Explore MATLAB source code curated for "图像检测" with clean implementations, documentation, and examples.
This process involves reading an image, performing binarization and other operations to identify all circles in the image, marking them with red circles, and displaying the processed image in a window. The implementation utilizes computer vision algorithms and library functions for accurate circle detection and annotation.
This MATLAB implementation performs keypoint detection on input images, identifying salient points that typically correspond to important objects within the image. These keypoints serve as robust features for object recognition and image classification tasks. The algorithm can be executed by running the demonstration script Ldx_GoSalScale.m (compatible with MATLAB 7.0 environment), which showcases practical usage examples and demonstrates the core detection methodology.
PCA demonstrates exceptional effectiveness in blind forensic applications for image detection
Quantize image colors into 72 distinct shades - an excellent tool for image retrieval applications with implementation insights.
The SURF operator (Speeded Up Robust Features) is a robust image detection and description algorithm. Partially inspired by the SIFT operator, standard SURF implementation runs several times faster than SIFT while demonstrating superior stability across various image transformations, using integral images and Haar wavelet responses for efficient feature computation.
Principal Component Analysis for Copy-Move Forgery Detection in Digital Images
PCA for Blind Forensic Analysis in Image Detection