Wavelet-Based Edge Detection Implementation in MATLAB

Edge detection in images is a crucial technique in the field of computer vision. By performing edge detection on images, we can extract contour information of objects within the image, providing a foundation for subsequent image processing and analysis tasks. In this experiment, we utilize the 'women' sample image as our test dataset for edge detection implementation. The implementation employs wavelet transform methods, which involve decomposing the image into different frequency components using functions like wavedec2() for 2D discrete wavelet decomposition. Key steps include: - Applying wavelet filters to detect high-frequency components where edges typically occur - Using edge detection algorithms that leverage wavelet coefficients at multiple scales - Implementing thresholding techniques on wavelet coefficients to distinguish significant edges from noise Through comprehensive code annotations, the edge detection process becomes more accessible and understandable. Edge detection represents one of the most common operations in image processing, enabling us to better understand and analyze structural features within images. Therefore, research and exploration of image edge detection techniques hold significant importance in computer vision applications. This experiment aims to provide deeper understanding and practical application of wavelet-based edge detection technology, featuring: - Demonstration of multi-resolution analysis capabilities of wavelet transforms - Comparison of different wavelet families (Haar, Daubechies, etc.) for edge detection performance - Implementation of post-processing steps to connect broken edge segments and remove false positives By studying this implementation, users can gain insights into how wavelet coefficients correspond to edge information and how to optimize parameters for different image characteristics.