View source for US Patent Application 17638900. LEARNING SYSTEM, AUTHENTICATION SYSTEM, LEARNING METHOD, COMPUTER PROGRAM, LEARNING MODEL GENERATION APPARATUS, AND ESTIMATION APPARATUS simplified abstract

=LEARNING SYSTEM, AUTHENTICATION SYSTEM, LEARNING METHOD, COMPUTER PROGRAM, LEARNING MODEL GENERATION APPARATUS, AND ESTIMATION APPARATUS=


==Organization Name==

[[:Category:NEC Corporation|NEC Corporation]]


[[Category:NEC Corporation]]

==Inventor(s)==

[[:Category:Masato Tsukada of Tokyo (JP)|Masato Tsukada of Tokyo (JP)]][[Category:Masato Tsukada of Tokyo (JP)]]

[[:Category:Takahiro Toizumi of Tokyo (JP)|Takahiro Toizumi of Tokyo (JP)]][[Category:Takahiro Toizumi of Tokyo (JP)]]

[[:Category:Ryuichi Akashi of Tokyo (JP)|Ryuichi Akashi of Tokyo (JP)]][[Category:Ryuichi Akashi of Tokyo (JP)]]

==LEARNING SYSTEM, AUTHENTICATION SYSTEM, LEARNING METHOD, COMPUTER PROGRAM, LEARNING MODEL GENERATION APPARATUS, AND ESTIMATION APPARATUS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17638900 titled 'LEARNING SYSTEM, AUTHENTICATION SYSTEM, LEARNING METHOD, COMPUTER PROGRAM, LEARNING MODEL GENERATION APPARATUS, AND ESTIMATION APPARATUS

==Simplified Explanation==

The abstract describes a learning system that can perform machine learning on moving images shot at a low frame rate. Here are the key points:

* The learning system includes a selection unit that chooses certain images from a collection of frames shot at a high frame rate.
* The selected images include at least one image taken outside the focus range.
* An extraction unit extracts a feature amount from the selected images.
* A learning unit uses the extracted feature amount and correct answer information to train the extraction unit.
* The learning process enables the system to perform machine learning on moving images assumed to be shot at a low frame rate.


==Original Abstract Submitted==

A learning system () comprises: a selection unit () that selects from images corresponding to a plurality of frames shot at a first frame rate, part of the images, the part including an image taken outside a focus range; an extraction unit () that extracts a feature amount from the part of the images; and a learning unit () that performs learning for the extraction unit based on the feature amount extracted and correct answer information indicating a correct answer with respect to the feature amount. According to such a learning system, it is possible to execute machine learning assumed that moving images are shot at a low frame rate.



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