MATLAB Implementation of Harris Corner Detection with Image Stitching
Implementation of Harris corner detection algorithm for feature point extraction, feature matching between two images, and final image stitching using MATLAB
Fingerprint Recognition Using Feature Point Matching
Several fingerprint matching techniques exist, with the most widely used being: Correlation-based matching, which computes similarity across different pixel paths and rotations but suffers from high computational complexity. Feature point-based matching, the most popular technique, identifies and maps minutiae points for comparison, yet faces challenges with low-quality fingerprints. Pattern-based (image-based) matching compares fingerprint patterns (arches, whorls, loops) between templates and candidate images but requires precise alignment. Implementation considerations include algorithmic efficiency, preprocessing requirements, and reliability under varying image quality conditions.
Image Feature Point Matching Implementation with Harris Corner Detection and SSD Algorithm
This program implements feature point matching between two images using a three-stage approach: first detecting corners with Harris corner detection algorithm, then filtering matching points using Sum of Squared Differences (SSD) algorithm, and finally connecting matched points with lines for visualization. The implementation includes parameter tuning capabilities for optimizing matching accuracy.
SIFT Feature Point Extraction Code
SIFT feature point extraction code with feature matching between two images, suitable for applications like image stitching. Implementation includes keypoint detection, descriptor computation, and matching algorithms.
Image Feature Point Matching Implementation
MATLAB program by David Lowe for image feature point matching, featuring keypoint detection and feature descriptor algorithms.
Fingerprint Recognition Technology
The general process of fingerprint recognition involves fingerprint acquisition, preprocessing, feature extraction, and feature matching. Fingerprints are categorized into three main types: whorl, arch, and loop. Image preprocessing plays a critical role since its quality directly impacts subsequent feature extraction and recognition accuracy. Code implementation typically includes noise reduction algorithms, filtering techniques, and image enhancement operations.