1 / 5

Stereo Vision Project III

Stereo Vision Project III. By: Rob Gilliland Class: ECE563 Image Processing Due: 4/2/2007. Input Images. Magnitude of the Motion Vector and Optical Flow. Bmaoflow3 inputs: Block size = 11 max x = 10, max y = 1. Set up conditions: images were taken from a camera 10 cm apart

solange
Download Presentation

Stereo Vision Project III

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Stereo Vision Project III By: Rob Gilliland Class: ECE563 Image Processing Due: 4/2/2007

  2. Input Images

  3. Magnitude of the Motion Vector and Optical Flow Bmaoflow3 inputs: Block size = 11 max x = 10, max y = 1 Set up conditions: images were taken from a camera 10 cm apart wine bottle on left 72 cm from camera, cup on right 112 cm from camera

  4. Magnitude of the Motion Vector and Optical Flow Comments • Notice the magnitude to the motion vector is highest for the wine bottle as expected because it is the closest. • The cup on the right has the next highest magnitude of the motion vector. • The large amount of noise in the background is due to the blanket being imaged not having enough variation.

  5. Optical Flow Estimation • Optical Flow Estimation was done with the ‘bmaoflow3.m’ function which computes block matching optical flow estimation. • Block matching does an exhaustive search over one image with a block of pixels for each pixel in another image. • The minimum error in both the x and y direction is then recorded and can be used to estimate the depth of each pixel.

More Related