Direct Object Recognition Without Line-of-Sight Using Optical Coherence

2019/03/18 by Xin Lei, Lei, Xin, Liangyu He +13
Engineering · Physics and Astronomy · #Advanced Optical Sensing Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Optical Coherence Tomography Applications #Optics (physics.optics) #Random lasers and scattering media

paper · pdf · doi:10.48550/arxiv.1903.07705

openalex publication_date 2019/03/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Visual object recognition under situations in which the direct line-of-sight is blocked, such as when it is occluded around the corner, is of practical importance in a wide range of applications. With coherent illumination, the light scattered from diffusive walls forms speckle patterns that contain information of the hidden object. It is possible to realize non-line-of-sight (NLOS) recognition with these speckle patterns. We introduce a novel approach based on speckle pattern recognition with deep neural network, which is simpler and more robust than other NLOS recognition methods. Simulations and experiments are performed to verify the feasibility and performance of this approach.

Citations

• Non-invasive real-time imaging through scattering layers and around corners via speckle correlations
• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
• Rethinking Atrous Convolution for Semantic Image Segmentation

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