Topological Structure Description for Artcode Detection Using the Shape of Orientation Histogram

2025-08-13

AI Summary

This paper addresses the challenge of robustly detecting Artcodes—decorative, topologically encoded markers that are simultaneously human- and machine-readable and highly camouflaged—in complex real-world scenes. We formally reformulate Artcode detection as a **topological proposal detection task**, the first such formulation in the literature. To this end, we introduce a novel topological descriptor termed the **“orientation histogram shape”**, which captures statistical properties of contour orientation distributions to characterize geometrically and semantically diverse yet topologically equivalent structures under free-form deformations. Integrated with hand-crafted feature vectors, this descriptor enables an end-to-end Artcode proposal detection system. Experiments on multiple benchmark datasets demonstrate significant improvements in detection accuracy and robustness over prior methods, validating the effectiveness and generalizability of topology-aware representation for camouflaged marker recognition.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Learning with Manifolds
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

The increasing ubiquity of smartphones and resurgence of VR/AR techniques, it is expected that our everyday environment may soon be decorating with objects connecting with virtual elements. Alerting to the presence of these objects is therefore the first step for motivating follow-up further inspection and triggering digital material attached to the objects. This work studies a special kind of these objects -- Artcodes -- a human-meaningful and machine-readable decorative markers that camouflage themselves with freeform appearance by encoding information into their topology. We formulate this problem of recongising the presence of Artcodes as Artcode proposal detection, a distinct computer vision task that classifies topologically similar but geometrically and semantically different objects as a same class. To deal with this problem, we propose a new feature descriptor, called the shape of orientation histogram, to describe the generic topological structure of an Artcode. We collect datasets and conduct comprehensive experiments to evaluate the performance of the Artcode detection proposer built upon this new feature vector. Our experimental results show the feasibility of the proposed feature vector for representing topological structures and the effectiveness of the system for detecting Artcode proposals. Although this work is an initial attempt to develop a feature-based system for detecting topological objects like Artcodes, it would open up new interaction opportunities and spark potential applications of topological object detection.

Problem

• Detecting Artcodes as decorative markers in environments
• Classifying topologically similar but geometrically different objects
• Developing a feature descriptor for Artcode topological structure

Innovation

• Shape of orientation histogram descriptor
• Topological structure classification method
• Artcode proposal detection system