Binary Image Clustering via Pooling and Boundary Mapping
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Solution Overview
Problem
Existing image processing techniques for high-resolution images, such as K-means and connected-component labeling, are resource-intensive and require multiple iterations, making them unsuitable for low-power devices with limited memory and processing resources, especially when dealing with an unknown number of clusters.
Innovation Solution
Downsampling high-resolution images using pooling operations to create a grid of binary cells, grouping these cells into contiguous regions, determining boundaries, and mapping these boundaries back to the original image, thereby reducing computational and memory overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If K-means clustering is applied to high-resolution images, then clustering accuracy is improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the high-resolution image into multiple blocks or regions, applying clustering operations to each block separately rather than to the entire image at once. This division reduces the computational burden on each processing unit while maintaining overall clustering accuracy through coordinated processing of all blocks.
Solution Approach 2:
The patent transforms the image data from spatial domain to frequency domain using techniques like Discrete Cosine Transform (DCT), converting the problem from processing pixel intensities to processing frequency coefficients. This dimensional transformation reduces data complexity and enables more efficient clustering operations.
2Manufacturing precision
If connected-component labeling with morphological operations is applied to high-resolution images, then boundary accuracy is improved, but processing time and memory consumption increase
Solution Approach 1:
The patent performs preliminary processing steps such as noise filtering and initial thresholding before applying connected-component labeling. By preparing the image data in advance with preprocessing operations, the subsequent boundary detection and clustering operations require fewer iterations and less computational time while achieving accurate results.
Solution Approach 2:
The patent divides the image processing into staged operations where morphological operations are applied selectively to specific regions or at reduced resolution levels, rather than uniformly across the entire high-resolution image. This segmented approach reduces overall processing time while preserving boundary accuracy in critical regions.
3Reliability
If clustering operations are performed on high-resolution images with unknown number of clusters, then clustering completeness is improved, but memory consumption and processing resources increase
Solution Approach 1:
The patent employs dynamic clustering methods where the number of clusters is not fixed in advance but adapts during processing. Techniques such as hierarchical clustering or density-based clustering allow the system to discover the appropriate number of clusters automatically based on data characteristics, ensuring completeness without requiring excessive memory for pre-defining all possible clusters.
Solution Approach 2:
The patent processes the image in manageable blocks or tiles rather than loading the entire high-resolution image into memory at once. Each block is clustered independently or with limited context from neighboring blocks, dramatically reducing peak memory consumption while maintaining overall clustering completeness through coordinated processing of all blocks.
Data Source
AI summary
This disclosure provides methods, devices, and systems for image processing. The present implementations more specifically relate to systems and techniques for binary image processing. In some aspects, an image processing system downsamples an image as a grid of binary cells based on a pooling operation. In some implementations, the pooling operation is a max pooling operation. In some other aspects, the image processing system groups a subset of the binary cells into one or more contiguous regions of the grid based on a binary image clustering algorithm. In some implementations, the binary image clustering algorithm is a connected-component labeling (CCL) algorithm. In some other aspects, the image processing system determines a respective boundary for each of the one or more contiguous regions. In some other aspects, the image processing system maps the determined boundaries to the image. In some instances, the image is a binary motion map of an environment.