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The third part of the master imgproc components
Chapter VI image processing
6.1, linear filtering: Pdf.P170
Pdf.P171
Smoothing (Smoothing) (blurring process (bluring)) - high frequency of use - Uses: less noise / distortion on the image. When reducing the resolution, the smoothing process is a very useful method.
Image filtering, noise refers to the object image is suppressed under conditions to preserve image detail features.
Eliminating noise components in the image is called smoothing / filtering operation image. ... Thus a filter can be reduced high-frequency component can be reduced the influence of noise.
Image filtering two purposes: (1) extracting features of the object as image pattern recognition feature; (2) to meet the requirements of the image processing, the digitized image to eliminate noise mixed.
Two pairs of the filtering process requires: (1) does not damage the important information and the contour of the edge image and the like; (2) the image clarity is good visual effect.
Smoothing - spatial domain low frequency enhancement filtering technique - two purposes: (1) blur; (2) to eliminate noise.
Smoothing spatial domain generally use simple average method, it is to compute the average luminance value of the pixel adjacent to the point. Is directly related to the size of the neighborhood and the effect of smoothing, the better the larger the effect of smoothing the neighborhood, the neighborhood is too large, smooth edges also make greater loss of information, so that the output image becomes blurred, and therefore need to rationalize select the size of the neighborhood.
Metaphor image filter: the filter imagine a window containing the weighting coefficients, when the image smoothing filter, put into the window over the image, we look through the window image.
Pdf.P172
Linear filter: reject the input signal frequency is often used unwanted or select a desired frequency from a number of frequencies.
Several common linear filter:
Low pass filter: by allowing low frequencies;
High-pass filter: by allowing high frequency;
Bandpass filter: allow a range of frequencies by;
Bandstop filter: by blocking certain frequency range and by allowing other frequencies;
All-pass filter: allows all frequencies by changing only the phase relationship;
Notch filter (Band-Stop Filter): by blocking a narrow frequency range, it is a special band rejection filter.
6.1.4, filter and fuzzy
An important measure of the filtered pilot signal filtered specific wavelength band frequency operation, and is disposed to suppress interference.
Filter tap low pass filtering and high pass filtering in two ways: using a Gaussian filter means is a Gaussian function as a filter function of filtering operation, low pass filter is a Gaussian blur, Gaussian high pass filtering is sharpened.
6.1.5, the linear operator neighborhood filtering Neighborhood
Filter block - BoxBlur function
Mean filter (neighborhood average filter) - Blur function
Gaussian filtering - GaussianBlur function
6.2, nonlinear filtering:
Median filtering - medianBlur function
Bilateral Filtering - bilateralFilter function
6.3, morphological filtering (1):
corrosion
Swell
6.4, morphological filtering (2):
Open computing
Closing operation
Morphological Gradient
Top hat
Black Hat
6.5, diffuse water filled
6.6, image size scaling and image pyramid
6.7, thresholding
Chapter VII of the image transformation
7.1, based on edge detection OpenCV
7.2, Hough transform
7.3, Remap
7.4, affine transformation
7.5, histogram equalization
Chapter VIII of the outline of the image segmentation and image restoration
8.1, find and contouring
8.2, the convex hull find items
8.3, using a polygonal contour surrounded
8.4, rectangular image (ZC: What is the rectangle??)
8.5, watershed algorithm
8.6, image repair
Chapter IX histogram matching
9.1, the image histogram Overview
9.2, the histogram is calculated and plotted
9.3, the histogram comparison
9.4, back projection
9.5, template matching
The fourth component further feature2d
Chapter X corner detection
10.1, Harris corner detection
10.2, Shi-Tomasi corner point 检测
10.3, sub-pixel corner detection
Chapter XI feature detection and matching
11.1, SURF feature point detection
11.2, SURF feature extraction
11.3, using feature point matching FLANN
11.4, looking for known objects
11.5, ORB feature extraction
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