The COTBLEDTCID approach to object detection and pose estimation, Part V – Circles detection

Introduction

Let’s do a summary of what we have done so far:

  • COT: colour thresholding. We separated yellow objects from the rest.
  • BLED: blob edge detection. We retrieved the bottom edges of blobs (pawns).
  • T: transformation. We transformed the image’s pixels into game field points (aka. pixels to meters).

And the last step CID: Circles Detection.

As you may have already noticed, pawns and tower of pawns are in fact circles when viewed from above. Therefore, the bottom edges we found with BLED are also circles’ segments when transformed into game field coordinates (step T). This is the property we’re exploiting below.

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The COTBLEDTCID approach to object detection and pose estimation, Part IV – Transformation

Introduction

With the last step we know where the bottom edges of the pawn are located on the image, we just need to find a way to transform the coordinates of those pixels into game field coordinates.

Given a point p' from the Image plane, we’d like to transform it into p from the Game field plane. We can write:

p = H \cdot p'

We observe that straight lines are kept straight, thus H is called the homography matrix which can be computed if at least 4 different matching points are given for both planes. (p1 \leftrightarrow p1', p2 \leftrightarrow p2', p3 \leftrightarrow p3', p4 \leftrightarrow p4')

HomographyIt’s worth noticing that both p and p' points are given in homogeneus coordinates.

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The COTBLEDTCID approach to object detection and pose estimation, Part III – Blob Edge Detection

Introduction

There is still too much information we do not need on the B&W image we got on the last step. That’s why we need to extract the features we do need. One way of accomplishing this is by performing a connected component analysis in binary images, aka blob labelling. However as you’ll will see, this method is not completely adapted to our needs, so a new approach is proposed: Blob Edge Detection.

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