Deep learning based compressed sensing in machine vision: an iterative approach to multi object detection

A. Birk; K. Frenner; W. Osten

doi:10.1117/12.2683929

7 June 2023 Deep learning based compressed sensing in machine vision: an iterative approach to multi object detection

A. Birk, K. Frenner, W. Osten

Proceedings Volume 12701, Fifteenth International Conference on Machine Vision (ICMV 2022); 1270109 (2023) https://doi.org/10.1117/12.2683929
Event: Fifteenth International Conference on Machine Vision (ICMV 2022), 2022, Rome, Italy

Abstract

The use of Compressed Sensing (CS) and Deep Learning (DL) techniques in Machine Vision (MV) is an area of significant research interest. It is especially promising in the area of image free MV, where one deliberately skips the image reconstruction step and performs the information extraction directly on a set of highly compressed raw data, as provided e.g. through CS schemes. These approaches tend to perform well on simplified data sets where there is a single object to detect with otherwise little background. To make them useful in practice, they need to be able to detect multiple objects from this raw data. In this work, we present an expansion to our own DL based detection scheme that satisfies this condition. Its defining feature is that it works without requiring extra data acquisition steps compared to the single object case. We will discuss trainability and robustness aspects as well as the mathematical background that enables the concept. Finally, we will show this implementation in action.

Citation Download Citation

A. Birk, K. Frenner, and W. Osten "Deep learning based compressed sensing in machine vision: an iterative approach to multi object detection", Proc. SPIE 12701, Fifteenth International Conference on Machine Vision (ICMV 2022), 1270109 (7 June 2023); https://doi.org/10.1117/12.2683929

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available