Scientists at Brookhaven National Laboratory developed scanning Lidar equipment to detect toxic clouds but they needed application software to pinpoint the size and location of detected plumes. In this case study, MATLAB and the Image Processing Toolbox were used to analyze some raw scan data. Background was removed by ensemble median averaging and image subtraction. Clouds were segmented by statistical based thresholding. Detected clouds were visualized in 2 and 3 dimensions. In order to accurately determine plume dimensions, a model was developed to understand geometric distortions caused by the non-uniform polar coordinate system of the laser scanner. The model, which used spatial transformations, was first validated using a known, synthetic test image to ensure accuracy of the algorithm. The spatial transforms were then used to correct for geometric distortions in actual scan data. With the MATLAB code and example data in this package you can follow the steps used for this application.
Robert Bemis (2020). Lidar Imaging Case Study(with Geometric Distortion Correction) (https://www.mathworks.com/matlabcentral/fileexchange/2072-lidar-imaging-case-study-with-geometric-distortion-correction), MATLAB Central File Exchange. Retrieved .
i need to classify and filter lidar data using machine or deep learning can you help me
pls send me the mat lab coding for patches (ie,segment division)at boundary of an image
pls send me the mat lab coding & explanation for median background subtraction &
foreground graph cut method of an image
The original submission was only part of this larger case study