In 1954, Fred Attneave showed a line drawing of his cat. He argued that object shape information concentrates around high curvature locations or corners, hence efficiently captured in a piecewise line representation.

Attneave's Cat (1954)

A line representation is particularly attractive for articulated objects, such as people, where individual limbs can be roughly modeled as parallel lines. In comparison to using points, a line representation is more compact and is better at capturing the configuration of parts.

Of course, lines are harder to work with than points, as they are more abstract and are not well-defined physically. To use lines for recognition, we need to answer two questions:

• How to construct a line representation?

  We construct lines from bottom-up using edge detection and constrained Delaunay triangulation, as in our ICCV '05 paper on contour grouping.

• How to match lines to lines?

  A line by itself is non-discriminative; shape exists only in the way lines are posed w.r.t. one another. Imagine matching a template (a set of lines) to a test image (also a set of lines). If a pair of lines in the template are adjacent (or parallel), then they should match to a pair of lines in the image that are adjacent (or parallel). We define shape using such relative geometric configurations between all pairs of lines. The matching can be solved as quadratic programming.

  One challenge in line matching is that there is always a certain amount of arbitrariness how we break up contours into line segments. We accommodate this in our framework by allowing "fractional" matching between lines.

10 line aspects we learn from a skating video sequence. A line representation is more efficient than a point-based representation, hence covering a large variety of poses with a small set of exemplars.

Sample results using these templates for single image detection:

Image

Edges

Lines

Matched lines

Template used