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Depth Estimation Through Disparity Analysis in Computer Vision

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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.

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Depth Estimation Through Disparity Analysis in Computer Vision

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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)

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