View source for 18547605. METHOD FOR FUSING MEASUREMENT DATA CAPTURED USING DIFFERENT MEASUREMENT MODALITIES simplified abstract (Robert Bosch GmbH)

=METHOD FOR FUSING MEASUREMENT DATA CAPTURED USING DIFFERENT MEASUREMENT MODALITIES=


==Organization Name==

[[:Category:Robert Bosch GmbH|Robert Bosch GmbH]]


[[Category:Robert Bosch GmbH]]

==Inventor(s)==

[[:Category:Balint Szollosi-nagy of Budapest (HU)|Balint Szollosi-nagy of Budapest (HU)]][[Category:Balint Szollosi-nagy of Budapest (HU)]]

[[:Category:Ernest-Adrian Scheiber of Budapest (HU)|Ernest-Adrian Scheiber of Budapest (HU)]][[Category:Ernest-Adrian Scheiber of Budapest (HU)]]

[[:Category:Istvan Remenyi of Tata (HU)|Istvan Remenyi of Tata (HU)]][[Category:Istvan Remenyi of Tata (HU)]]

[[:Category:Zoltan Karasz of Budapest (HU)|Zoltan Karasz of Budapest (HU)]][[Category:Zoltan Karasz of Budapest (HU)]]

==METHOD FOR FUSING MEASUREMENT DATA CAPTURED USING DIFFERENT MEASUREMENT MODALITIES - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18547605 titled 'METHOD FOR FUSING MEASUREMENT DATA CAPTURED USING DIFFERENT MEASUREMENT MODALITIES

==Simplified Explanation==
This patent application describes a method for combining data from two different measurements by modifying latent representations of features.

==Key Features and Innovation==
* Determining latent representations of features from two sets of measurement data.
* Decoding information about features from the latent representations.
* Modifying features in one latent representation based on the other.
* Updating and decoding information about features from the modified latent representations.

==Potential Applications==
This technology could be applied in various fields such as image processing, data analysis, and pattern recognition.

==Problems Solved==
This method addresses the challenge of integrating data from multiple sources to improve the accuracy and reliability of feature representation.

==Benefits==
The benefits of this technology include enhanced data fusion capabilities, improved feature extraction, and increased accuracy in data analysis.

==Commercial Applications==
* Image recognition systems
* Data mining software
* Biometric identification technology

==Prior Art==
Researchers can explore prior art related to latent feature representation, data fusion techniques, and machine learning algorithms.

==Frequently Updated Research==
Stay informed about the latest advancements in data fusion, feature extraction, and machine learning techniques to enhance the application of this technology.

==Questions about the Technology==
===How does this method improve feature representation compared to traditional data fusion techniques?===
This method enhances feature representation by modifying latent representations based on information from multiple measurements, leading to more accurate and comprehensive feature extraction.

===What are the potential limitations of using this technology in real-world applications?===
The limitations may include computational complexity, data compatibility issues, and the need for large datasets to train the model effectively.


==Original Abstract Submitted==

A method for fusing first measurement data. The method include: determining a first latent representation of features from the first measurement data; decoding first information about features from the first latent representation; determining a second latent representation of features from the second measurement data; decoding second information about features from the second latent representation; modifying features in the first latent representation based on features in the second latent representation; modifying features in the second latent representation based on features in the first latent representation; decoding updated information about features from the updated first latent representation; and decoding updated information about features from the updated second latent representation.



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[[Category:G06T7/73]]
[[Category:G06V10/774]]
[[Category:G06V10/776]]
[[Category:G06V10/82]]
[[Category:G06V20/58]]
[[Category:CPC_G06V10/806]]