Discover sseshadr/auvsi-cv-motionEstimation Open Source project

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2
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MATLAB
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Other
Created over 8 years ago
Updated over 8 years ago

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Learn how we perceive motion and how to estimate motion using a technique called Optical Flow. You can use three algorithms to implement optical flow using the Computer Vision Toolbox. These three algorithms are Horn-Schunck method, Farneback method, and Lucas-Kanade method. An example of a robot boat moving through a field of buoys will be used.

auvsi-cv-pointCloud

Learn how to read, load and visualize point clouds using MATLAB and pre-process the data by down sampling and de-noising. You will also learn how to apply affine transforms like translation and rotation. Finally, you will learn how to fit point clouds to geometric shapes and how to extract a region of interest from images using point clouds.

MATLAB

auvsi-cv-stereoVision

Learn how to import and display stereo vision images. Also understand how to calibrate stereo cameras, rectify images to align them horizontally, generate disparity maps and create point clouds with scene reconstruction.

MATLAB

auvsi-cv-lineDetection

Learn how to detect lines using MATLAB. The concept of a Hough Transform is demonstrated to show how to use them to extract line segments. Tips on some preprocessing techniques are also provided to improve line detection results. An example of lane detection is used to explain these concepts.

MATLAB

auvsi-cv-objectTracking

Learn how to track an object across video frames. Object tracking using histogram based tracking, tracking occluded or hidden objects using a Kalman Filter, and multiple objects tracking are covered. An example of tracking a moving ball will be used.

MATLAB