[学习笔记] CS131 Computer Vision: Foundations and Applications：Lecture 4 像素和滤波器

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