Edge detection

Related lectures (30)

Discusses edge detection techniques and the evolution of deep learning in neural networks.

Covers the potential and limits of automatic vectorization techniques for digitizing objects from scanned documents or images.

Delves into choosing a proper feature size for image analysis in life sciences, presenting a Rule Of Thumb for defining object size in pixels.

The lecture discusses the main types of automatic vectorization techniques.

Emphasizes the need for independent channels when detecting objects in images.

Introduces image processing techniques in Python, focusing on manipulation and convolution operations using NumPy and Pillow.

Explores automation techniques for vectorizing objects using polygonization, edge detection, segment detection, and automatic tracing.

Covers fundamental principles, opportunities, and challenges in machine learning.

Covers techniques for reconstructing color images using optical detection and artificial intelligence to enhance image quality and reduce noise.

Explores topology concepts and edge detection in computer vision, highlighting the significance of contours and gradients in image analysis.

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