Affiliation:
1. Department of Electronic and Electrical Engineering, The University of Sheffield, Sheffield, United Kingdom
Abstract
An efficient and accurate shape detection model plays a major role in many research areas. With the emergence of more complex shapes in real-life applications, shape recognition models need to capture the structure with more effective features to achieve high accuracy rates for shape recognition. This article presents a new method for 2D/3D shape recognition based on graph spectral domain handcrafted features, which are formulated by exploiting both an outline and a skeleton shape through the global outline and internal details. A fully connected graph is generated over the shape outline to capture the global outline representation while a hierarchically clustered graph with adaptive connectivity is formed on the skeleton to capture the structural descriptions of the shape. We demonstrate the ability of the Fiedler vector to provide the graph partitioning of the skeleton graph. The performance evaluation demonstrates the efficiency of the proposed method compared to state-of-the-art studies with increments of 4.09%, 2.2%, and 14.02% for 2D static hand gestures, 2D shapes, and 3D shapes, respectively.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture
Reference72 articles.
1. Extracting the principal shape components via convex programming;Aghasi A.;IEEE Trans. Image Process.,2018
2. Improving skeletal shape abstraction using multiple optimal solutions
3. Graph Spectral Domain Feature Learning With Application to in-Air Hand-Drawn Number and Shape Recognition
4. B. Alwaely and C. Abhayaratne. 2019. Graph spectral domain features for static hand gesture recognition. In Proceedings of the European Signal Processing Conference (EUSIPCO’19). 1–5.
5. AGSF: Adaptive graph formulation and handcrafted graph spectral features for shape representation;Alwaely B.;IEEE Access,2020
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1 articles.
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