Explore MATLAB source code curated for "检测" with clean implementations, documentation, and examples.
Application Context: Applied to signal detection at the receiver end of communication MIMO systems. For received signals, detection and estimation are required to determine the estimated values of transmitted source symbols and compute error probabilities. Key Technologies: ZF detection algorithm and MMSE detection algorithm, implemented with matrix inversion and optimization techniques to enhance signal recovery accuracy.
Optical Flow-Based Vehicle Detection and Tracking for Traffic Analysis with Implementation Insights
Utilizing gray-scale integral projection for direct face detection and eye localization is a common algorithm, but its direct application can be affected by background noise and feature variations, resulting in lower recognition accuracy. We propose a detection and localization algorithm combining Otsu's thresholding method (maximum inter-class variance) and region dilation. The algorithm first calculates the Otsu threshold to binarize the gray-scale image and detect face regions, then performs dilation and connectivity processing on connected components within the face area to precisely locate eye coordinates. Experimental results demonstrate that this algorithm is reliable with improved recognition performance. Key implementation aspects include adaptive threshold calculation using Otsu's method and morphological operations for feature enhancement.
Detection of images with embedded watermarks using the previously uploaded watermarking algorithm add_score.m, which handles the watermark insertion process.
This resource provides comprehensive MATLAB source code for target recognition and detection applications, featuring automated license plate recognition systems complete with image processing algorithms and pattern recognition techniques.
Implementation of eye detection in facial regions using EyeMap and MouthMap algorithms on MATLAB platform with enhanced code-related descriptions
Implementation of digital watermark embedding and detection algorithm using DCT blocking and Human Visual System (HVS) characteristics, developed by Mao Li. This approach embeds watermarks into images through DCT coefficient modification and detects them using similarity comparison methods with enhanced visual perception considerations.
An effective edge detection method called CORNER for finding corners in intensity images. The algorithm operates through these key steps: 1) Apply Canny edge detector to grayscale image to generate binary edge map, 2) Extract edge contours from the edge map and fill gaps in contours. Implementation typically involves using edge detection functions like edge() with 'Canny' option and contour tracing algorithms.
This program is capable of detecting zebra crossings on roads by employing typical image edge detection and feature extraction techniques.
Implementation of SUSAN algorithm for precise eye corner detection in MATLAB environment with noise-resistant feature point identification