Image and Scalar Sensor Data Comparison Without Heavy NN Training
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Solution Overview
Problem
Existing methods for analyzing image and acoustic information using neural networks are complicated and require a large number of training data, which are often not available in sufficient quantities.
Innovation Solution
A method that combines image information with scalar sensor values into consistent data structures for comparison, allowing for analysis without relying solely on neural networks, using automated or manual methods, and optionally with a trained neural network for further evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural networks are used to analyze image and sensor data, then analysis capability is improved, but system complexity and training requirements increase
Solution Approach 1:
The patent segments the analysis system into two parts: a simple data collection and comparison module for routine analysis, and an optional neural network module for enhanced evaluation. This segmentation allows the system to achieve reliable analysis through the straightforward comparison of structured data structures containing image and sensor information, while the complex neural network is only invoked when additional analytical power is needed, thus reducing overall system complexity and training requirements
Solution Approach 2:
The patent introduces structured data structures as an intermediary layer between raw image/sensor data and analysis results. These data structures serve as a standardized format that facilitates direct comparison without requiring complex neural network processing. The intermediary data structure approach enables reliable analysis through simple comparison operations, while reducing the burden of neural network configuration and training
2Measurement precision
If neural networks are trained to analyze sensor data, then analysis accuracy is improved, but training data requirements and time increase
Solution Approach 1:
The patent performs preliminary organization of image and sensor data into structured data structures before analysis. By pre-structuring the data with consistent formats and relationships, the system enables accurate analysis through direct comparison without requiring extensive neural network training. This preliminary data preparation action reduces the need for time-consuming training while maintaining analysis accuracy
Solution Approach 2:
The patent employs simple, lightweight data comparison methods as a disposable alternative to expensive, time-consuming neural network training. The structured data structure comparison approach provides sufficient accuracy for many applications without requiring large training datasets or extensive training time, effectively replacing the need for heavy neural network training in scenarios where simple comparison suffices
Data Source
AI summary
A method for analyzing image information using assigned scalar values includes a.) capturing a first image of an object or a situation and capturing at least one first scalar sensor value relating to the object or the situation; b.) capturing a second image of the object or the situation and capturing at least one second scalar sensor value relating to the object or the situation; c.) inserting the first image and the at least one first scalar sensor value into a first data structure as a consistent representation form, and inserting the second image and the at least one second scalar sensor value into a second data structure as a consistent representation form; d.) comparing the first and the second data structure and e.) outputting information if the comparison results in a difference that corresponds to a defined or definable criterion.