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 compileCH3_pixel_operation.cpp:
./compile.sh -DCH3 CH3_pixel_operation.cpp utility.cpp In which-DCH3 activates themain function in the source file, otherwisemain function is ignored. And this source file includesutility.h, so we need to compileCH3_pixel_operation.cpp withutility.h's implementation, which isutility.cpp.
Similar method for CH 4,5,6,7,8 to compile.
To compileCH9_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.
Set threshold as 100:
SetdFa as2.0, setdFb as-55:
Setgamma as1.8, setcomp as0:
SetdC as 10:
Setx1,x2,y1,y2 as20,50,100,200:
Histogram matching to dark
Histogram matching to light
CH4 geometric transformation Move20 right and50 down:
Rotate 30 degrees counterclockwise:
CH5 spatial domain image enhancement Kernel size 3:
Kernel size 5:
Kernel size 7:
Average vs Gaussian vs Median filter
Roberts cross gradient operator Positive 45 degrees v.s. Negative 45 degrees v.s. Positive+Negative 45 degrees:
Vertical v.s. Horizontal v.s. Vertical+Horizontal:
90 degrees rotation isotropy v.s. 45 degrees rotation isotropy v.s. Weighted
Roberts positive 45 degrees:
Sobel vertical:
Laplacian weighted:
CH6 frequency domain image enhancement
Gauss band rejection filter Image and noised image in frequency domain:
Filter and filterd image in frequency domain:
Before and after applying Gauss band rejection filter:
CH7 color image processing
CH8 morphology image processing Erode using 3 x 3 square kernel and using cross kernel
Dilate using 3 x 3 square kernel and using cross kernel
Erode operation and open operation
Dilate operation and close operation
Hit-or-miss transform with 50 x 50 square kernel
Label connected component
Convex hull unconstrained and constrained(using bounding rectangle)
Edge detection using Prewitt operator
Edge detection using Sobel operator
Edge detection using LoG operator
Hough transformation for line detection
Automatically choose threshold for binarization
Region growing for image segmentation
Region splitting(decomposing) for image segmentation
