对象识别我们ing Speeded-Up Robust Features (SURF) is composed of three steps: feature extraction, feature description, and feature matching. This example performs feature extraction, which is the first step of the SURF algorithm. The algorithm used here is based on the OpenSURF library implementation. This example shows how you can use GPU Coder™ to solve this compute intensive problem through CUDA® code generation.

Generate CUDA® MEX from MATLAB® code and perform feature matching between two images. This example uses the matchFeatures function from the Image Processing Toolbox™ to match the feature descriptors between two images that are rotated and scaled with respect to each other. The feature descriptors of the two images are detected and extracted by using the Speeded-Up Robust Features (SURF) algorithm.

Generate CUDA® MEX for a MATLAB® function that can detect and output lane marker boundaries on an image. The example takes an RGB image as input and uses the ordfilt2, hough, houghpeaks, and houghlines functions that are part of Image Processing Toolbox™ to produce the lane-detected output image.

Demonstrates edge detection in an image with a CUDA® MEX function generated from a MATLAB® function. The edge detection algorithm is implemented with half-precision data type.

为Simulink®模型生成CUDA®代码，该模型可万博1manbetx以在图像上检测和输出车道标记边界。此示例将RGB图像作为输入，并使用imresize,RGB2Gray,ordfilt2(Image Processing Toolbox),hough(Image Processing Toolbox),Houghpeaks（图像处理工具箱）和houghlines(Image Processing Toolbox) functions that are part of Image Processing Toolbox™ to detect lane markings. This example closely follows Lane Detection on the GPU by Using the houghlines Function.

A typical implementation of fog rectification algorithm. The example usesconv2,im2gray, andimhist(Image Processing Toolbox) functions. This example closely follows Fog Rectification example. This example illustrates the following concepts: Verification of GPU Environment.