| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a68790a12228f5b99e176e8aacff640a |
| 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/G06T2207-10024
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2200-28 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-217
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6262
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
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/G06K9-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate |
2020-07-06-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2022-02-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_19fdc3731b4d15cbb548c7a9a99b1d07
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c8b81ba1580ca360781c623373b173ab
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1c6f5817875303ae36ac29c397874369
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8ea434c1a4b4e36645f0311729f3790b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4e7b62092a6547f52c587363da9f7d12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9d5dc5f728b23cb644683bdd3a89a5fb |
| publicationDate |
2022-02-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-11256961-B2 |
| titleOfInvention |
Training a neural network to predict superpixels using segmentation-aware affinity loss |
| abstract |
Segmentation is the identification of separate objects within an image. An example is identification of a pedestrian passing in front of a car, where the pedestrian is a first object and the car is a second object. Superpixel segmentation is the identification of regions of pixels within an object that have similar properties. An example is identification of pixel regions having a similar color, such as different articles of clothing worn by the pedestrian and different components of the car. A pixel affinity neural network (PAN) model is trained to generate pixel affinity maps for superpixel segmentation. The pixel affinity map defines the similarity of two points in space. In an embodiment, the pixel affinity map indicates a horizontal affinity and vertical affinity for each pixel in the image. The pixel affinity map is processed to identify the superpixels. |
| priorityDate |
2017-11-21-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |