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University of Central Florida

Filtering in Computer Vision - Lecture 2

University of Central Florida via YouTube

Overview

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This lecture introduces filtering for binary, grayscale, and color images, covering histograms, noise, derivatives, correlation, convolution, Gaussian smoothing, and edge detection, with MATLAB functions and applications.

Syllabus

General
Binary Images
Gray Level Image
Gray Scale Image
Color Image Red, Green, Blue Channels
Image Histogram
Image Noise
Gaussian Noise
Definitions
Discrete Derivative Finite Difference
Derivatives in 2 Dimensions
Derivatives of Images
Correlation
Convolution
Averages
Gaussian Filter
Properties of Gaussian
Linear Filtering
Filtering Examples
Blurring Examples
Filtering Gaussian
Gaussian vs. Smoothing
Noise Filtering
MATLAB Functions
An Application
Edge Detection in Images
What is an Edge?
Detecting Discontinuities
Derivative in Two-Dimensions
Image Derivatives
Derivatives and Noise
Image Smoothing
Gaussian Smoothing (Examples)
Edge Detectors

Taught by

UCF CRCV

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