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Introduction to Image Processing Representasi Citra Tahap-Tahap Kunci pada Image Processing Aplikasi dan Topik Penelitian pada Image Processing.

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Presentation on theme: "Introduction to Image Processing Representasi Citra Tahap-Tahap Kunci pada Image Processing Aplikasi dan Topik Penelitian pada Image Processing."— Presentation transcript:

1 Introduction to Image Processing Representasi Citra Tahap-Tahap Kunci pada Image Processing Aplikasi dan Topik Penelitian pada Image Processing

2 Image Representation Representasi Citra

3 Images are Ubiquitous Input Optical photoreceptors Digital camera CCD array Output TVs Computer monitors Printers

4 Image Formation

5 Sampling and Quantization

6

7 Image as Array of Pixels An image is a 2-d rectilinear array of pixels

8 Pixels as samples A pixel is a sample of a continuous function

9 9 What is an image? The bitmap representation Also called “raster or pixel maps” representation An image is broken up into a grid pixel Gray level Original picture Digital image f(x, y) I[i, j] or I[x, y] x y

10 10 What is an image? The bitmap representation

11 11 What is an image? The vector representation Object-oriented representation Does not show information of individual pixel, but information of an object (circle, line, square, etc.) Circle(100, 20, 20) Line(xa1, ya1, xa2, ya2) Line(xb1, yb1, xb2, yb2) Line(xc1, yc1, xc2, yc2) Line(xd1, yd1, xd2, yd2)

12 12 Comparison between Bitmap Representation and Vector Representation Bitmap Can represent images with complex variations in colors, shades, shapes. Larger image size Fixed resolution Easier to implement Vector Can only represent simple line drawings (CAD), shapes, shadings, etc. Efficient Flexible Difficult to implement

13 Image as a Function We can think of an image as a function, f, from R 2 to R: f( x, y ) gives the intensity at position ( x, y ) Realistically, we expect the image only to be defined over a rectangle, with a finite range: f: [a,b] x [c,d]  [0,1] A color image is just three functions pasted together. We can write this as a “vector-valued” function:

14 Image as a function

15 Properties of Images Spatial resolution Width pixels/width cm and height pixels/ height cm Intensity resolution Intensity bits/intensity range (per channel) Number of channels RGB is 3 channels, grayscale is one channel

16 Common image file formats GIF (Graphic Interchange Format) - PNG (Portable Network Graphics) JPEG (Joint Photographic Experts Group) TIFF (Tagged Image File Format) PGM (Portable Gray Map) FITS (Flexible Image Transport System)

17 Key Stages in Digital Image Processing Tahap-tahap Kunci pada Pemrosesan Citra Digital

18 Key Stages in Digital Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression

19 Key Stages in Digital Image Processing: Image Aquisition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

20 Key Stages in Digital Image Processing: Image Enhancement Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

21 Key Stages in Digital Image Processing: Image Restoration Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

22 Key Stages in Digital Image Processing: Morphological Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

23 Key Stages in Digital Image Processing: Segmentation Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

24 Key Stages in Digital Image Processing: Object Recognition Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

25 Key Stages in Digital Image Processing: Representation & Description Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

26 Key Stages in Digital Image Processing: Image Compression Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression

27 Key Stages in Digital Image Processing: Colour Image Processing Image Acquisition Image Restoration Morphological Processing Segmentation Representation & Description Image Enhancement Object Recognition Problem Domain Colour Image Processing Image Compression Images taken from Gonzalez & Woods, Digital Image Processing (2002)

28 Applications and Research Topics

29 Document Handling

30 Signature Verification

31 Biometrics

32 Fingerprint Verification / Identification

33 Fingerprint Identification Research at UNR Minutiae Matching Delaunay Triangulation

34 Object Recognition

35 Object Recognition Research reference view 1 reference view 2 novel view recognized

36 Indexing into Databases Shape content

37 Indexing into Databases (cont’d) Color, texture

38 Target Recognition Department of Defense (Army, Airforce, Navy)

39 Interpretation of aerial photography is a problem domain in both computer vision and registration. Interpretation of Aerial Photography

40 Autonomous Vehicles Land, Underwater, Space

41 Traffic Monitoring

42 Face Detection

43 Face Recognition

44 Face Detection/Recognition Research at UNR

45 Facial Expression Recognition

46 Face Tracking

47 Face Tracking (cont’d)

48 Hand Gesture Recognition Smart Human-Computer User Interfaces Sign Language Recognition

49 Human Activity Recognition

50 Medical Applications skin cancer breast cancer

51 Morphing

52 Inserting Artificial Objects into a Scene


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