http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112465835-B
Outgoing Links
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|---|---|
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10016 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20221 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-40 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T5-50 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-13 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-136 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-136 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-13 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-40 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-11 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T5-50 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00 |
| filingDate | 2020-11-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2022-07-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2022-07-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-112465835-B |
| titleOfInvention | Method for Emerald Image Segmentation and Model Training Method |
| abstract | Embodiments of the present disclosure disclose a method for segmenting a jadeite image and a model training method, wherein the method for segmenting a jadeite image includes: first, in response to acquiring an original image containing an image of jadeite, inputting the original image to a In the first network model pre-trained by machine learning, the first network model can segment the original image and output an initial emerald image containing no background image; then input the initial emerald image to the second network model pre-trained based on machine learning , so that the second network model can segment the highlight area image of the initial emerald image, and output the emerald image that does not contain the highlight area image. Using the machine learning training model can accurately remove the background and highlight areas of the jade image, which solves the technical problems that the jade image segmentation adopts digital image processing, which is applicable to a single scene, blurred segmentation boundaries and inaccurate segmentation. |
| priorityDate | 2020-11-26-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID451216657 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID12449 |
Total number of triples: 30.