Patent ID: 11967126
Assignee: ROBERT BOSCH GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer implemented method of anomaly detection, comprising the following steps:
processing a digital representation of a signal or image with a wavelet decomposition to generate a first plurality of decomposed representations;
processing a first decomposed representation of the first plurality of decomposed representations with a wavelet decomposition to generate a second plurality of decomposed representations;
processing a first decomposed representation of the second plurality of decomposed representations with a wavelet decomposition to generate a third plurality of decomposed representations;
processing a first decomposed representation of the third plurality of decomposed representations with a first generative model to determine a first likelihood;
processing at least one second decomposed representation of the third plurality of decomposed representations with a second generative model to determine a second likelihood;
processing at least one second decomposed representation of the second plurality of decomposed representations with a third generative model to determine a third likelihood;
processing at least one second decomposed representation of the first plurality of decomposed representations with a fourth generative model to determine a fourth likelihood; and
detecting an anomaly when a criterium for anomaly detection is met by at least one of: the first likelihood, the second likelihood, the third likelihood, and the fourth likelihood;
wherein the method includes at least one of the following features (a)-(f):
(a) the digital representation is composed of a two-dimensional set of data, and at least one of:
(i) each of the decomposed representations is of a resolution that is reduced compared to a resolution of the representation from which the respective decomposed representation has been decomposed by a division of the resolution of the representation from which the respective decomposed representation has been decomposed with a factor of four; and
(ii) each of the decompositions increases a number of the representations by a multiple of four;

(b) each of the likelihoods is a respective indication of a likelihood of a plurality of data points of the respective one or more of the decomposed representations for which the respective likelihood has been determined;
(c) each of at least one of the second, third, and fourth generative models is conditioned on at least one of the decomposed representations other than the respective representations used by the respective one of the second, third, and fourth generative models for the respective likelihood determination;
(d) the detecting of the anomaly includes determining a sum depending on the first likelihood, the second likelihood, the third likelihood, and the fourth likelihood, comparing the sum to a threshold in a comparison, and determining whether the anomaly is present depending on a result of the comparison;
(e) each pixel of a plurality of pixels defines at least one intensity value, and at least one of:
(i) the digital representation is defined by a set of the pixels, and the method comprises:
determining the first decomposed representation of the first plurality of decomposed representations depending on an average intensity value obtained by averaging intensity values of non-overlapping subsets of the set of pixels; and
determining at least one of the at least one second decomposed representation of the first plurality of decomposed representations depending on a difference between the intensity values of at least two different ones of the pixels;

(ii) the first decomposed representation of the first plurality of decomposed representations is defined by a set of the pixels, and the method comprises:
determining the first decomposed representation of the second plurality of decomposed representations depending on an average intensity value obtained by averaging intensity values of non-overlapping subsets of the set of pixels; and
determining the at least one second decomposed representation of the second plurality of decomposed representations depending on a difference between the intensity values of at least two different ones of the pixels; and

(iii) the first decomposed representation of the second plurality of decomposed representations is defined by a set of the pixels, and the method further comprises:
determining the first decomposed representation of the third plurality of decomposed representations depending on an average intensity value obtained by averaging intensity values of non-overlapping subsets of the set of pixels; and
determining the at least one second decomposed representation of the third plurality of decomposed representations depending on a difference between the intensity values of at least two different ones of the pixels; and

(f) the method further comprises:
processing a digital training representation of a signal or image with the wavelet decomposition to generate a first plurality of decomposed training representations;
processing a first decomposed training representation of the first plurality of decomposed training representations with the wavelet decomposition to generate a second plurality of decomposed training representations;
processing a first decomposed training representation of the second plurality of decomposed training representations with the wavelet decomposition to generate a third plurality of decomposed training representations;
determining first training data for the first generative model including a first decomposed training representation of the third plurality of decomposed training representations;
determining second training data for the second generative model including at least one second decomposed training representation of the third plurality of decomposed training representations;
determining third training data for the third generative model including at least one second decomposed training representation of the second plurality of decomposed training representations; and
determining fourth training data for the fourth generative model including at least one second decomposed training representation of the first plurality of decomposed training representations.