Machine Vision with Single Pixel Camera and Deep Learning based Signal Processing

Making a machine aware of its environment by leveraging optical metrology – Machine Vision – is a very active research area in light of the continued push for autonomy in technical systems. Within this research project, we developed a novel approach to Machine Vision.

Its defining features are that it works without areal optical sensors by leveraging methods from Compressed Sensing, that a Neural Network performs the data processing and that it directly extracts features like size and positioning of objects in a 3D space. The setup offers multiple advantages: It reduces hardware requirements for the data acquisition by simplifying the sensor and greatly lightens the processing side by directly returning relevant scene information, eliminating explicit image reconstruction. Thus, this approach belongs to the still sparsely covered area of image free methods.

Comparison between classical and image free machine vision.

We realize this based on a Single Pixel camera setup, which works by projecting structured dark-light-patterns onto a scene. A photodiode collects the diffusely reflected light, yielding a single intensity value. This intensity depends on the structure of both the masks and the objects in the scene, so by using multiple different masks, we encode the objects’ characteristics. Using appropriate data processing, in our case a specially trained Neural Net, we are able to reconstruct object parameters of interest directly.

Principle of the single-pixel camera.

Supported by:


This image shows Alexander  Birk

Alexander Birk


Research Assistant

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