Object Identification Confidence Fusion via Alpha Beta Parameters
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Solution Overview
Problem
Existing object discrimination techniques often result in overconfident classifications due to the use of additive or multiplicative methods, failing to accurately represent uncertainty in discrimination results, leading to binary-like probabilities.
Innovation Solution
A system and method that incorporate a communication module, object identification module, confidence rules module, object confidence module, and data fusion module to generate a probability of identification based on alpha and beta parameters, which account for the independence and confidence in the received data, allowing for flexible data fusion and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additive or multiplicative techniques are used to combine discrimination data, then the classification confidence is increased, but the uncertainty representation is lost and probabilities become binary-like
Solution Approach 1:
The patent transforms the probability combination approach by introducing alpha and beta parameters that control the fusion behavior. Instead of fixed additive or multiplicative rules, the system uses parameterized fusion functions that can adapt the degree of confidence aggregation, preserving uncertainty information while maintaining classification reliability.
Solution Approach 2:
The system dynamically adjusts the data fusion process based on object confidence weights and independence assessments. The alpha and beta parameters allow the fusion mechanism to adapt its behavior, transitioning between different confidence aggregation strategies depending on the specific discrimination scenario, thereby preserving uncertainty representation.
2Adaptability or versatility
If multiple data fusion parameters (alpha and beta) are introduced to control independence and confidence, then the flexibility and quality of discrimination results are improved, but the device complexity increases
Solution Approach 1:
The patent manages complexity by parameterizing the fusion function rather than implementing multiple separate fusion mechanisms. The alpha and beta parameters provide a compact representation of fusion behavior, allowing flexible adaptation to different independence and confidence scenarios without requiring complex system architecture.
3Measurement precision
If object confidence weights are calculated based on multiple identification parameters and confidence rules, then the accuracy of probability of identification is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary assessment of data independence (beta parameter) and confidence levels before executing the full fusion process. This preliminary action allows the system to quickly evaluate whether detailed confidence weight calculation is necessary, reducing processing time for cases where simple fusion suffices while maintaining accuracy when needed.
Data Source
AI summary
The technology described herein includes a system and/or a method for determining confidence of object identification. The technology includes selecting at least one confidence rule from a plurality of confidence rules based on a first discrimination identification and a second discrimination identification. The technology further includes generating an object confidence weight based on one or more first identification parameters, one or more second identification parameters, and the selected at least one confidence rule. The technology further includes fusing the received set of data based on an alpha parameter, a beta parameter, and the object confidence weight. The technology further includes generating a probability of identification of a classification of the object based on the fused data.