Image Object Extraction Using Masks and Nested Segmentation
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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 effort or computer resources.
Innovation Solution
A system comprising an extraction module, a clustering module, and an indexing module to automatically identify and label objects in images, using techniques such as mask generation and vector analysis to group similar objects, and create an index for efficient object recognition 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 human operators for each labeling task.
Solution Approach 2:
The patent replaces the manual mechanical process of human labeling with an automated computer vision system. The system uses algorithmic image processing, mask generation, and object detection techniques to substitute human manual operations with automated computational processes, thereby increasing productivity while maintaining accuracy.
2Measurement precision
If advanced object recognition systems are implemented, then identification accuracy improves, but device complexity and computational resource requirements increase
Solution Approach 1:
The system segments the object recognition process into distinct functional modules: image processing, mask generation, object detection, and labeling. By dividing the complex task into smaller, specialized components, the system manages complexity more effectively while maintaining high recognition accuracy through focused processing at each stage.
Solution Approach 2:
The system performs preliminary actions by pre-processing images and generating masks before final object identification. This preparatory processing simplifies the subsequent recognition tasks, allowing the system to achieve high accuracy without requiring overly complex final-stage processing, thereby managing overall system complexity.
3Use of energy by moving object
If manual object labeling is performed, then resource consumption is reduced, but time and productivity are significantly increased
Solution Approach 1:
The system enables continuous automated processing of images without interruption by manual operations. Once initialized, the computer vision model continuously processes images, generates labels, and updates masks in an uninterrupted workflow, eliminating the time losses associated with manual task switching and human operator limitations while managing computational resources through efficient algorithmic processing.
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.