Image Masking via Object-Type Segmentation Pipelines
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Solution Overview
Problem
Conventional image processing systems face difficulties in accurately segmenting objects from images, especially with complex boundaries and edges, such as those found in images with hair or fur, leading to time-consuming and often inaccurate manual masking processes.
Innovation Solution
An image processing system that identifies regions of an image using an object detector, classifies them based on object type, and applies optimized masking pipelines to generate region masks, which are then merged to create an image mask, with overlapping regions weighted by saliency scores to improve segmentation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single masking pipeline is used for all object types, then the system complexity is reduced, but the segmentation accuracy deteriorates for complex objects like hair and fur
Solution Approach 1:
The patent segments the masking task by dividing objects into different categories (e.g., hair/fur objects vs. other objects) and applying different masking pipelines to each category. This allows the system to use specialized processing for complex objects while maintaining simpler processing for other objects, thereby improving segmentation accuracy without requiring all objects to undergo complex processing.
Solution Approach 2:
The patent applies local quality by using different masking pipelines tailored to specific object types in different regions of the image. Objects with hair or fur are processed with a specialized pipeline that handles complex boundaries, while other objects use a standard pipeline. This localized approach improves accuracy for specific object types without increasing overall system complexity unnecessarily.
2Manufacturing precision
If multiple masking pipelines are used for different object types, then the segmentation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent implements dynamics by making the masking pipeline selection adaptive based on object characteristics. The system dynamically chooses which masking pipeline to apply based on the detected object type (e.g., whether the object has hair or fur). This dynamic selection allows the system to use multiple specialized pipelines only when needed, rather than always using the most complex pipeline, thus improving accuracy while controlling complexity.
3Manufacturing precision
If manual masking is used for complex boundaries, then the segmentation accuracy is improved, but the processing time increases significantly
Solution Approach 1:
The patent applies self-service by enabling the system to automatically detect object types and select appropriate masking pipelines without manual intervention. The automated object detection and classification system identifies complex objects like hair and fur, then automatically applies the suitable masking pipeline, eliminating the need for manual masking while maintaining high segmentation accuracy and reducing processing time.
4Productivity
If automated object detection is used, then the processing time is reduced, but the segmentation accuracy deteriorates for objects with complex edges
Solution Approach 1:
The patent applies preliminary action by performing object detection and classification before applying the masking pipeline. The system first detects objects and categorizes them by type (e.g., identifying hair or fur objects), then selects and applies the appropriate masking pipeline. This preliminary classification enables automated processing to achieve high accuracy by preparing the correct processing approach in advance, rather than using a one-size-fits-all method.
Data Source
AI summary
An image processing system generates an image mask from an image. The image is processed by an object detector to identify a region having an object, and the region is classified based on an object type of the object. A masking pipeline is selected from a number of masking pipelines based on the classification of the region. The region is processed using the masking pipeline to generate a region mask. An image mask for the image is generated using the region mask.