Object Labeling Workflow for Unrecognized AR Environment Objects
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Solution Overview
Problem
Existing systems struggle to effectively associate and label objects in environments, particularly in augmented reality and autonomous systems, due to limitations in recognizing and processing objects without prior models, leading to inefficiencies and user burdens.
Innovation Solution
An information processing system that determines the appropriateness of processing based on object information and adds user-designated label information when recognition is inadequate, utilizing a processor with image recognition and a region object associated table to generate personalized label lists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automatic object recognition is used without prior models, then the system can process novel objects, but the recognition accuracy deteriorates
Solution Approach 1:
The patent introduces an intermediary labeling mechanism where users can manually add or correct labels for unrecognized objects. This intermediary step bridges the gap between automatic recognition limitations and accurate object identification, allowing the system to handle novel objects while maintaining recognition accuracy through user feedback.
Solution Approach 2:
The system implements a feedback loop where recognition results are evaluated and used to generate appropriate labels. When automatic recognition fails or identifies objects incorrectly, the system provides feedback by presenting labeling options to users, whose corrections are then fed back into the system to improve future recognition accuracy.
2Measurement precision
If manual labeling is required for all objects, then recognition accuracy is maintained, but user burden increases
Solution Approach 1:
Instead of requiring manual labeling for all objects, the system applies partial action by only prompting users to label objects when automatic recognition is uncertain or fails. This selective approach maintains recognition accuracy for problematic cases while avoiding unnecessary user intervention for clearly recognized objects, thereby reducing overall user burden.
Solution Approach 2:
The system performs self-service by automatically generating recognition results for objects that can be reliably identified. This allows the system to handle the majority of objects autonomously without user intervention, reserving manual labeling only for cases where self-service recognition is insufficient.
3Measurement precision
If comprehensive object models are pre-generated, then recognition accuracy improves, but system complexity increases
Solution Approach 1:
The patent employs a universal labeling mechanism that serves multiple functions: it provides user feedback for improvement, generates training data for machine learning models, and enables the system to adapt to various object types without requiring separate pre-generated models for each. This multi-functional approach maintains recognition accuracy while avoiding the complexity of comprehensive pre-generated models.
Solution Approach 2:
The system performs preliminary action by automatically generating recognition results and label suggestions before user intervention is needed. This preliminary processing reduces the complexity of requiring comprehensive pre-generated models, as the system prepares recognition outcomes in advance and only engages users when necessary.
Data Source
AI summary
An information processing system according to an embodiment includes processing circuitry. The processing circuitry determines whether or not processing related to an object disposed in an environment is appropriate based on information related to the object. When determining that the processing is not appropriate, the processing circuitry adds label information designated by a user to data on the object.