Image Material Token Classification for Shadow Edge Recognition
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Solution Overview
Problem
Conventional image processing techniques struggle to accurately distinguish between shadow and material object edges, leading to significant false positives and false negatives, as they rely on assumptions about the sharpness of boundaries which often fail in real-world scenarios.
Innovation Solution
A method and system that identify regions of an image corresponding to a single material by classifying tokens as Type A, Type B, or Type C, using spatio-spectral information and filtering techniques to differentiate between material and illumination components, and then constructing Type B tokens through arbitrary boundary removal, adjacent planar merging, and local token analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional brightness boundary analysis is used to detect object edges, then the method can be accurately executed by a computer, but the results will often be incorrect due to false positives and false negatives in shadow recognition
Solution Approach 1:
The patent segments the image processing task into multiple distinct stages: token generation from brightness boundaries, token classification into types (shadow, object edge, or unknown), and iterative refinement through merging and splitting operations. This segmentation allows each stage to specialize in specific aspects of the problem, improving overall reliability while maintaining computational feasibility.
Solution Approach 2:
The patent performs preliminary classification of brightness boundaries into typed tokens before final interpretation. By预先 categorizing boundaries as potential shadows, object edges, or unknown regions, and then systematically refining these classifications through multiple passes, the system resolves ambiguities that would cause false positives and false negatives in single-pass conventional methods.
2Productivity
If assumptions about boundary sharpness are made to simplify shadow detection, then the algorithm can be executed efficiently, but the results will be incorrect in real-world scenarios where shadows form sharp boundaries and material edges form soft boundaries
Solution Approach 1:
The patent implements dynamic, iterative refinement of token classifications rather than relying on static initial assumptions. The system repeatedly merges and splits tokens based on evolving understanding of boundary characteristics, allowing classifications to adapt to actual image content rather than being constrained by predetermined assumptions about shadow versus object edge properties.
Solution Approach 2:
The patent introduces typed tokens as intermediary representations between raw brightness boundaries and final interpretation. These tokens carry classification information (shadow, object edge, unknown) that mediates the analysis, allowing the system to handle ambiguous cases systematically through iterative refinement rather than making premature binary decisions.
3Device complexity
If conventional techniques are used for shadow and object edge recognition, then the processing can be completed with simple algorithms, but there are significant possibilities for false positives and false negatives
Solution Approach 1:
The patent segments the complex recognition task into manageable stages: generating brightness boundary tokens, classifying them into types, merging similar tokens, splitting ambiguous tokens, and iteratively refining classifications. This segmentation transforms an intractably complex single-step problem into a sequence of simpler, more reliable operations.
Solution Approach 2:
The patent implements feedback loops where classification results from one iteration inform the next. Tokens are classified, then the system evaluates classification confidence, merges or splits tokens based on results, and reclassifies in subsequent passes. This feedback mechanism allows the system to learn from and correct its own classifications, progressively improving accuracy.
Data Source
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AI summary
In a first exemplary embodiment of the present invention, an automated, computerized method is provided for processing an image. According to a feature of the present invention, the method comprises the step of performing a filter response technique to identify patches of uniform reflectance within the image. In a further exemplary embodiment, the method comprises the additional step of utilizing the identified patches of uniform material reflectance to generate single material token regions for use in processing material and illumination aspects of the image.