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 was a Funciton $f $ from $R ^2$ to $R ^m$
Linear Systems: Forming a new image whose pixel values is transformed from original pixel values
Goal:extract useful information from images, or transform images to another domain where we can modify/enhance image p Roperties.
- Features (edges, corners, blobs)
- Super-resolution, In-painting, de-nosing
Moving Average, image segmentation,
Convolution and correlation:
Edge Effect: A computer would only convolve finite support signal,at the edge:
- Zero padding
- Edge Value Replication
- Mirror extension
[Learning Notes] CS131 Computer vision:foundations and applications:lecture 4 pixels and filters