Ambiguous Image Pixel Classification via Border Analysis
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Solution Overview
Problem
Imaging devices, particularly depth cameras, face challenges in distinguishing between pixels outside their depth imaging range ('far' pixels) and pixels that image dark objects, leading to ambiguous data interpretation.
Innovation Solution
A method is introduced to classify ambiguous pixels by defining contiguous areas and determining their classification based on characteristics of the border pixels, using techniques such as flood-fill approaches and depth value ratios, to differentiate between 'dark' and 'far' pixels, thereby processing the data accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If depth imaging is performed beyond maximum range, then coverage area is improved, but measurement precision deteriorates due to ambiguous data between far pixels and dark pixels
Solution Approach 1:
The patent segments ambiguous pixels into distinct categories ('far' pixels and 'dark' pixels) by analyzing spatial patterns and border characteristics. This segmentation resolves the ambiguity that would otherwise degrade measurement precision while maintaining extended coverage area.
Solution Approach 2:
The patent uses border pixel characteristics as an intermediary to classify ambiguous pixels. By examining the properties of pixels surrounding the ambiguous region, the system can indirectly determine whether ambiguous pixels represent distant objects or dark objects, thereby restoring measurement precision without limiting coverage.
2Measurement precision
If ambiguous pixels are classified using border characteristics, then measurement precision is improved, but device complexity increases due to additional processing steps
Solution Approach 1:
The patent applies partial action by focusing classification efforts only on ambiguous pixels and their immediate border regions, rather than processing the entire image. This selective approach maintains high classification accuracy while avoiding the computational overhead of analyzing all pixels, thus managing device complexity.
3Measurement precision
If flood-fill approach is used to define contiguous areas, then measurement precision is improved, but loss of time increases due to additional processing steps
Solution Approach 1:
The patent performs preliminary classification of border pixels before applying the flood-fill algorithm to ambiguous pixels. By pre-establishing the classification criteria from border characteristics, the subsequent flood-fill operation can proceed more efficiently with predetermined rules, reducing overall processing time while maintaining accurate contiguous area definition.
Data Source
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AI summary
Examples are disclosed herein that relate to classifying ambiguous data in an image. One example provides, on a computing device, a method of processing image data, the method comprising defining a contiguous area of ambiguous data in an image, determining a border surrounding the contiguous area, classifying the contiguous area as dark or far based on a characteristic of one or more pixels of the border, and for the classified contiguous area, processing one or more pixels of the contiguous area differently if the contiguous area is classified as far than if the contiguous area is classified as dark.