Morphological Transformations and Histogram Equalization

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Presentation transcript:

Morphological Transformations and Histogram Equalization Dr. Rongzhong Li CSC391/691 Fall 2016 CameraCalibration Example Images are from: OpenCV-Python Documentation

Input + Neighbor + Rule = Output Cellular Automaton Input + Neighbor + Rule = Output 1 1 && || 1 1 Growth of Crystal, Replication of bacteria, Complex rules=> randomness, Stephen Wolfram Conway's Game of Life

Kernel Shape 1 1 1 Complexity? Optimization?

Morphological Transformations Transform(Origin, Kernel) Erosion Dilation Opening = Erosion + Dilation Closing = Dilation + Erosion Morphological Gradient = diff( Dilation, Erosion) Top Hat = diff(Origin, Opening) Black Hat = diff(Origin, Closing) Original image Kernel 1

Erosion A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel is 1, otherwise it is eroded (made to zero).

Dilation Opposite of erosion. A pixel element is '1' if at least one pixel under the kernel is '1'.

1st Level Combination Opening = Erosion + Dilation Closing = Dilation + Erosion

Morphological Gradient = diff(Dilation, Erosion) Top Hat 2nd Level Combination Morphological Gradient = diff(Dilation, Erosion) Top Hat = diff(Origin, Opening) Black Hat = diff(Origin, Closing)

Generating Examples from Scratch Reduce problem complexity

Results of Combinations

Histogram Threshold 2 3 1 4 128 4 3 2 1

Histogram Threshold 50

Histogram Equalization 50 150

Enhancing Details