Object Image Masking Using Depth Cameras for Accurate Segmentation
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Solution Overview
Problem
Current 3D modeling techniques face challenges in accurately separating foreground objects from background images, especially when the objects and backgrounds have similar pixel colors, making it difficult to generate clean object masks for effective image segmentation.
Innovation Solution
A method involving the receipt of a depth mask of an object, projecting it onto an image in a background to generate a depth image, determining portions representative of the object and background, and using these determinations to create a foreground mask for identifying object pixels, thereby isolating the object from the background.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image segmentation methods are used to separate foreground objects from background, then the process is simple and fast, but the segmentation accuracy deteriorates when objects and backgrounds have similar pixel colors
Solution Approach 1:
The patent transitions from 2D color-based segmentation to 3D depth-based segmentation by introducing depth camera data. Depth information provides an additional dimension for distinguishing foreground objects from backgrounds, enabling accurate segmentation even when color information is insufficient. The depth map captures distance information that creates clear separation between objects and backgrounds regardless of color similarity.
Solution Approach 2:
The patent introduces a depth map as an intermediary data structure that mediates between the raw image and the final segmentation result. The depth map serves as an additional information source that helps resolve ambiguities in color-based segmentation, acting as a mediator that provides depth context to improve foreground-background separation accuracy.
2Reliability
If depth camera data is integrated to improve segmentation accuracy, then segmentation robustness improves, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary actions by capturing depth information simultaneously with the image data using synchronized cameras. The depth map is generated in advance during the image capture process, making the depth information readily available for segmentation without requiring additional processing time later. This preliminary capture of depth data establishes a solid foundation for robust segmentation.
Solution Approach 2:
The patent segments the image processing task into multiple independent components: depth map generation, foreground mask creation, and final segmentation. By dividing the processing into discrete segments that can be handled separately and in parallel, the system reduces overall processing time while maintaining segmentation robustness through the integration of depth information.
Data Source
AI summary
Within examples, object image masking is provided. An example method includes receiving a depth mask of an object, projecting the depth mask of the object onto an image of the object in a background so as to generate a depth image of the object in the background, determining portions of the depth image of the object in the background that are representative of the object and that are representative of the background, based on the portions of the depth image of the object in the background that are representative of the object determining a foreground mask of the object, and using the foreground mask of the object to identify portions of the image representative of the object.