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Greyscale, Color and Special Images
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Related lectures (30)
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Image Processing Fundamentals
Covers scientific image processing fundamentals, software practices, and ethical considerations in image processing.
Image Formation: Understanding 2D Projections from 3D Scenes
Explains the principles of image formation, covering light projection, digitization, and the pinhole camera model.
Image Acquisition
Covers the basics of image acquisition, including optical devices, resolution factors, lens distortions, and sensor technologies.
Image Processing: Practical
Covers practical image processing using Fiji software, emphasizing data quality importance.
Reconstructing Color Images: Techniques and Applications
Covers techniques for reconstructing color images using optical detection and artificial intelligence to enhance image quality and reduce noise.
Image Processing Basics
Introduces image processing basics in Python, covering manipulation, grayscale conversion, edge detection, and convolution with kernels.
Delineation: Techniques and Applications
Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.
Detecting Objects: Independent Channels
Emphasizes the need for independent channels when detecting objects in images.
Image Processing I: Discrete Histogram Processing and Spatial Operators
Explores discrete histogram processing and spatial operators for image manipulation and noise reduction.
Image Processing: Smoothing and Filtering
Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.
