Indexed Object Labeling for Accurate Cross-Location Image Recognition
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Solution Overview
Problem
Current systems for identifying and labeling objects in images are not always accurate, can be slow, require manual intervention, or excessive computer resources, and lack automated functionality for efficient object recognition.
Innovation Solution
A system comprising an extraction module, a clustering module, and an indexing module for automatically identifying and labeling objects in images, utilizing masks, vectors, and an index to group and recognize similar objects across multiple locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to identify objects in images, then accuracy can be maintained, but productivity is reduced and time consumption increases
Solution Approach 1:
The system performs automatic object identification and labeling without requiring manual human intervention. The computer vision model autonomously processes images, detects objects, generates labels, and creates masks, enabling the system to serve itself rather than relying on external manual labeling operations.
Solution Approach 2:
The patent replaces the mechanical manual process of human labeling with an automated computer vision system. The system uses algorithms to detect objects, generate labels, and create masks automatically, substituting human manual operations with computational processes that achieve both high accuracy and high productivity.
2Productivity
If automated object recognition systems are implemented, then productivity increases, but measurement precision and reliability may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the computer vision model continuously learns from processed images and labeling results. The model adjusts its parameters based on performance metrics and feedback from object detection outcomes, improving accuracy over time while maintaining high productivity through automated processing.
Solution Approach 2:
The system performs preliminary actions by pre-training the computer vision model on large datasets before deployment. This preliminary training establishes a foundation of accuracy that enables the system to maintain high measurement precision during automated operation, reducing the need for continuous manual correction while preserving productivity.
3Productivity
If complex automated labeling systems are deployed, then productivity and automation extent improve, but device complexity increases
Solution Approach 1:
The computer vision model serves multiple functions within the system: object detection, label generation, mask creation, and parameter extraction. This multi-functionality reduces the need for separate specialized components, managing device complexity while enabling batch processing and high productivity through a single versatile system.
Solution Approach 2:
The system segments the object recognition process into distinct functional modules: image input, object detection, label generation, mask creation, and output. This segmentation allows each module to be optimized independently while working together in an integrated pipeline, managing overall system complexity through modular architecture that supports batch processing and high productivity.
4Reliability
If manual object labeling is performed, then reliability can be maintained through human judgment, but loss of time and productivity increase
Solution Approach 1:
The system achieves reliability through self-consistent automated processes rather than human judgment. The computer vision model applies the same detection and labeling criteria uniformly across all images, eliminating variability introduced by different human operators while maintaining consistent quality standards through automated validation and processing.
Data Source
AI summary
A method for automatically identifying and labeling objects in images using an index includes accessing first video frames captured at a first physical location. The method further includes identifying a plurality of first objects from the first video frames and generating first composite vectors for the plurality of first objects. The method further includes storing the first composite vectors in an index. The method further includes accessing second video frames captured at a second physical location. The method further includes identifying a plurality of second objects from the second video frames and generating second composite vectors for the plurality of second objects. The method further includes determining, using the index and the second composite vectors for the plurality of second objects, a plurality of similar objects. The method further includes displaying images of one or more of the plurality of similar objects in a graphical user interface.