Grouping and Recognition of Dot Patterns with Straight Offset Polygons

TL;DR

This paper presents a novel algorithm for grouping and recognizing dot patterns by transforming initial connections into straight polygons, effectively mimicking human object recognition even with distracting dots.

Contribution

The paper introduces a new algorithm that groups dots into polygons using straight polygon transformation, enabling computer recognition of objects from isolated dot patterns.

Findings

Effective grouping of dots into polygons demonstrated on natural and synthetic images.

Algorithm robust to distracting dots around the object.

O(n) complexity for polygon generation.

Abstract

When the boundary of a familiar object is shown by a series of isolated dots, humans can often recognize the object with ease. This ability can be sustained with addition of distracting dots around the object. However, such capability has not been reproduced algorithmically on computers. We introduce a new algorithm that groups a set of dots into multiple non-disjoint subsets. It connects the dots into a spanning tree using the proximity cue. It then applies the straight polygon transformation to an initial polygon derived from the spanning tree. The straight polygon divides the space into polygons recursively and each polygon can be viewed as grouping of a subset of the dots. The number of polygons generated is O($n$). We also introduce simple shape selection and recognition algorithms that can be applied to the grouping result. We used both natural and synthetic images to show effectiveness of these algorithms.