Image Reconstruction Using Filtered Back Projection Method
Implementation of image reconstruction through filtered back projection method with configurable filtering and interpolation options for optimal results.
Fractional Fourier Transform Filtering Program
The Fractional Fourier Transform (FRFT) serves as a generalized form of the Fourier transform, exhibiting excellent energy concentration properties for Linear Frequency Modulated (LFM) signals where both time and frequency domains can be considered special cases of the FRFT domain. As illustrated in Figure 2, LFM signal projections across different transform domains show energy distributed widely in frequency domain while converging to an impulse function in the appropriate fractional Fourier domain. Being a linear transformation, the FRFT of a signal-noise mixture equals the superposition of their individual FRFTs. These properties enable effective LFM signal filtering in the FRFT domain. Key implementation involves scanning through rotation angles to compute fractional Fourier transforms of observed signals, generating two-dimensional energy distributions in parameter space for LFM detection with unknown parameters.
ECG Signal Filtering and Feature Value Extraction
Process ECG signals to extract feature values and useful information through signal processing algorithms
FIR Hamming Window Filter and IIR Bilinear Transform Method for Low-Pass Filter Design
FIR Hamming Window Filter and IIR Bilinear Transform Method for Low-Pass Filter Design, Applied to ECG Signal Filtering
Digital Image Filtering Using Wiener Adaptive Filter
Implementation Guide: This program has been successfully debugged in MATLAB 6.5. Copy all files from the "Program" directory to MATLAB's "work" directory, then type "WienerFilter" in the MATLAB command window and press Enter to execute. The implementation demonstrates adaptive noise reduction through Wiener filtering algorithm.
Video Surveillance with Background Subtraction, Filtering, Binarization, and Recognition Technologies
This code implements intelligent video/visual surveillance with an intuitive user interface. It captures live video streams from computer-connected cameras and processes images using background subtraction, filtering, binarization, and recognition techniques to monitor targets, with OpenCV integration for real-time processing capabilities.
Research on Human Brain Image Segmentation Technology
This research explores human brain image segmentation through a three-stage computational approach: a) Filtering raw 2D medical images and reconstructing them into 3D volumetric data structures. b) Implementing multi-threshold segmentation algorithms to determine optimal dual thresholds for binarizing 3D volumes, extracting brain tissue regions. c) Applying mathematical morphology operations and seed-filling algorithms for region refinement, ultimately achieving high-quality 3D brain segmentation with volumetric visualization capabilities.
Computation of Nonlinear Parameters for EEG Signals
Calculation of EEG signal nonlinear parameters, ECG signal analysis and filtering, and baseline drift removal
Filtering Data from a MAT File Followed by Singular Value Decomposition Denoising
Filtering data from a MAT file followed by denoising using Singular Value Decomposition. The SVD denoising methodology references literature provided in the attachment, with enhanced descriptions of code implementation approaches and key algorithmic steps.
Harmonic Wavelet Extraction of Specific Frequency Signals
Designed for extracting specific frequency signals using harmonic wavelets, with additional applications in filtering, noise reduction, and spectral analysis.