Abstract
We present PTPG (Poisson triangular pattern generator), a method that generates and optimizes triangular patterns for tokens on tangible surfaces. PTPG uses spatial information of markers to form triangular patterns and processes a template-matching algorithm to identify patterns. We introduce Poisson disk sampling to search patterns in the feature vector space and set sampling constraints to obtain patterns that meet the requirements of size, shape, and pattern distance. The patterns optimized by a shape constraint address the marker-loss problem caused by a common design of capacitive tokens, and therefore improve the recognition rate. We manufacture ten sets of prototype tokens based on the patterns generated by Poisson sampling and test these tokens on different devices. Experimental results show that our method provides more unique patterns than other heuristic methods, and the patterns are applicable to different tangible surfaces with high recognition rates.
Highlights
Tangible User Interfaces (TUIs) enable users to interact with digital content by manipulating physical objects [1], [2]
PTPG uses the spatial information of three marker points to construct a triangular pattern, and introduces Poisson disk sampling to search for a maximal number of patterns in pattern space
By using sample constraints to optimize the shape of patterns, we addressed the marker-loss problem in a common design of capacitive tokens, and improved the recognition rates to 100% on the tested capacitive screens
Summary
Tangible User Interfaces (TUIs) enable users to interact with digital content by manipulating physical objects [1], [2]. The limited touchpoints resulted in an insufficient number of unique patterns on capacitive devices. Our goal is to present a general pattern design, which applies to a variety of tangible systems with a high. Z. Huang et al.: PTPG: Poisson Triangular Pattern Generator for Tokens on Tangible Surfaces recognition rate and a large number of unique IDs. PTPG uses the spatial information of three marker points to construct a triangular pattern, and introduces Poisson disk sampling to search for a maximal number of patterns in pattern space. This paper makes the following contributions: (1) Present a triangular pattern design suitable for various tangible screens, and a light-weight and robust recognition algorithm. (2) Introduce a novel Poisson-disk-sampling-based method to generate a maximal number of patterns in the feature vector space, and improve the recognition rate of patterns by optimizing their shapes. We report the interactive experiences in three applications, summarize the limitations of our method and provide a future research plan
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