Segmentation: Theory and Algorithms

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Related lectures (31)

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Image Segmentation: K-means and Color Spaces

Explores image segmentation using K-means clustering and discusses the impact of different color spaces.

K-Means Clustering: Basics and Applications

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

K-means Clustering: Lloyd's Algorithm and RGB Space

Explains K-means clustering with Lloyd's algorithm and RGB space for color segmentation.

K-means Clustering: Initialization and Image Segmentation

Explores k-means clustering, emphasizing initialization and image segmentation.

Graph Coloring: Random vs Symmetrical

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.

High Dynamic Range Imaging: Algorithms and Techniques

Explores High Dynamic Range imaging, explaining how cameras stack multiple exposed pictures to create HDR images.

Clustering: K-means & LDA

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Segmentation Techniques

Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.