Sensor agnostic object recognition using a map seeking circuit

Timothy L. Overman; Michael Hart

doi:10.1117/12.917640

2 May 2012 Sensor agnostic object recognition using a map seeking circuit

Timothy L. Overman, Michael Hart

Proceedings Volume 8391, Automatic Target Recognition XXII; 83910N (2012) https://doi.org/10.1117/12.917640
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Automatic object recognition capabilities are traditionally tuned to exploit the specific sensing modality they were designed to. Their successes (and shortcomings) are tied to object segmentation from the background, they typically require highly skilled personnel to train them, and they become cumbersome with the introduction of new objects. In this paper we describe a sensor independent algorithm based on the biologically inspired technology of map seeking circuits (MSC) which overcomes many of these obstacles. In particular, the MSC concept offers transparency in object recognition from a common interface to all sensor types, analogous to a USB device. It also provides a common core framework that is independent of the sensor and expandable to support high dimensionality decision spaces. Ease in training is assured by using commercially available 3D models from the video game community. The search time remains linear no matter how many objects are introduced, ensuring rapid object recognition. Here, we report results of an MSC algorithm applied to object recognition and pose estimation from high range resolution radar (1D), electrooptical imagery (2D), and LIDAR point clouds (3D) separately. By abstracting the sensor phenomenology from the underlying a prior knowledge base, MSC shows promise as an easily adaptable tool for incorporating additional sensor inputs.

Citation Download Citation

Timothy L. Overman and Michael Hart "Sensor agnostic object recognition using a map seeking circuit", Proc. SPIE 8391, Automatic Target Recognition XXII, 83910N (2 May 2012); https://doi.org/10.1117/12.917640

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