A General Ambiguity Model for Binary Edge Images with Edge Tracing and its Implementation

TL;DR

This paper introduces a versatile ambiguity model for binary edge images that improves edge tracing and junction analysis, aiding tasks like segmentation and recognition with a simple, efficient algorithm.

Contribution

The paper presents a novel, intuitive ambiguity model combined with a recursive edge tracing algorithm, implemented in less than 50 lines of pseudocode, enhancing image analysis tasks.

Findings

Efficient edge tracing with less than 50 lines of pseudocode.

Improved handling of junction ambiguities in binary edge images.

Versatile preprocessing for segmentation and recognition tasks.

Abstract

We present a general and intuitive ambiguity model for intersections, junctions and other structures in binary edge images. The model is combined with edge tracing, where edges are ordered sequences of connected pixels. The objective is to provide a versatile preprocessing method for tasks such as figure-ground segmentation, object recognition, topological analysis, etc. By using only a small set of straightforward principles, the results are intuitive to describe. This helps to implement subsequent processing steps, such as resolving ambiguous edge connections at junctions. By using an augmented edge map, neighboring edges can be directly accessed using quick local search operations. The edge tracing uses recursion, which leads to compact programming code. We explain our algorithm using pseudocode, compare it with related methods, and show how simple modular postprocessing steps can be used to optimize the results. The complete algorithm, including all data structures, requires less than 50 lines of pseudocode. We also provide a C++ implementation of our method.