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cpp implementation for algorithms in the book "digital image processing and computer vision"("数字图像处理与机器视觉-Visual C++与Matlab实现")

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digital-image-processing-cpp

cpp implementation for algorithms in the book "数字图像处理与机器视觉-Visual C++与Matlab实现"

To compile CH3_pixel_operation.cpp:

./compile.sh -DCH3 CH3_pixel_operation.cpp utility.cpp

In which -DCH3 activates the main function in the source file, otherwise main function is ignored. And this source file includes utility.h, so we need to compile CH3_pixel_operation.cpp with utility.h's implementation, which is utility.cpp.

Similar method for CH 4,5,6,7,8 to compile.

To compile CH9_image_segmentation.cpp:

./compile.sh -DCH9 CH9_image_segmentation.cpp CH3_pixel_operation.cpp CH5_spatial_domain_image_enhancement.cpp CH8_morphology_image_processing.cpp utility.cpp

Because it includes the corresponding headers.

CH3 pixel operation

Threshold

Set threshold as 100:

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Linear Transform

Set dFa as 2.0, set dFb as -55:

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Gamma Transform

Set gamma as 1.8, set comp as 0:

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Log Transform

Set dC as 10:

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Partial Linear Transform

Set x1, x2, y1, y2 as 20, 50, 100, 200:

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Histogram equalization

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Histogram matching to dark

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Histogram matching to light

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CH4 geometric transformation

Move

Move 20 right and 50 down:

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Horizontal mirror

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Vertical mirror

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Scale

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Rotate

Rotate 30 degrees counterclockwise:

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Image projection restore

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CH5 spatial domain image enhancement

Smooth average

Kernel size 3:

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Kernel size 5:

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Kernel size 7:

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Smooth gaussian

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Log edge detection

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Average vs Gaussian vs Median filter

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Roberts cross gradient operator

Positive 45 degrees v.s. Negative 45 degrees v.s. Positive+Negative 45 degrees:

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Sobel gradient operator

Vertical v.s. Horizontal v.s. Vertical+Horizontal:

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Laplacian operator

90 degrees rotation isotropy v.s. 45 degrees rotation isotropy v.s. Weighted

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Enhancement

Roberts positive 45 degrees:

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Sobel vertical:

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Laplacian weighted:

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CH6 frequency domain image enhancement

Ideal low pass filter

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Gauss low pass filter

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Gauss high pass filter

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Laplace filter

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Gauss band rejection filter

Image and noised image in frequency domain: drawing

Filter and filterd image in frequency domain: drawing

Before and after applying Gauss band rejection filter: drawing

CH7 color image processing

CMY

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HSI

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HSV

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YUV

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YIQ

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Color compensating

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Color balancing

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CH8 morphology image processing

Erode using 3 x 3 square kernel and using cross kernel

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Dilate using 3 x 3 square kernel and using cross kernel

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Erode operation and open operation

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Dilate operation and close operation

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Hit-or-miss transform with 50 x 50 square kernel

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Extract boundary

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Trace boundary

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Fill region

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Label connected component

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Thining

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Pixelate

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Convex hull unconstrained and constrained(using bounding rectangle)

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Gray dilate

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Gray erode

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Gray open

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Gray close

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Top-hat transform

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CH9 image segmentation

Edge detection using Prewitt operator

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Edge detection using Sobel operator

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Edge detection using LoG operator

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Canny edge detector

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Hough transformation for line detection

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Automatically choose threshold for binarization

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Region growing for image segmentation

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Region splitting(decomposing) for image segmentation

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