US 12,223,696 B2
Computer-implemented method for testing conformance between real and synthetic images for machine learning
Christoph Gladisch, Renningen (DE); Christian Heinzemann, Ludwigsburg (DE); and Matthias Woehrle, Bietigheim-Bissingen (DE)
Assigned to ROBERT BOSCH GMBH, Stuttgart (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Jan. 27, 2022, as Appl. No. 17/649,175.
Claims priority of application No. 10 2021 201 445.3 (DE), filed on Feb. 16, 2021.
Prior Publication US 2022/0262103 A1, Aug. 18, 2022
Int. Cl. G06V 10/776 (2022.01); G06V 10/774 (2022.01); G06V 10/98 (2022.01)
CPC G06V 10/776 (2022.01) [G06V 10/774 (2022.01); G06V 10/993 (2022.01)] 19 Claims
OG exemplary drawing
 
1. A computer-implemented method for testing a conformance between images generated by a synthetic image generator and images obtained from authentic visual data, comprising the following steps:
obtaining a computer vision model in an initial training state configured to perform a computer vision function;
obtaining a visual parameter set including a plurality of visual parameters;
generating a synthetic visual data set including synthetic visual data and corresponding groundtruth data according to the visual parameter set, and sampling an authentic visual data set including authentic images and corresponding groundtruth data according to the visual parameter set;
applying the computer vision model to the synthetic visual data set and the authentic visual data set, to obtain a predicted synthetic visual data set and a predicted authentic visual data set, respectively, and generating a plurality of synthetic and authentic performance scores over the plurality of visual parameters of the visual parameter set, wherein each of the plurality of synthetic and authentic performance scores is a comparison of an item of predicted synthetic visual data or predicted authentic visual data with a corresponding item of groundtruth data;
(i) generating a first sensitivity measure of the plurality of synthetic performance scores over the plurality of visual parameters, and generating a second sensitivity measure of the plurality of authentic performance scores over the plurality of visual parameters, or (ii) generating a combined sensitivity measure based on a difference between corresponding synthetic and authentic performance scores over the plurality of visual parameters; and
generating a conformance result defining the conformance between images generated by the synthetic image generator and images obtained from the authentic visual data at a same or similar visual parameter of the visual parameter set by (i) comparing the first sensitivity measure and the second sensitivity measure, or (ii) generating the conformance result based on the combined sensitivity measure.