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DIFF Subgroup
G06V 20/10

Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59)

Introduced: January 2022

Title

Titles differ between systems:

IPC: Terrestrial scenes

CPC: Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59)

Full Title

Full titles differ between systems:

IPC:

Scenes; Scene-specific elements > Terrestrial scenes

CPC:

Scenes; Scene-specific elements (control of digital cameras H04N23/60) > Terrestrial scenes (scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59)

Additional Content IPC

Glossary

aerial imagery images taken from an aircraft or other flying object (e.g. aircrafts, helicopters, UAVs, balloons, etc.) band bands response sensed by the optical sensor to a certain range of wavelength. endmember endmembers material that has a spectrally unique signature in the wavelength bands used to collect the image GIS geographic information system Hughes Phenomenon/Curse of dimensionality when the dimensionality of the data increases, the volume of the data-space increases. Thus, if the dimensionality of a fixed amount of data is increased, the data becomes sparse in the increased data-space. This causes the classifier’s performance to deteriorate. Increasing the amount of data or decreasing the dimensionality of the data will improve the performance of the classifier. hyperspectral image hyperspectral images multi-band image where the z dimension corresponds to consecutive spectral wavelengths ranges. multispectral image multispectral images multi-band image where the z dimension corresponds to spectral wavelengths ranges (not necessarily consecutive) remote sensing process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation from a satellite or aircraft SAR synthetic aperture radar spectral image cube spectral image cubes data having 3 dimensions [3D], 2 spatial (x,y) and a third spectral dimension UAV UAVs unmanned aerial vehicle

Limiting references

Surveillance or monitoring of activities, e.g. for recognising suspicious objects Recognition or understanding of scenes outside a vehicle by using sensors mounted on the vehicle Recognition or understanding of scenes inside of a vehicle

CPC subdivides this area 3x more granularly than IPC with 4 additional codes.

4 codes are CPC-only extensions.

IPC defines codes here since 2022.

Child Classifications

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Top Applicants

Top Applicants (IPC)

Class G06,2013–2023, worldwide · Source: EPO PATSTAT

  1. SAMSUNG ELECTRONICS COMPANY KR 66,669
  2. IBM (INTERNATIONAL BUSINESS MACHINES CORPORATION) US 62,313
  3. MICROSOFT TECHNOLOGY LICENSING US 41,918
  4. GOOGLE US 32,969
  5. SGCC(STATE GRID CORPORATION OF CHINA) 30,822
  6. INTEL CORPORATION US 30,010
  7. TENCENT TECHNOLOGY (SHENZHEN) COMPANY 28,235
  8. HUAWEI TECHNOLOGIES COMPANY CN 26,079
  9. APPLE US 21,891
  10. HUAWEI TECHNOLOGIES COMPANY 20,505

Top Applicants (CPC)

Class G06,2013–2023, worldwide · Source: EPO PATSTAT

  1. SAMSUNG ELECTRONICS COMPANY KR 76,952
  2. IBM (INTERNATIONAL BUSINESS MACHINES CORPORATION) US 62,841
  3. MICROSOFT TECHNOLOGY LICENSING US 44,778
  4. GOOGLE US 35,735
  5. INTEL CORPORATION US 32,087
  6. HUAWEI TECHNOLOGIES COMPANY CN 30,572
  7. TENCENT TECHNOLOGY (SHENZHEN) COMPANY 25,023
  8. APPLE US 23,482
  9. SGCC(STATE GRID CORPORATION OF CHINA) 22,548
  10. HUAWEI TECHNOLOGIES COMPANY 20,917