u/Fresh_Library_1934

Hey guys, I’m doing camera calibration using Zhang's method with a checkerboard and around 30 images. The results I’m getting are pretty random. Sometimes the error is around 1.5 px, and sometimes it goes up to 3 px.

Board flatness: My calibration board is not perfectly flat; it has some small ups and downs (varied depths) across the plane. Does this small change in depth mess up the results?

Barrel distortion: On both sides of the camera near the edges, there is more barrel distortion as the R value increases. Is this why the error is high?

Reproducibility: I can't reproduce the same error even if I pick the same number of images (30). The values keep shifting.

Low image count: Sometimes, if I calibrate with very few images (like 4-5), the undistorted image looks okay in the middle, but the edges and sides get totally warped or "wobbled."

Error increases with distance: I noticed that when the checkerboard is near the camera, the error is low (around 0.5px). But as I move the board back along the axis, the error shoots up to 2.2px in a single jump. Why does the error increase so much just by moving the board further away?

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u/Fresh_Library_1934 — 15 days ago

hey guys follow up to my post yesterday here is the entire Selective Search algo built with numpy

actually the output i showed yesterday was only step 1 using FH algo to get the initial base segments of the image

now i added step 2 which is the iterative merging process .. it loops and merges those base segments based on similarities considering ( Histograms of gradients colors etc ) to generate the final bounding box proposals !!

u/Fresh_Library_1934 — 23 days ago

Hey guys, it's been a while since I posted here!

Here is what I got while implementing the Felzenszwalb-Huttenlocher algorithm for region proposals in RCNN's .

I'm currently only considering pixel colour, but I plan to extend this further : )

u/Fresh_Library_1934 — 24 days ago