Image Segmentation Using Depth Map Clustering
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Solution Overview
Problem
Existing methods for segmenting images into foreground and background regions fail when depth values are continuously distributed, leading to improper segmentation and adverse effects on post-processing.
Innovation Solution
A method involving obtaining a depth map, processing it to eliminate noise, and employing a clustering algorithm like K-means to determine cluster centers and a segmentation threshold, which allows for effective segmentation of the image into foreground and background regions by identifying a clear boundary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If known segmentation methods are used on images with continuously distributed depth values, then the segmentation process can be completed, but the segmentation accuracy deteriorates and foreground/background regions are improperly separated
Solution Approach 1:
The patent applies segmentation by dividing the depth map into distinct foreground and background regions using a clustering algorithm. The depth values are segmented into multiple clusters, with the foreground region identified as the cluster containing the minimum depth value. This resolves the contradiction by creating clear separations even when depth values are continuously distributed, improving both segmentation accuracy and reliability.
Solution Approach 2:
The patent changes the parameter approach by using clustering algorithms to identify discrete depth value groups rather than relying on fixed threshold values. By transforming the continuous depth parameter into clustered discrete groups, the method achieves proper segmentation boundaries even when depth values are continuously distributed, thereby improving segmentation reliability without sacrificing accuracy.
2Productivity
If simple threshold-based segmentation is used, then the processing speed is maintained, but the segmentation quality deteriorates when depth values are continuously distributed
Solution Approach 1:
The patent replaces the mechanical threshold-based segmentation system with a clustering algorithm system. Instead of using fixed depth thresholds to separate regions, the system uses iterative clustering to identify natural groupings in the depth data. This substitution maintains computational efficiency while dramatically improving segmentation quality for continuously distributed depth values.
Solution Approach 2:
The patent performs preliminary actions by first clustering the depth values to identify distinct groups before performing the actual segmentation. This preliminary clustering step organizes the continuous depth data into discrete groups, making the subsequent segmentation straightforward and accurate. The foreground region is then easily identified as the cluster containing the minimum depth value, maintaining processing speed while improving quality.
3Measurement precision
If clustering algorithms are employed to find clear boundaries, then segmentation quality is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the depth map into distinct foreground and background regions using a clustering algorithm. The depth values are segmented into multiple clusters, with the foreground region identified as the cluster containing the minimum depth value. This resolves the contradiction by creating clear separations even when depth values are continuously distributed, improving both segmentation accuracy and reliability.
Solution Approach 2:
The patent changes the parameter approach by using clustering algorithms to identify discrete depth value groups rather than relying on fixed threshold values. By transforming the continuous depth parameter into clustered discrete groups, the method achieves proper segmentation boundaries even when depth values are continuously distributed, thereby improving segmentation reliability without sacrificing accuracy.
Data Source
AI summary
A method for dividing an image obtains the image by captured and a depth map of the image. The method creates a histogram of the depth map including horizontal and vertical axes. A clustering algorithm is applied to the data in the histogram to determine upon two data clusters and the cluster centers of the two clusters. The method determines that the abscissa value of the smallest ordinate value between two cluster centers in the histogram is a segmentation threshold. If the obtained segmentation threshold meets a preset condition, the image is segmented into a foreground region and a background region. An electronic device for applying the method is also provided.