A Dual Approach for Machine-Awareness in Indoor Environment Combining Pseudo-3D Imaging and Soft-computing Techniques

Kurosh Madani 1 D. Hassan C. Sabourin 1
1 SYNAPSE
LISSI - Laboratoire Images, Signaux et Systèmes Intelligents
Abstract : 3D spatial characterization of objects is a key-step for autonomous knowledge extraction leading to machine-awareness. In this paper we describe a dual-images-based approach for objects’ 3D characterization and localization for Machine-Awareness in indoor environment. The so-called dual images are provided by color and depth cameras of Kinect system, which presents an appealing potential for 3D objects modeling and localization. Placing the human–machine (including human-robot) interaction as a primary outcome of the intended visual Machine-Awareness in investigated system, we aspire proffering the machine spatial awareness about its surrounding environment with identification and semantically description of relative positions of detected items in that environment. In this research work, the aforementioned pseudo-3D imaging and Soft-computing techniques are combined in order to extract, to recognize and to spatially characterize objects and the distance between objects in 3D environment. In other words, on the one hand, a pseudo-3D object modeling and localization method based on the dual-images of Kinect system is proposed, in which the computation of object’s spatial characterization is presented. On the other hand, a computational semantically description algorithm by using Fuzzy Inference System is proposed, in which the numerical-to-semantic conversion for machine awareness is accomplished. Experimental results validating the investigated approach are reported and discussed.
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Submitted on : Tuesday, July 25, 2017 - 11:43:05 AM
Last modification on : Friday, February 15, 2019 - 9:37:24 AM

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Kurosh Madani, D. Hassan, C. Sabourin. A Dual Approach for Machine-Awareness in Indoor Environment Combining Pseudo-3D Imaging and Soft-computing Techniques. International Journal of Machine Learning and Cybernetics, Springer, 2017, 8 (6), pp.1795-1814. ⟨10.1007/s13042-016-0559-2⟩. ⟨hal-01568406⟩

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