Image Segmentation Model Selection by Object Size and Label Level
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Solution Overview
Problem
Existing electronic devices face challenges in optimizing segmentation performance and memory usage when employing multiple segmentation models, particularly due to inefficiencies in selecting the appropriate model based on object size and label level.
Innovation Solution
An electronic device is equipped with a processor that identifies an object-of-interest in an image, determines a specific segmentation model from a plurality based on the object's size, and applies it to the region of interest, thereby optimizing segmentation performance and minimizing memory use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple segmentation models are used to handle different object sizes and label levels, then segmentation performance is improved, but memory consumption increases
Solution Approach 1:
The patent divides the segmentation task into multiple specialized models based on object size categories (e.g., fine-grained models for small objects, coarse-grained models for large objects) and label levels. This segmentation allows each model to be optimized for specific conditions, improving overall segmentation performance while enabling selective loading to control memory usage.
Solution Approach 2:
The system dynamically selects which segmentation models to load and execute based on the detected object size and label level in the current image. This dynamic adaptation allows the system to optimize the balance between segmentation performance and memory consumption by only loading necessary models rather than maintaining all models in memory simultaneously.
2Reliability
If multiple segmentation models are loaded simultaneously to handle various object sizes, then segmentation accuracy improves, but processing efficiency deteriorates
Solution Approach 1:
The patent segments the segmentation models into distinct categories based on object size and label level, allowing each model to specialize in specific tasks. This segmentation enables the system to maintain high accuracy for different object types while improving processing efficiency through targeted model application.
Solution Approach 2:
The system dynamically determines which segmentation models to apply based on real-time analysis of object size and label level in the input image. This dynamic selection optimizes processing efficiency by avoiding unnecessary model executions while maintaining segmentation accuracy through the appropriate model choice.
3Adaptability or versatility
If all segmentation models are used to ensure comprehensive object recognition, then segmentation completeness improves, but computational resources increase
Solution Approach 1:
The patent segments segmentation models into specialized groups based on object size and label level, enabling comprehensive coverage of different object types. This segmentation approach ensures segmentation completeness by having dedicated models for various categories while reducing computational resources by only activating relevant models.
Solution Approach 2:
The system dynamically selects and applies segmentation models based on the specific object size and label level detected in each image, ensuring comprehensive segmentation coverage. This dynamic adaptation optimizes computational resource usage by avoiding unnecessary processing for objects that can be handled by simpler models.
Data Source
AI summary
According to an embodiment of the specification, disclosed is an electronic device that obtains an image by using a camera, identifies an object-of-interest among a plurality of objects included in the image, determines a selected segmentation model among a plurality of segmentation models based on a size of the object-of-interest and apply the determined segmentation model to a region of interest (ROI) of the image containing the object-of-interest is disclosed.