Images and Signal Foundations
Sampling, quantization, color, and linear filtering that turn light into arrays and arrays into structure.
Key Terms
SamplingQuantizationColor SpacesConvolutionImage GradientsGaussian Pyramid
Python Tools
OpenCVNumPySciPyMatplotlib
Textbook Reference
Szeliski Ch.3
Topics Covered
- 1Image formation, sampling, aliasing, and the Nyquist criterion
- 2Quantization, dynamic range, and noise models
- 3Color representation: RGB, HSV, and linear versus gamma-encoded intensity
- 4Linear filtering and convolution: kernels, separability, and boundary handling
- 5Image gradients, derivative-of-Gaussian filters, and scale via Gaussian pyramids
Learning Outcomes
- Explain sampling and quantization and predict aliasing artifacts
- Apply convolution with common kernels and reason about separability and borders
- Compute image gradients and build a Gaussian scale pyramid
Lecture PDFs
Images and Signal Foundations
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