Mobile Robot Image Segmentation Using Superpixel Representative Values
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Solution Overview
Problem
Existing image processing technologies for mobile robots struggle to perform rapid and accurate image segmentation, which is crucial for obstacle avoidance and efficient movement in public spaces.
Innovation Solution
An image processing apparatus and mobile robot system that groups super pixels based on colors or luminances, calculates representative values, and performs segmentation using these values, allowing for efficient image processing without the need for additional hardware like GPUs, using a local convolutional neural network technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image segmentation methods are used, then segmentation can be performed, but the processing speed is slow and accuracy is insufficient
Solution Approach 1:
The patent divides the image into superpixels first, grouping adjacent pixels with similar colors or luminances together. This segmentation approach reduces the number of processing units from individual pixels to superpixel regions, enabling faster processing while maintaining boundary accuracy. The superpixel segmentation is performed before the main segmentation algorithm, creating coarser regions that speed up subsequent processing without losing important edge information.
Solution Approach 2:
The patent applies different processing strategies to different regions of the image based on their characteristics. Representative values are calculated for each superpixel region, and the segmentation algorithm adapts its processing based on local color and luminance variations. This local quality approach allows the system to focus computational resources on regions with significant variations while using simplified processing for homogeneous regions, improving both speed and accuracy.
2Measurement precision
If detailed pattern recognition is performed on all pixels, then accurate segmentation can be achieved, but unnecessary calculations increase processing time
Solution Approach 1:
The patent extracts representative values from each superpixel region instead of processing every individual pixel. By selecting key representative pixels or calculating aggregate statistics (such as mean color or luminance) for each superpixel, the system reduces the data volume significantly. This extraction approach maintains the essential information needed for accurate pattern recognition while eliminating redundant calculations on pixels with similar characteristics.
Solution Approach 2:
The patent performs detailed analysis only on representative pixels within superpixel regions rather than all pixels. This partial action approach applies full segmentation algorithms selectively to key representative points while using the superpixel structure to infer information for the entire region. The result is sufficient segmentation accuracy achieved with substantially reduced computational effort.
Data Source
AI summary
The present invention relates to an image processing apparatus and a mobile robot including the same. The image processing apparatus according to an embodiment of the present invention includes an image acquisition unit for obtaining an image and a processor for performing signal processing on the image from the image acquisition unit, and the processor is configured to group super pixels in the image on the basis of colors or luminances of the image, calculate representative values of the super pixels and perform segmentation on the basis of the representative values of the super pixels. Accordingly, image segmentation can be performed rapidly and accurately.