Object Identification via Angular Boundary Pattern Matching
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Solution Overview
Problem
Existing object identification systems for augmented reality face challenges in accurately identifying real objects without direction or distance sensors, and require large image data storage capacity, limiting their effectiveness.
Innovation Solution
An object identification system that divides image angles into uniform sections, calculates boundaries, and generates pattern data using image edge detection, allowing for matching with virtual objects stored in a database, enabling accurate identification without additional sensors and reduced data storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transmits an image of a building to a server computer to identify a building, then the identification can be performed remotely, but the server computer must secure image data of large capacity in advance which increases data storage requirements
Solution Approach 1:
The patent extracts only the essential angular position information and boundary characteristics from images, storing these as compact pattern data in the database. Instead of storing complete image data, only the extracted features (angular positions of boundaries, object characteristics) are retained, dramatically reducing storage requirements while maintaining identification capability.
Solution Approach 2:
The system performs preliminary extraction of angular position patterns and boundary characteristics during database creation. The pattern data is pre-processed and stored in an optimized format that enables rapid comparison and identification without requiring storage of raw image data, preparing the data structure in advance for efficient querying.
2Measurement precision
If identifies a building through a relatively position difference between GPS coordinate of building and GPS coordinate of terminal, then the identification uses location data, but requires a terminal having a direction sensor and a distance sensor which increases device complexity
Solution Approach 1:
The patent extracts angular position information directly from image data through edge detection and boundary calculation, eliminating the need for separate direction sensors. The angular position is derived mathematically from the image coordinates and camera parameters, replacing hardware sensors with computational extraction.
Solution Approach 2:
The system replaces mechanical direction sensors and distance sensors with computational methods based on image processing and geometric calculations. The angular position and object identification are achieved through software-based edge detection and pattern matching rather than hardware sensors.
3Measurement precision
If uses an identification method requiring direction sensor and distance sensor, then accurate position and direction values can be obtained, but the identification ratio of an object is reduced in accordance with a sensor error
Solution Approach 1:
The system uses feedback through pattern matching comparison between extracted angular positions and database patterns. The identification process continuously refines results by comparing actual measured angular positions with stored reference patterns, correcting for errors and improving identification reliability through iterative validation.
Solution Approach 2:
The database is pre-populated with reference angular position patterns and object characteristics under various conditions. This preliminary preparation enables the system to account for potential measurement variations by comparing against multiple reference patterns, improving robustness against sensor errors.
4Measurement precision
If divides a first angle section into a first angle gap which is uniform with respect to a position where the real objects are previewed, then the angle section can be systematically analyzed, but the processing complexity increases
Solution Approach 1:
The patent segments the angular field of view into uniform angular gaps, dividing the continuous angle section into discrete intervals. This segmentation allows systematic processing of angular positions by assigning each detected boundary to a specific angular gap, simplifying the matching process with database patterns while maintaining measurement precision.
Data Source
AI summary
An object identification system calculates boundaries between real objects from images of the real objects, and calculates first indicators which correspond to each angles of a first angle section divided into a first angle gap and which varies every boundary between the calculated real objects. The object identification system extracts a virtual object having an outline firstly meet with a radiating line corresponding to each map angle of the divided into a second angle gap, and generate a set of second indicators corresponding to each map angle. The object identification system matches the first indicators into second indicators having a repeat ratio substantially equal to a repeat ratio of the first indicators in an angle section, and extracts virtual objects matched with each of the previewed real objects.