Types of Features and Image Segmentation
Types of features: Edges, Corners and Blobs.
Corner Detector: Intersection of 2 edges, can be calculated with Sobel operators (Sobel x and Sobel y) Gx and Gy (G for Gradient)
Dilation (add pixels to the boundaries of an object) and erosion (removes pixels along object boundaries) can be combined to fill in gaps in the image or eliminate noise.
Some combined operations are: opening, which is erosion followed by dilation, This is useful in noise reduction in which erosion first gets rid of noise (and shrinks the object) then dilation enlarges the object again.
Closing is the reverse combination of opening; it’s dilation followed by erosion, which is useful in closing small holes or dark areas within an object.
Image Contouring: Allow us to get the area, perimeter, center and bounding rectangle of an image. It can be obtained with a binary thresholded image with black and white pixels (inverted so the background is black) in openCV you can use cv2.findContours method.
K-means Clustering: Separates an image into segments by clustering data points that have similar traits. K-means is an unsupervised learning method.