Concept-Based Segmentation Using Characteristic Pixel Metadata
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Solution Overview
Problem
Image segmentation is often inaccurate, especially when the background has the same color as the object, and rule-based segmentation can be complex, requiring learning for each object to achieve accurate results.
Innovation Solution
A concept-based segmentation method using characteristic pixel metadata to differentiate between objects and their backgrounds, even when they share similar colors, by associating objects with metadata that includes shape and property statistics, and updating or generating new metadata for improved segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule based segmentation is used, then segmentation accuracy can be improved for specific objects, but the system complexity increases and requires learning each object individually
Solution Approach 1:
The patent introduces an intermediary component - a trained neural network model that acts as a mediator between the input image and segmentation rules. This model pre-processes images to generate feature representations and probability maps, which then feed into simplified segmentation rules, reducing the complexity of rule-based approaches while maintaining accuracy
Solution Approach 2:
The system performs preliminary actions by training a neural network model in advance to learn object characteristics and generate feature representations. This pre-computed knowledge is then reused during segmentation, eliminating the need to relearn object properties for each segmentation task and reducing system complexity
2Measurement precision
If traditional segmentation methods are used, then processing speed may be maintained, but segmentation accuracy deteriorates when background color matches object color
Solution Approach 1:
The patent transitions from traditional 2D color-based segmentation to a multi-dimensional approach by incorporating feature vectors that include color, texture, shape, and other visual characteristics. The neural network processes images in multiple dimensions simultaneously, enabling accurate segmentation even when objects and backgrounds have similar colors by considering additional distinguishing features
Solution Approach 2:
The system changes the parameters used for segmentation from simple color thresholds to complex feature representations generated by the neural network. By transforming the input data into a different parameter space where objects and backgrounds are more distinguishable, the system achieves high accuracy while maintaining real-time processing speed through efficient neural network inference
Data Source
AI summary
A method for concept based segmentation, the method may include (a) detecting an object within a region of an image; wherein the object is associated with characteristic pixels metadata that indicative of multiple examples of pixels properties of pixels that are included in at least one appearance of the object within at least one image; and (b) finding, within the region, one or more object boundaries, based on the characteristic pixels metadata.