Image Processing I

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Related lectures (32)

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Numerical Methods: Boundary Value Problems

Covers numerical methods for solving boundary value problems, including applications with the Fast Fourier transform (FFT) and de-noising data.

Frequency Estimation (Theory)

Covers the theory of numerical methods for frequency estimation on deterministic signals, including Fourier series and transform, Discrete Fourier transform, and the Sampling theorem.

Fourier Filtering

Explores Fourier filtering principles, applications in image processing, and efficient FFT implementation.

Fourier Analysis: Fourier Series and Transform

Explores Fourier series, DFT, FFT, and their applications in signal processing and spectroscopy.

The Fourier Transform

Introduces the basics of the Fourier Transform, covering concepts like resolution, periodicity, 2D transforms, amplitude, phase, and convolution.

Fourier Series and Analysis

Covers Fourier series, analysis, the heat equation, Gibbs phenomenon, and Fourier transform properties.

Texture: Analysis and Classification

Explores the analysis and classification of texture in images, emphasizing the role of Machine Learning techniques like Convolutional Neural Networks.

Fourier Transform: Analysis IV

Covers the Fourier transform in Analysis IV, discussing Fourier series and solutions for various problems.

Spectrogram Analysis: Psychoacoustic Foundations & Signal Processing

Explores spectrogram analysis, including DFT, STFT, and CQT, for tasks like music transcription and onset detection.

Fast Fourier Transform (FFT): Basics and Applications

Covers the Fast Fourier Transform (FFT) algorithm and its applications in computational physics, including image processing, experimental techniques, filters, and analysis of microscopy images.