Image Object Extraction With Recursive Nested Mask Labeling
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Solution Overview
Problem
Current systems for identifying and labeling objects in images are not always accurate, slow, and require excessive manual intervention or computational resources.
Innovation Solution
A system comprising an extraction module, a clustering module, and an indexing module that automatically identifies and labels objects in images, grouping them based on similarity, and creating an index for future object identification 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 enables self-service by allowing the model to automatically generate object labels without human intervention. The extraction module processes images and generates labels autonomously, eliminating the need for manual labeling while maintaining consistent accuracy across all processed images.
Solution Approach 2:
The patent replaces the mechanical manual labeling process with an automated computer vision system. The extraction module uses image processing algorithms to automatically identify and label objects, substituting human manual operations with automated computational processes that achieve both high speed and consistent accuracy.
2Productivity
If automated object identification systems are implemented, then productivity increases, but measurement precision and reliability may deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the extraction module continuously refines its object identification based on processed images and generated labels. This feedback loop allows the automated system to learn from its outputs and improve accuracy over time, ensuring that high productivity does not compromise measurement precision.
3Productivity
If automated object identification systems are implemented, then productivity increases, but device complexity and resource consumption increase
Solution Approach 1:
The extraction module is designed as a universal system that can handle multiple image processing tasks and identify various types of objects across different image types. This multi-functionality consolidates what would otherwise require multiple separate systems, reducing overall device complexity while maintaining high productivity across diverse applications.
Data Source
AI summary
A method for identifying and extracting objects from a video frame includes accessing a video frame of a video. The method further includes analyzing the video frame to generate a plurality of masks. Each mask includes neighboring pixels that are determined to be related. The method further includes extracting a plurality of objects from the video frame based on the generated plurality of masks. The method further includes recursively extracting a plurality of nested objects from the video frame based on the generated plurality of masks. The method further includes creating a plurality of object images by combining the plurality of masks of the plurality of objects with image data of the video frame and combining the plurality of masks of the plurality of nested objects with the image data of the video frame. The method further includes displaying the plurality of object images in a graphical user interface.