Data management system for spatial phase imaging

1. A data management system for spatial phase imaging, comprising:

a storage engine configured to receive and store input data in a record format, the input data comprising:

pixel-level first-order primitives generated based on electromagnetic (EM) radiation received from an object located in a field-of-view of an image sensor device; and

pixel-level second-order primitives generated based on the pixel-level first-order primitives;

an analytics engine configured to determine a plurality of features of the object by:

clustering the pixel-level first-order primitives having similar attributes and the pixel-level second-order primitives having similar attributes to define surfaces of the object; and

attributing a respective set of surface-level first-order primitives and surface-level second-order primitives to each surface of the object; and

an access engine configured to provide a user access to the plurality of features of the object determined by the analytics engine and to the input data stored by the storage engine.

2. The data management system of claim 1 , wherein the access engine is configured to provide the user access through a software developer kit (SDK) layer, a portal through which the user can interact with the input data and the plurality of features of the object and transform the input data and the plurality of features into other data types through a human-directed approach, a technical platform providing a specific plurality of features to the user, or a combination thereof.

3. The data management system of claim 1 , wherein the access engine is configured to provide the user access to the sets of surface-level first-order primitives and surface-level second-order primitives, the access engine being an application operating on an existence, an extent, or a location of the surfaces of the object in a defined coordinate space.

4. The data management system of claim 3 , wherein the application comprises an autonomous driving application or a scene segmentation application.

5. The data management system of claim 1 , wherein the analytics engine is configured to determine the plurality of features of the object by determining one or more geometric or angular relationships among the surfaces of the object that are invariant to orientation, translation, distance, or a coordinate system used to specify a location of the object.

6. The data management system of claim 5 , wherein the analytics engine is configured to determine the plurality of features of the object by identifying a shape of the object, identifying one or more materials of the object, identifying a type of the object, or determining a volume occupied by the object based on the one or more geometric or angular relationships.

7. The data management system of claim 6 , wherein the storage engine is configured to receive and store input data from repeated measurements of the object by the image sensor device, and the analytics engine is configured to determine a change in the shape of the object or a size of the object based on the repeated measurements.

8. The data management system of claim 7 , wherein the analytics engine is configured to determine a growth, a speed, or an acceleration of the object based on the change in the shape of the object or the change in the size of the object.

9. The data management system of claim 7 , wherein the analytics engine is configured to determine the plurality of features based on the pixel-level first-order primitives, the pixel-level second-order primitives, the surface-level first-order primitives, the surface-level second-order primitives, and the shape of the object using artificial intelligence (AI)-based analytics.

10. The data management system of claim 9 , wherein the access engine is a machine vision application, a prediction application, a scene segmentation application, or a combination thereof.

11. The data management system of claim 1 , wherein the plurality of features of the object comprises microscopic-level features of the object or macroscopic-level features of the object.

12. The data management system of claim 1 , wherein the plurality of features comprises:

gradient layers of the object;

a full rendering of the object and a scene;

a distance of the object from the image sensor device, machine vision analysis of the object and the scene;

a three-dimensional model or image of the object; a frequency distribution of electromagnetic radiation emanating from the object;

angles of one or more surfaces or shapes of the object;

surface normal vectors associated with the one or more surfaces of the object;

velocity and acceleration of the surface normal vectors;

an identification of a material of the object; and

interior volume features of the object; segmentation of the object; surface features within segments of the object; or a combination thereof.

13. The data management system of claim 1 , being implemented by a mainframe processor, an edge processor, a graphic processing unit, a quantum-based processor, a photonic-based processor, a biological-based processor, or a combination thereof.

14. A method, comprising:

storing, in a storage engine of a data management system, input data in a record format, the input data comprising:

pixel-level first-order primitives generated based on electromagnetic (EM) radiation received from an object located in a field-of-view of an image sensor device; and

pixel-level second-order primitives generated based on the pixel-level first-order primitives;

determining, by operation of an analytics engine of the data management system, a plurality of features of the object by clustering the pixel-level first-order primitives having similar attributes and the pixel-level second-order primitives having similar attributes to define surfaces of the object; and

attributing a respective set of surface-level first-order primitives and surface-level second-order primitives to each surface of the object; and

providing, by operation of an access engine of the data management system, a user access to the plurality of features of the object determined by the analytics engine and to the input data stored by the storage engine.

15. The method of claim 14 , comprising providing, by operation of the access engine, the user access through a software developer kit (SDK) layer, a portal through which the user can interact with the input data and the plurality of features of the object and transform the input data and the plurality of features into other data types through a human-directed approach, a technical platform providing a specific plurality of features to the user, or a combination thereof.

16. The method of claim 14 , comprising providing the user access to the sets of surface-level first-order primitives and surface-level second-order primitives, the access engine being an application operating on an existence, an extent, or a location of the surfaces of the object in a defined coordinate space.

17. The method of claim 16 , wherein the application comprises an autonomous driving application or a scene segmentation application.

18. The method of claim 14 , wherein determining the plurality of features of the object comprises determining one or more geometric or angular relationships among the surfaces of the object that are invariant to orientation, translation, distance, or a coordinate system used to specify a location of the object.

19. The method of claim 18 , wherein determining the plurality of features of the object comprises identifying a shape of the object, identifying one or more materials of the object, identifying a type of the object, or determining a volume occupied by the object based on the one or more geometric or angular relationships.

20. The method of claim 19 , comprising receiving and storing input data from repeated measurements of the object by the image sensor device, and determining a change in the shape of the object or a size of the object based on the repeated measurements.

21. The method of claim 20 , comprising determining a growth, a speed, or an acceleration of the object based on the change in the shape of the object or the change in the size of the object.

22. The method of claim 20 , wherein determining the plurality of features comprises determining the plurality of features based on the pixel-level first-order primitives, the pixel-level second-order primitives, the surface-level first-order primitives, the surface-level second-order primitives, and the shape of the object using artificial intelligence (AI)-based analytics.

23. The method of claim 22 , wherein the access engine is a machine vision application, a prediction application, a scene segmentation application, or a combination thereof.

24. The method of claim 14 , wherein the plurality of features of the object comprises microscopic-level features of the object or macroscopic-level features of the object.

25. The method of claim 14 , wherein the plurality of features comprises:

gradient layers of the object; a full rendering of the object and a scene;

a distance of the object from the image sensor device, machine vision analysis of the object and the scene; a three-dimensional model or image of the object;

a frequency distribution of electromagnetic radiation emanating from the object;

angles of one or more surfaces or shapes of the object;

surface normal vectors associated with the one or more surfaces of the object;

velocity and acceleration of the surface normal vectors;

an identification of a material of the object;

interior volume features of the object; and

segmentation of the object; surface features within segments of the object; or a combination thereof.