1 / 6

Depth Estimation Through Disparity Analysis in Computer Vision

Explore the concept of disparity in computer vision to measure image depth based on pixel movement. Utilizing calibrated cameras, rectify images to improve accuracy by fine-tuning alignment and disparity calculations. Leverage sliding technique for dense correspondence analysis and extract 3D information from rectified images. Witness a visual demonstration showcasing the potential of disparity for depth estimation in computer vision.

hallie
Download Presentation

Depth Estimation Through Disparity Analysis in Computer Vision

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. Joshua Michalczak For UCF REU in Computer Vision, Summer 2010 Project Presentation – Week 8

  2. Last Week • Switched to calibrated camera model • Was having trouble rectifying images • Often off by up to 10 scanlines

  3. Rectification… FIX’D(and now in Technicolor)

  4. Disparity • Measuring the amount of movement (in pixels) between the two rectified images • Inversely proportional to the depth of the image (low disparity = far, high disparity = close) • We can do this by “sliding” the left image over the right image, subtracting, and saving the lowest (closest to zero) results • DEMO’D

  5. Disparity cont’d • Essentially, disparity will give us dense correspondence

  6. 3D DEMO’D • Noisy, but not terrible (the “essence” of the scene is captured)

More Related