US 11,947,013 B2
Detector for identifying at least one material property
Friedrich Schick, Ludwigshafen (DE); Peter Schillen, Ludwigshafen (DE); Patrick Schindler, Ludwigshafen (DE); Andre Schmidt, Ludwigshafen (DE); Michael Eberspach, Ludwigshafen (DE); Christian Lennartz, Ludwigshafen (DE); Robert Send, Karlsruhe (DE); Lars Diesselberg, Karlsruhe (DE); Heiko Hengen, Steinweiler (DE); Ingmar Bruder, Ludwigshafen (DE); Jakob Unger, Ludwigshafen (DE); and Christian Bonsignore, Ludwigshafen (DE)
Assigned to TRINAMIX GMBH, Ludwigshafen am Rhein (DE)
Appl. No. 17/439,492
Filed by trinamiX GmbH, Ludwigshafen am Rhein (DE)
PCT Filed Mar. 13, 2020, PCT No. PCT/EP2020/056759
§ 371(c)(1), (2) Date Sep. 15, 2021,
PCT Pub. No. WO2020/187719, PCT Pub. Date Sep. 24, 2020.
Claims priority of application No. 19163250 (EP), filed on Mar. 15, 2019.
Prior Publication US 2022/0157044 A1, May 19, 2022
Int. Cl. G06T 7/521 (2017.01); G01B 11/22 (2006.01); G01C 21/16 (2006.01); G01S 17/46 (2006.01); G01S 17/66 (2006.01); G06T 5/00 (2006.01); G06T 5/20 (2006.01); G06T 7/20 (2017.01); G06T 7/73 (2017.01); G06V 10/145 (2022.01); G06V 10/60 (2022.01); G06V 10/764 (2022.01); H04N 23/56 (2023.01); H04N 23/74 (2023.01)
CPC G01S 17/46 (2013.01) [G01B 11/22 (2013.01); G01C 21/16 (2013.01); G01S 17/66 (2013.01); G06T 5/002 (2013.01); G06T 5/20 (2013.01); G06T 7/20 (2013.01); G06T 7/521 (2017.01); G06T 7/73 (2017.01); G06V 10/145 (2022.01); G06V 10/60 (2022.01); G06V 10/764 (2022.01); H04N 23/56 (2023.01); H04N 23/74 (2023.01)] 25 Claims
OG exemplary drawing
 
1. A detector for identifying at least one material property m comprising
at least one sensor element comprising a matrix of optical sensors, the optical sensors each having a light-sensitive area, wherein the sensor element is configured for recording at least one reflection image of a light beam originating from at least one object, and
at least one evaluation device configured for determining the material property by evaluation of at least one beam profile of the reflection image,
wherein the evaluation device is configured for determining at least one distance feature φ1z by applying at least one distance dependent image filter Φ1 to the reflection image, wherein the distance dependent image filter is at least one filter selected from the group consisting of: a depth-from-photon-ratio filter; a depth-from-defocus filter, or a linear combination thereof; and a further distance dependent image filter Φ1other which correlates to the depth-from-photon-ratio filter and/or the depth-from-defocus filter or a linear combination thereof by |ρΦ1other,Φz|≥0.40 with Oz being one of the depth-from-photon-ratio filter or the depth-from-defocus filter or a linear combination thereof,
wherein the evaluation device is configured for determining at least one material feature φ2m by applying at least one material dependent image filter Φ2 to the reflection image, wherein the material dependent image filter is at least one filter selected from the group consisting of a luminance filter; a spot shape filter; a squared norm gradient: a standard deviation; a smoothness filter; a Gaussian filter or median filter; a grey-level-occurrence-based contrast filter, a grey-level-occurrence-based energy filter, a grey-level-occurrence-based homogeneity filter; a grey-level-occurrence-based dissimilarity filter: a Law's energy filter; a threshold area filter; or a linear combination thereof, and a further material dependent image filter Φ2other which correlates to one or more of the luminance filter, the spot shape filter, the squared norm gradient, the standard deviation, the smoothness filter, the grey-level-occurrence-based energy filter, the grey-level-occurrence-based homogeneity filter, the grey-level-occurrence-based dissimilarity filter, the Law's energy filter, or the threshold area filter, or a linear combination thereof by |ρΦ2other,Φm|≥0.40 with Φm being one of the luminance filter, the spot shape filter, the squared norm gradient, the standard deviation, the smoothness filter, the grey-level-occurrence-based energy filter, the grey-level-occurrence-based homogeneity filter, the grey-level-occurrence-based dissimilarity filter, the Law's energy filter, or the threshold area filter, or a linear combination thereof, and
wherein the evaluation device is configured for determining a longitudinal coordinate z and the material property m by evaluating the distance feature φ1z and the material feature φ2m.