Multi-scale deep learning system
A system for identifying objects in an image is provided. The system identifies segments of an image that may contain objects. For each segment, the system generates a segment score by inputting to a multi-scale neural network windows of multiple scales that include the segment that have been resampled to a fixed window size. A multi-scale neural network includes a feature extracting convolutional neural network (“feCNN”) for each scale and a classifier that inputs each feature of each feCNN. The segment score indicates whether the segment contains an object. The system generates a pixel score for pixels of the image. The pixel score for a pixel indicates that that pixel is within an object based on the segment scores of segments that contain that pixel. The system then identifies the object based on the pixel scores of neighboring pixels.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-07NA27344
- Assignee:
- Lawrence Livermore National Security, LLC (Livermore, CA)
- Patent Number(s):
- 10,521,699
- Application Number:
- 15/782,771
- OSTI ID:
- 1600450
- Resource Relation:
- Patent File Date: 10/12/2017
- Country of Publication:
- United States
- Language:
- English
Image segmentation using consensus from hierarchical segmentation ensembles
|
conference | October 2014 |
Image Recording System
|
patent-application | July 2015 |
Kernel Sparse Modules for Automated Tumor Segmentation
|
patent-application | January 2016 |
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