Multi-Scale Object Segmentation via Local Candidate Region Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image segmentation techniques face challenges in rapidly and accurately segmenting objects in images, especially in identifying object boundaries and distinguishing between adjacent objects, which is crucial for applications like object identification and robot navigation.
Innovation Solution
The method involves selecting multiple local candidate regions at different scales, performing image segmentation and classification on these regions, and fusing the results based on object classes and binary segmentation masks to achieve precise object segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation techniques are used, then the process is simple, but the accuracy and speed of object boundary identification are insufficient
Solution Approach 1:
The patent divides the image into multiple local candidate regions at different scales before performing segmentation. This multi-scale segmentation approach allows the system to capture both fine-grained boundary details and coarse object structures, improving boundary identification accuracy without overwhelming complexity by organizing the processing into hierarchical stages
Solution Approach 2:
The patent introduces a multi-scale dimension to the segmentation process by generating local candidate regions at multiple different scales. This adds a scale dimension to the traditional single-scale segmentation, enabling the system to consider both detailed local structures and overall object contexts simultaneously, thereby improving boundary accuracy
2Reliability
If multiple local candidate regions at different scales are processed, then the fault-tolerant ability and segmentation accuracy are improved, but the processing time and computational complexity increase
Solution Approach 1:
By segmenting the image into multiple local candidate regions at different scales, the system can process each region independently and combine results. This segmentation strategy improves fault tolerance because if one scale or region fails, others can still provide valid segmentation results, while the modular structure enables efficient parallel processing to manage time consumption
Solution Approach 2:
The patent processes multiple local candidate regions at different scales, which may be more regions than strictly necessary. This partial/excessive action approach improves reliability by having redundant information at multiple scales, allowing the system to tolerate failures or ambiguities in individual regions while maintaining overall segmentation accuracy
3Manufacturing precision
If local candidate regions are fused based on object class and binary masks, then the segmentation effect is improved, but the algorithm complexity increases
Solution Approach 1:
The patent applies different fusion strategies to different local candidate regions based on their specific characteristics and object class predictions. Instead of using a uniform fusion approach, the system adapts the fusion method to local requirements, improving segmentation precision for each region while managing overall algorithm complexity through localized processing decisions
Solution Approach 2:
The system performs preliminary classification of local candidate regions into different object classes before the fusion step. This preliminary action organizes the data structure and enables more efficient fusion processing, as regions can be grouped and processed according to their semantic meanings, reducing the complexity of the subsequent fusion algorithm
Data Source
AI summary
A method, an apparatus and a computing device for segmenting an object include: selecting, for an image to be processed, multiple local candidate regions according to two or more different preset scales respectively; performing image segmentation processing on two or more local candidate regions, to predict and obtain binary segmentation masks of the two or more local candidate regions; performing image classification processing on the two or more local candidate regions, to predict and obtain object classes to which the two or more local candidate regions belong; and fusing the two or more local candidate regions according to the object class to which the two or more local candidate regions belong and the binary segmentation masks of the two or more local candidate regions, to obtain an object segmentation image.