- Patent Title: Methods for generating a deep neural net and for localising an object in an input image, deep neural net, computer program product, and computer-readable storage medium
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Application No.: US17279087Application Date: 2019-08-28
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Publication No.: US11900646B2Publication Date: 2024-02-13
- Inventor: Peter Amon , Sanjukta Ghosh , Andreas Hutter
- Applicant: SIEMENS AKTIENGESELLSCHAFT
- Applicant Address: DE Munich
- Assignee: Siemens Aktiengesellschaft
- Current Assignee: Siemens Aktiengesellschaft
- Current Assignee Address: DE Munich
- Agency: Lempia Summerfield Katz LLC
- Priority: EP 196304 2018.09.24
- International Application: PCT/EP2019/072960 2019.08.28
- International Announcement: WO2020/064253A 2020.04.02
- Date entered country: 2021-03-23
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06V10/44 ; G06T7/10 ; G06T7/70 ; G06N3/08 ; G06V10/772 ; G06V10/82 ; G06V20/52

Abstract:
Methods for generating a deep neural net and for localizing an object in an input image, the deep neural net, a corresponding computer program product, and a corresponding computer-readable storage medium are provided. A discriminative counting model is trained to classify images according to a number of objects of a predetermined type depicted in each of the images, and a segmentation model is trained to segment images by classifying each pixel according to what image part the pixel belongs to. Parts and/or features of both models are combined to form the deep neural net. The deep neural net is adapted to generate, in a single forward pass, a map indicating locations of any objects for each input image.
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