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Measurements in Fluid Mechanics 058:180 (ME:5180) Time & Location: 2:30P - 3:20P MWF 3315 SC Office Hours: 4:00P – 5:00P MWF 223B-5 HL Instructor: Lichuan.

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Presentation on theme: "Measurements in Fluid Mechanics 058:180 (ME:5180) Time & Location: 2:30P - 3:20P MWF 3315 SC Office Hours: 4:00P – 5:00P MWF 223B-5 HL Instructor: Lichuan."— Presentation transcript:

1 Measurements in Fluid Mechanics 058:180 (ME:5180) Time & Location: 2:30P - 3:20P MWF 3315 SC Office Hours: 4:00P – 5:00P MWF 223B-5 HL Instructor: Lichuan Gui Phone: (Lab), (Cell)

2 2 Lecture 24. Digital image & image processing

3 3 Digital image & image processing Digital Image

4 4 Digital image & image processing Digital Image Images constitute a continuous spatial distribution of the irradiance at a plane. Digital images consist of pixels. Each pixel represents a square region of the image on a square grid. Pixel value (gray value) represents intensity of the irradiance – 1-bit: 0  1 – 8-bit: 0  255 –10-bit: 0  1,023 –12-bit: 0  4,095 –24-bit: 0  16,777,215 Bitmap data in file.

5 5 Digital image & image processing Digital Image Sufficient pixels make image look continuous

6 6 Digital image & image processing Digital Image Color models - True color model: (Red,Green,Blue)=(0  255, 0  255, 0  255) - Others: Palettes/look-up table (LUT)/color map/index map/etc.

7 7 Digital image & image processing Digital Image Physical & logical pixels

8 8 Digital image & image processing Digital Image Digital PIV image sample displayed on a PC screen presented as a 2D-function G(x,y)

9 9 Digital image & image processing Digital Image 2D & 3D digital images

10 10 Digital image & image processing Digital Image Histogram (PDF) of digital image PDF – Probability density function

11 11 Digital image & image processing Digital Image Processing Pixel operation Filter operation Many others - Changing gray value of single pixel without considering the neighborhood - Changing gray value of single pixel considering the neighborhood of (2r+1)  (2r+1) pixels

12 12 Digital image & image processing Pixel Operation Linear transformation

13 13 Digital image & image processing Pixel Operation Binary operation

14 14 Digital image & image processing Pixel Operation Threshold

15 15 Digital image & image processing Pixel Operation Invert Square Root

16 16 Digital image & image processing Digital Filter Smooth filter

17 17 Digital image & image processing Digital Filter Gradient filter

18 18 Digital image & image processing Digital Filter Laplace filter

19 19 Digital image & image processing Digital Filter Regional normalization

20 20 Digital image & image processing Digital Filter (Removing) unsharp mask

21 21 Digital image & image processing Digital Filter Median Expansion Erosion

22 22 Digital image & image processing Digital image files Device independent image files Device dependent image files - Microsoft Windows Bitmap “*.bmp” - Tag Image File Format “*.tif” - Graphics Interchange Format “*.gif” - JPEG File Interchange Format “*.jpg” - Many others - Raw image formats (e.g. *.raww) - TSI image file Format “*.img” - DANTEC image file format “*.img” - LAVISION image file format “*.img” - Others Basic components of a digital image Information in a bitmap header - Header, Palette, Bitmap Data, Footer etc. –File Identifier –File Version –Number of Lines per Image –Number of Pixels per Line –Number of Bits per Pixel –Number of Color Planes –Compression Type –X & Y Origin of Image –Text Description –Others –Unused Space

23 23 Digital image & image processing Digital image files Microsoft Windows Bitmap –14-byte file header (Version 2.x +) Byte 1  2File type, always 4D42h (“BM”); Byte 3  6Size of the file in bytes; Byte 7  8Reserved 1, always 0; Byte 9  10Reserved 2, always 0; Byte 11  14Starting position of image data in bytes. –Bitmap header Byte 1  4Size of this header in bits; Byte 5  8Image width in pixels;(2 bytes for version 2.x) Byte 9  12Image height in pixels; (2 bytes for version 2.x) Byte 13  14Number of color planes; Byte 15  16Number of bits per pixel; (end of version 2.x) Byte 17  20Compression methods used; Byte 21  24Size of bitmap in Bytes; Byte 25  28Horizontal resolution in pixels per meter; Byte 29  32Vertical resolution in pixels per meter; Byte 33  36Number of colors in the image; Byte 37  40Minimum number of important colors. (end of version 3.x) Up to 108 bytes for Version 4.x –Color Palette One-, 4-, and 8-bit BMP files always contain a color palette 24-bit BMP files never contain color palettes

24 24 Class project Project option #1 The image in Case D of PIV Challenge 2001 is used here as a sample recording. It is a double-exposure provided by the research group of Professor Adrian for a near-wall turbulent pip flow experiment. The measurement domain is 6-mm x 6-mm (beginning at the wall) in a 137-mm diameter pipe, and the flow was directed to right. The airflow was seeded with 1-micron olive oil droplets and illuminated with a pulsed YAG laser (~200 mJ/pulse). The particles were imaged with a Nikkor 80-mm f/5.6 Lens and recorded in a 4"x5" photographic film with resolution of 125 lines/mm. The time interval between the laser double pulses is 18 µs. The photographic PIV recording is digitized in size of 1024x1024 pixels. - Write a computer program to evaluate a double-exposed PIV recording with auto-correlation algorithm

25 25 Class project Project option #2 A pair of PIV recordings, which is one example out of several thousand PIV recordings recorded by Kaehler (2001) in DLR within the EC funded EUROWAKE project dedicated to the investigation of wake vortices behind a transport aircraft, is used here to demonstrate the procedures for evaluating a single-exposed PIV recording pair with the EDPIV software. The measurement was conducted at 1.64 m behind the wing tip, and the field of view of 170-mm x 140-mm was imaged with a digital resolution of 1280x1024 pixels. An evaluation procedure is suggested as follows. - Write a computer program to evaluate a single-exposed PIV recording pair with cross-correlation algorithm

26 26 Class project Start with image processing - Write a Matlab function of unsharp mask for project option #1 - Write a Matlab function of regional normalization for project option #2


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