Lensless-camera based machine learning for image classification

2017/09/03 by Ganghun Kim, Stefan Kapetanovic, Kim, Ganghun +5
Computer Science · Engineering · #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Image Processing Techniques and Applications #Optical Coherence Tomography Applications #Optics (physics.optics)

paper · pdf · doi:10.48550/arxiv.1709.00408

openalex publication_date 2017/09/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Machine learning (ML) has been widely applied to image classification. Here, we extend this application to data generated by a camera comprised of only a standard CMOS image sensor with no lens. We first created a database of lensless images of handwritten digits. Then, we trained a ML algorithm on this dataset. Finally, we demonstrated that the trained ML algorithm is able to classify the digits with accuracy as high as 99% for 2 digits. Our approach clearly demonstrates the potential for non-human cameras in machine-based decision-making scenarios.

Citations

• A survey of decision tree classifier methodology

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