Super Pixel Scene Segmentation for Focal Stack Images
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Solution Overview
Problem
Conventional image segmentation from focal stack images is inefficient, particularly in areas with depth discontinuity and weak texture, and lacks a balance between incorporating user input and minimizing user burden, with per-pixel focus measurement being unreliable and sophisticated techniques requiring significant computational resources.
Innovation Solution
The method groups pixels into super pixels, calculates focal measures and color probabilities for these super pixels, and segments images based on both focal measures and color probabilities, allowing for user input through selecting a focus frame to enhance segmentation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If per-pixel focus measurement is used for segmentation, then segmentation can be performed, but reliability is poor especially in areas with depth discontinuity and weak texture
Solution Approach 1:
The patent merges multiple focal stack images to compute a composite focus measure for each super pixel. By combining information from multiple images with different focus planes, the system overcomes the unreliability of per-pixel measurements in challenging areas, achieving more robust segmentation reliability.
2Manufacturing precision
If sophisticated segmentation techniques like graph-cut algorithm are used, then segmentation accuracy may improve, but computation time increases significantly due to large graph size
Solution Approach 1:
The patent segments the image into super pixels first, then performs segmentation calculations on these larger units rather than individual pixels. This hierarchical segmentation approach maintains segmentation accuracy while dramatically reducing the number of nodes in the graph, thus reducing computation time.
Solution Approach 2:
The patent transitions from pixel-level operations to super pixel-level operations, effectively changing the dimension of operation. By grouping pixels into super pixels, the system reduces the data dimension while preserving essential segmentation information, enabling efficient processing.
3Reliability
If user input is incorporated into segmentation calculations, then segmentation quality may improve, but user burden increases when significant detail is required
Solution Approach 1:
The patent implements semi-automatic segmentation where users provide minimal input (such as selecting a focus frame or providing rough annotations) rather than detailed complete segmentation. This partial action approach achieves sufficient segmentation quality without imposing excessive user burden.
Solution Approach 2:
The system performs automatic super pixel formation, focal measure calculation, and segmentation computation without requiring extensive user input. The algorithm self-services by automatically processing the images and generating segmentation results, with user input serving only as optional guidance.
Data Source
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AI summary
A method, apparatus and computer program product are provided to facilitate scene segmentation from focal stack images. The method may include receiving a set of focal stack images, calculating a focal measure for each of a plurality ofpixels of the set of focal stack images, and grouping each of a plurality of pixels for which the focal measure was calculated into a plurality of super pixels. The method may also include calculating a focal measure for each of the plurality of super pixels, segmenting a respective focal stack image based on the focal measure of each of the plurality of super pixels, calculating a color probability for each of the plurality of super pixels, and segmenting each focal stack image based on color probability of each of the plurality of super pixels.