chapter-2.1 Histogram.ppt
- 1. HISTOGRAM TRANSFORMATION IN IMAGE PROCESSING AND ITS APPLICATIONS
- 2. Image Enhancement (Spatial) Image enhancement: 1. Improving the interpretability or perception of information in images for human viewers 2. Providing `better' input for other automated image processing techniques Spatial domain methods: operate directly on pixels Frequency domain methods: operate on the Fourier transform of an image
- 3. Contents Histogram Histogram transformation Histogram equalization Contrast streching Applications
- 4. Histogram 0 1 1 2 4 2 1 0 0 2 5 2 0 0 4 1 1 2 4 1 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 • The (intensity or brightness) histogram shows how many times a particular grey level (intensity) appears in an image. For example, 0 - black, 255 – white image histogram
- 5. Histogram • An image has low contrast when the complete range of possible values is not used. Inspection of the histogram shows this lack of contrast.
- 6. Histogram of color images • RGB color can be converted to a gray scale value by Y = 0.299R + 0.587G + 0.114B • Y: the grayscale component in the YIQ color space used in NTSC television. • The weights reflect the eye's brightness sensitivity to the color primaries.
- 7. Histogram of color images • Histogram: individual histograms of RED, GREEN and BLUE Blue
- 8. Histogram of color images R G B
- 9. Histogram equalization (HE) • Ttransforms the intensity values so that the histogram of the output image approximately matches the flat (uniform) histogram
- 10. Histogram equalization • As for the discrete case the following formula applies: k = 0,1,2,...,L-1 L: number of grey levels in image (e.g., 255) nj: number of times j-th grey level appears in image n: total number of pixels in the image ·(L-1)
- 11. Histogram equalization Input image HE image
- 12. Histogram equalization Input image HE image
- 13. Histogram projection (HP) • Assigns equal display space to every occupied raw signal level, regardless of how many pixels are at that same level. In effect, the raw signal histogram is "projected" into a similar-looking display histogram.
- 14. Histogram projection HE HP IR image
- 15. Histogram projection • occupied (used) grey level: there is at least one pixel with that grey level • B(k): the fraction of occupied grey levels at or below grey level k • B(k) rises from 0 to 1 in discrete uniform steps of 1/n, where n is the total number of occupied levels • HP transformation: sk = 255 ·B(k).
- 16. Plateau equalization • By clipping the histogram count at a saturation or plateau value, one can produce display allocations intermediate in character between those of HP and HE.
- 17. Plateau equalization HE PE Input image
- 18. Plateau equalization • The PE algorithm computes the distribution not for the full image histogram but for the histogram clipped at a plateau (or saturation) value in the count. • When that plateau value is set at 1, we generate B(k) and so perform HP; • When it is set above the histogram peak, we generate F(k) and so perform HE. • At intermediate values, we generate an intermediate distribution which we denote by P(k). • PE transformation: sk = 255· P(k)
- 19. Contrast streching (CS) By stretching the histogram we attempt to use the available full grey level range. The appropriate CS transformation : sk = 255·(rk-min)/(max-min)
- 20. Contrast streching Input image HE image
- 21. Contrast streching CS does not help here HE Input image HE image
- 23. Contrast streching HE CS 79, 136 CS Cutoff fraction: 0.8 Input image
- 25. Applications CT lung studies Thresholding Normalization Normalization of MRI images Presentation of high dynamic images (IR, CT)
- 26. CT lung studies Yinpeng Jin HE taken in a part of the image
- 27. Thresholding
- 28. Thresholding converting a greyscale image to a binary one for example, when the histogram is bi-modal threshold: 120
- 29. Thresholding when the histogram is not bi-modal threshold: 80 threshold: 120