CSE445Computer Vision
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M01CO1CO2

Introduction, Image Foundations and the Vision Pipeline

Frames computer vision as an ambiguous inverse problem with a wide application footprint, walks the task family and the end-to-end pipeline, then builds the image-signal foundations (sampling, color, convolution, gradients, scale) and the linear-algebra, calculus, and probability bridge used for the rest of the course.

Explain why vision is ill-posed and how benchmark results differ from deployment
Classify vision tasks and trace the stages of an end-to-end pipeline
Apply sampling, convolution, gradients, and scale-space to images
Use SVD, least squares, Taylor expansion, and probability as course tools
OpenCVNumPySciPyMatplotlib

Lectures in this Module(4)

L01

Vision Problem, Applications, and Society

What vision must recover, why it is ambiguous, and how benchmark success differs from deployed reliability.

L02

Tasks and the End-to-End Pipeline

The canonical task family and the stages every vision system moves through, with a first look at evaluation.

L03

Images and Signal Foundations

Sampling, quantization, color, and linear filtering that turn light into arrays and arrays into structure.

L04

Mathematics Bridge

The linear algebra, calculus, and probability used throughout the course, with attention to units and conditioning.

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