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Lecture
Image Processing I: Edge Detection and Texture Analysis
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Related lectures (29)
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Image Processing I: Segmentation and Thresholding
Explores image segmentation, thresholding techniques, texture segmentation, and connected-component labeling in image processing.
Image Processing I: Discrete Histogram Processing and Spatial Operators
Explores discrete histogram processing and spatial operators for image manipulation and noise reduction.
Image Processing I
Covers fundamental concepts in image processing, including preprocessing techniques and matching methods.
Image Processing Basics
Covers the basics of image processing, focusing on writing a program to process images.
Image Processing: Enhancement and Analysis
Explores image processing techniques such as contrast manipulation, enhancement, and sharpening using Fiji software.
Segmentation: Theory and Algorithms
Covers the theory and algorithms behind image segmentation, focusing on region identification and evaluation.
Convolutional Neural Networks
Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.
Texture Analysis: Statistical and Structural Approaches
Discusses texture analysis in images, focusing on statistical and structural properties, segmentation techniques, and machine learning applications for texture classification.
Image Processing Techniques
Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.
