mathematical Representation of Grey Scale Digital Images (1) Def.: Grey scale image = visual representation of a finite size 2-D scene, in which the scene is represented, in each spatial position (x,y), by its brightness (=grey level value, lightness): - minimum brightness (0) = black; - maximum brightness (L Max )= white. => mathematically: let the physical dimensions of the scene be H f – height and W f – width; e.g., H f =25cm; W f =4cm; the scene origin = upper left corner => the space-continuous grey level image is described by the brightness spatial function: f:[0;W f )×[0;H f )→[0;L Max ], f(x,y)=the brightness of the scene in the spatial position (x,y) Digital Image Processing Lecture 1 – Introductory

15. Mathematical Representation of Grey Scale Digital Images (2) Note: The brightness information is the most important in the scene; it is perceived by a special type of photoreceptors (the rods) in the HVS; the perception of brightness makes possible the orientation at low light (illumination) levels y x (0,0) WfWf HfHf Continuous grey level scene Scene digitization: discretization of the spatial positions δxδx δyδy (0,0) Digital Image Processing Lecture 1 – Introductory