digital image processing lecture notes – fall 2010 lecturer: conf. dr. ing. mihaela gordan...

Digital Image Digital Image ProcessingProcessingLecture notes – fall 2010Lecture notes – fall 2010

Lecturer:Conf. dr. ing. Mihaela GORDANCommunications Departmente-mail: [email protected] phone: 0264-401309Office address: Multimedia (CTMED) laboratory, Str. C. Daicoviciu Nr. 15

Digital Image ProcessingDigital Image Processing

Lecture 1Lecture 1

• Introduction Introduction • Course descriptionCourse description• Examination grade informationExamination grade information

Lecture 1 – IntroductoryLecture 1 – Introductory


Introduction (1)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




Applications of digital image processing?

… virtually, everywhere!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…




• Industrial inspectionIndustrial inspection(industrial vision systems):(industrial vision systems):




• Environment surveillance/monitoring:


Forest fire monitoringHydro sites surveillance

Water sources inspection:


Introduction (5)Introduction (5)• Medical imaging applications:


Ultrasound image analysis/quantification

Color image segmentation &Cells counting


Course description (1)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



Course description (2)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 propertiesIII. 2. Sinusoidal transformsIII. 3. Rectangular transformsIII. 4. Eigenvector-based transforms



Course description (3)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 extractionV. 2. Edge detection, boundary extraction & representationV. 3. Regions detection, extraction & representationV. 4. Binary object structure analysis & representation: median axis transforms; binary morphology



Course description (4)Course description (4)V. 5. Shape descriptorsV. 6. Texture representation; texture descriptorsV. 7. Region-based image segmentation

VI. Image compression & coding:VI. 1. IntroductionVI. 2. Pixel codingVI. 3. Predictive coding of still imagesVI. 4. Transform coding of still imagesVI. 5. Video sequence (inter-frame) coding

… all with practical examples given – in the lectures & lab!



Examination grade informationExamination 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…103) Laboratory work evaluation: => grade L=1…10 4) Lecture participation/discussions: => grade LD=1…105) Project evaluation: => grade P=1…10

____________________________________________________________________

The grade = 0.75(0.7T+0.2L+0.1LD)+0.25PTo pass: must have T≥ 4.5, L≥ 5.


ReferencesReferences A) Lecture: A.Vlaicu – A.Vlaicu – Prelucrarea imaginilor Prelucrarea imaginilor digitaledigitale. Editura Microinformatica, . Editura Microinformatica, Cluj-N., 1997Cluj-N., 1997Lecture slides – available onlineLecture slides – available onlineB) Laboratory:

Will be soon available online (as pdf); Will be soon available online (as pdf); also online images, some sample also online images, some sample applications/codeapplications/codeC) Exercises, written test samples:

Available onlineAvailable onlineThe official DIP course site:The official DIP course site:http://193.226.17.10/sites/pnihttp://193.226.17.10/sites/pni

Digital Image ProcessingDigital Image Processing Lecture 1 – IntroductoryLecture 1 – Introductory

Mathematical Representation of Grey Mathematical Representation of Grey Scale Digital Images (1)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 (LMax)= white.

=> mathematically: let the physical dimensions of the scene be Hf – height and Wf – width; e.g., Hf=25cm; Wf=4cm; the scene origin = upper left corner => the space-continuous grey level image is described by the brightness spatial function:

f:[0;Wf)×[0;Hf)→[0;LMax], f(x,y)=the brightness of the scene in the spatial position (x,y)


Mathematical Representation of Grey Mathematical Representation of Grey Scale Digital Images (2)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 atlow light (illumination) levels

y

x(0,0)

Wf

Hf

Continuous grey level scene Scene digitization: discretization of the spatial positions

δx

δy

(0,0)


digital image processing lecture notes – fall 2010 lecturer: conf. dr. ing. mihaela gordan...

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