Image Segmentation Dead Cluster Control via Dynamic Representative Data
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Solution Overview
Problem
Existing image segmentation methods, such as SLIC and k-means clustering, face a decrease in segmentation accuracy when applied to temporally continuous images with significant changes, leading to the formation of 'dead clusters' which reduce the number of areas and accuracy.
Innovation Solution
An image processing apparatus that sets and updates representative data for each frame, determining whether to initialize or reuse representative data based on operating conditions like camera movements or scene changes, to prevent the accumulation of dead clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If representative data from the immediately preceding image is reused for the current image, then processing speed is improved, but segmentation accuracy deteriorates when the image has largely changed
Solution Approach 1:
The patent makes the representative data initialization dynamic by introducing a determination unit that adapts the initialization strategy based on image change detection. When image changes are detected (camera movement, scene change, or large variation), the system initializes representative data; when changes are minimal, it reuses previous representative data. This dynamic adaptation resolves the contradiction between processing speed and segmentation accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where the determination unit evaluates image changes between frames and uses this information to control representative data initialization. The feedback loop (image comparison → change detection → initialization decision) ensures that representative data is updated only when necessary, maintaining both speed and accuracy.
2Measurement precision
If representative data is initialized for each image, then segmentation accuracy is improved, but processing speed deteriorates
Solution Approach 1:
Instead of always initializing representative data (excessive action) or never initializing (insufficient action), the patent applies partial initialization only when image changes are detected. This selective approach maintains segmentation accuracy when needed while avoiding unnecessary initialization overhead, thus resolving the speed-accuracy tradeoff.
3Productivity
If the number of representative data is reduced to increase processing speed, then productivity is improved, but the number of dead clusters increases
Solution Approach 1:
The patent performs preliminary detection of image changes before clustering. When changes are detected, representative data is initialized beforehand to prevent dead cluster formation. This preliminary action ensures that the clustering process starts with appropriate representative data, maintaining reliability without requiring excessive representative data points.
Data Source
AI summary
A clustering processing unit sets representative data to be used in first clustering, for each of temporally continuous images, and repeats clustering of pixels based on the representative data. A representative data updating unit updates the representative data whenever clustering is repeated. As the representative data to be used in first clustering of each image, an initial value or representative data used in last clustering of an immediately preceding image is set based on predetermined input information. An example of the predetermined input information is operation information for an image capturing unit for capturing an image. If a dead cluster predictively forms due to an image change, the initial value is set as the representative data. This makes it possible to restrict accumulation of dead clusters, and suppress the decrease in segmentation accuracy.