Computer Vision

DESCRIPTION

This overview covers the basics of computer vision, focusing on image formats and manipulation techniques. We explore various image types, including black and white images as 2D matrices and color images represented as 3D RGB matrices. The discussion highlights linear filtering, emphasizing local methods and linear combinations of neighboring pixels. We delve into the practical applications of these techniques, such as information integration, change detection, and the use of Fourier analysis. Understanding convolution and its properties is essential for effective image processing.

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May 12, 2026

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Computer Vision

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Presentation Transcript

Computer Vision • Introduction to Image formats, reading and writing images, and image environments • Image filtering
Images • Black and white image is a 2D matrix. • Intensities represented as pixels. • Color images are 3D matrix, RBG.
Linear Filtering • About modifying pixels based on neighborhood. Local methods simplest. • Linear means linear combination of neighbors. Linear methods simplest. • Useful to: • Integrate information over constant regions. • Scale. • Detect changes. • Fourier analysis.
(from Freeman, MIT)
(Freeman)
Convolution • Convolution kernel g, represented as matrix. • it’s associative • Result is: