Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments

• URL: http://arxiv.org/abs/2309.08239v1
• Date: Fri, 15 Sep 2023 08:24:07 GMT
• Title: Human-Inspired Topological Representations for Visual Object Recognition in Unseen Environments
• Authors: Ekta U. Samani and Ashis G. Banerjee
• Abstract summary: We propose a shape-based TOPS2 descriptor and a THOR2 framework for visual object recognition. THOR2, trained using synthetic data, achieves substantially higher recognition accuracy than the shape-based THOR framework. THOR2 is a promising step toward achieving robust recognition in low-cost robots.
• Score: 2.356908851188234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual object recognition in unseen and cluttered indoor environments is a challenging problem for mobile robots. Toward this goal, we extend our previous work to propose the TOPS2 descriptor, and an accompanying recognition framework, THOR2, inspired by a human reasoning mechanism known as object unity. We interleave color embeddings obtained using the Mapper algorithm for topological soft clustering with the shape-based TOPS descriptor to obtain the TOPS2 descriptor. THOR2, trained using synthetic data, achieves substantially higher recognition accuracy than the shape-based THOR framework and outperforms RGB-D ViT on two real-world datasets: the benchmark OCID dataset and the UW-IS Occluded dataset. Therefore, THOR2 is a promising step toward achieving robust recognition in low-cost robots.

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