1 Computer Vision & Image Processing G. Andy Chang Department of Mathematics & Statistics Youngstown State University Youngstown, Ohio.

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Presentation transcript:

1 Computer Vision & Image Processing G. Andy Chang Department of Mathematics & Statistics Youngstown State University Youngstown, Ohio

CV & IP 2 Human Vision Illusion 1. Green > Red 2. Green = Red 3. Green < Red

CV & IP 3 Illusion (Human Vision)

CV & IP 4 Vision System Human Vision n Qualitative n Comparative Computer Vision n Quantitative 320 pixels 334 pixels Pixel (combination of Picture & Element) is the smallest element of a display which can be assigned a color.

CV & IP 5 Old design Wafer Human hair thickness is about 100 micron.

CV & IP 6 Computer Vision 564×380 Digital Image

CV & IP =256 0 ~ 255

CV & IP 8 Original Image (564×380) 8-bit Gray Scale Image (256 gray levels)

CV & IP 9 Image with 84 × 57 pixels (Low resolution)

CV & IP 10 3-bit Gray Scale Image (0 – 7) EXCEL WORKSHEET_AM EXCEL WORKSHEET_PM

CV & IP 11 Original Image (564×380) 8-bit Gray Scale Image (256 = 2 8 gray levels)

CV & IP 12 Smoothed Image

CV & IP 13 Sharpened Image

CV & IP 14 Inverted Image (564×380) 8-bit Gray Scale Image 0       250 …

CV & IP 15 Object Identification (Binary Thresholding)

CV & IP 16 Object Identification

CV & IP 17 Object Identification

CV & IP 18 Object Identification

CV & IP 19

CV & IP 20

CV & IP 21

CV & IP 22

CV & IP 23

CV & IP 24

CV & IP 25 Old design

CV & IP 26 Old design

CV & IP 27 Oyster Size Measurement (a)(b) a) Original Image b) Binary Image of Projected Area

CV & IP 28 Images of Firm and Soft Apples

CV & IP 29 Blood Cells