Virtual Object Display Using Inlier Entity Classification

The system segments the environment determination task into multiple components: capturing image data from the physical environment, identifying entities within the environment, classifying entity types, and determining the physical environment based on entity type frequencies. This segmentation improves measurement precision by breaking down the complex positioning problem into manageable steps that can be executed with higher accuracy at each stage.

The system introduces an intermediary classification process between direct positioning and environment determination. Instead of relying solely on positioning data, the patent uses entity identification and classification as an intermediate step that mediates the determination of physical environment. This intermediary layer filters out positioning errors by using entity type analysis as a verification mechanism.

The system applies local quality by focusing analysis on specific entity types that are most indicative of the physical environment rather than uniformly analyzing all entities. The patent identifies and prioritizes certain entity types (such as furniture, appliances, or structural elements) that provide stronger signals for environment classification, thereby reducing overall processing energy while maintaining or improving identification accuracy.

The system performs partial action by analyzing only the necessary portion of entities required for accurate environment determination. Instead of exhaustively processing every detected entity, the patent implements early termination criteria or sampling strategies that achieve sufficient accuracy with reduced processing, thereby lowering power consumption while maintaining measurement precision.

The classification system is segmented into distinct property evaluation stages, where entities are assessed based on multiple properties (such as mobility, consistency, and likelihood of erroneous classification) in a structured sequence. This segmentation allows the system to build classification accuracy incrementally without overwhelming complexity, as each property contributes a specific dimension to the overall classification decision.

The system performs preliminary classification actions by pre-defining entity properties and classification criteria before actual environment determination. The patent establishes a framework where entity properties are identified and weighted in advance, allowing the classification process to execute efficiently with reduced runtime complexity while maintaining high measurement precision through pre-computed classification rules.

The system implements feedback mechanisms where the determined physical environment type is used to validate and refine the classification of entities. The patent creates a closed-loop system where environment determination results feed back into entity classification, allowing the system to adjust and improve reliability iteratively. This feedback ensures that virtual object display decisions are based on consistently validated environment assessments.

The system performs preliminary environment analysis and entity classification before making virtual object display decisions. By establishing a robust classification framework in advance with predefined rules and criteria, the patent ensures that when environment determination is completed, it is sufficiently reliable to guide virtual object selection, thereby improving reliability without requiring complex real-time adjustments.