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Computer Vision Fundamentals: Image Formats, Filtering, and Convolution Techniques

A comprehensive introduction to image formats, pixel intensities, color matrices, linear filtering, and convolution methods in computer vision. Learn how filtering and convolution are used for image modifications, information integration, scaling, change detection, and Fourier analysis.

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Computer Vision Fundamentals: Image Formats, Filtering, and Convolution Techniques

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  1. Computer Vision • Introduction to Image formats, reading and writing images, and image environments • Image filtering

  2. Images • Black and white image is a 2D matrix. • Intensities represented as pixels. • Color images are 3D matrix, RBG.

  3. 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.

  4. (from Freeman, MIT)

  5. (Freeman)

  6. Convolution • Convolution kernel g, represented as matrix. • it’s associative • Result is:

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