Capture Useful Calibration Views
The calibration board fills only the middle of every photograph.
Capture Useful Calibration Views explained visually
The calibration board fills only the middle of every photograph. English captions are optional and start off. The complete transcript follows the written lesson.
What you will learn
The calibration board fills only the middle of every photograph.
A practical question
Someone photographs a calibration board five times without moving it. The solver receives five images, but they all tell nearly the same story. To calibrate our parking camera usefully, we need clear views with varied board positions and tilts.
The useful idea
Capture the pattern near the center and toward the edges, at several orientations, while keeping its corners sharp and visible. Edge views help constrain lens distortion. Blurry or partly hidden corners introduce errors; more files do not automatically fix poor measurements.
Work through the example
Show the collected frames as a contact sheet. Do the board corners cover the image area we care about? Are some views truly different? After solving, project points back onto each view and inspect which images disagree. If a particular capture is unreliable, identify why before quietly deleting it.
Limits and next step
Keep the board's real square dimensions and capture settings with the images. Next, we'll see how curved straight lines lead us to an estimate of lens distortion.
Worked example
Capture some tilted views with the board near the edges of the image.
Quick check: test your understanding
Try it yourself: In the situation described above, explain the method and one reason its output might be misleading.
Check the reasoning
Useful calibration views cover the frame, different orientations, and enough sharp detectable corners. Many nearly identical centered views add little evidence about edge distortion.
What is the main idea behind capture useful calibration views?
Useful calibration views cover the frame, different orientations, and enough sharp detectable corners.
How is it used in the example?
Capture some tilted views with the board near the edges of the image.
What limitation should be checked?
Many nearly identical centered views add little evidence about edge distortion.
Further reading
Programming Computer Vision with Python — Jan Erik Solem.
Original ToolsInVision teaching examples. Editorial approach · Suggest a correction
Accessible referenceLesson 21 transcript156 words · Video transcript · Matches the published narration and captionsOpen transcript
Someone photographs a calibration board five times without moving it. The solver receives five images, but they all tell nearly the same story. To calibrate our parking camera usefully, we need clear views with varied board positions and tilts.
Capture the pattern near the center and toward the edges, at several orientations, while keeping its corners sharp and visible. Edge views help constrain lens distortion. Blurry or partly hidden corners introduce errors; more files do not automatically fix poor measurements.
Show the collected frames as a contact sheet. Do the board corners cover the image area we care about? Are some views truly different? After solving, project points back onto each view and inspect which images disagree. If a particular capture is unreliable, identify why before quietly deleting it.
Keep the board's real square dimensions and capture settings with the images. Next, we'll see how curved straight lines lead us to an estimate of lens distortion.