Background reading: Forsyth and Ponce, Computer Vision Chapter 7
Image sampling and quantization
Types of images: binary, gray scale, color
Resolution: DPI: dots per inch, spatial pixel density
Image histograms: histogram of an image provides the frequency of the brightness(intensity) value in the image
Image as functions: an image is a funciton $f$ from $R^2$ to $R^M$
Linear systems: Forming a new image whose pixel values are transformed from original pixel values
Goal: extract useful information from images, or transform images into another domain where we can modify/enhance image properties.
- Features(edges, corners, blobs)
- super-resolution, in-painting, de-nosing
Moving Average, image segmentation,
Convolution and correlation:
Edge effect: A computer will only convolve finite support signal,at the edge:
- zero padding
- edge value replication
- mirror extension
