Explore how multiscale edges unlock practical insights in image processing.
This book explains how edges detected at multiple scales relate to wavelet transforms and how these ideas underpin modern edge analysis. You’ll see how the Canny edge detector emerges from the study of wavelet transform modulus maxima and how smoothing at different scales reveals sharp transitions in images.
Two concise sections show theory and practice. First, it ties edge detection to the evolution of wavelet representations, detailing how local maxima and zero-crossings identify meaningful edges. Second, it extends these ideas to two dimensions, describing how gradient direction, modulus, and edge points are extracted from images and how this leads to robust image analysis and reconstruction. The text also discusses practical applications, including compact image coding and noise removal, with numerical results that illustrate performance.
What you’ll experience
- A clear link between Canny edge detection and multiscale wavelet analysis
- How edge features are characterized across scales using modulus maxima
- A practical approach to reconstructing signals and denoising from multiscale edges
- Extensions to two-dimensional images and real-world applications like image coding
Ideal for readers of signal processing, computer vision, and pattern recognition who want a concrete, applicable view of multiscale edge analysis.