Connected Component Labeling via Two-Pass Cache Architecture
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current connected component labeling and contour tracking techniques in computer vision are inefficient due to slow processing speeds and excessive cache memory usage, often requiring multiple passes through the image data.
Innovation Solution
A hardware architecture within a graphics processor that performs connected component analysis using a two-pass procedure, where the first pass labels pixels and generates label connections, and the second pass updates and finalizes labels, reducing the need for repeated image reads and optimizing cache usage through a connected component processing cache system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a two-pass algorithm is used to speed up connected component labeling, then processing speed is improved, but cache memory usage increases
Solution Approach 1:
The patent segments the image processing into two distinct passes: a first pass that reads the image and creates a labeled image with provisional labels, and a second pass that reads only the labeled image to finalize labels. This segmentation allows the system to process the original image data once and reuse the labeled version, reducing the need to repeatedly read the entire original image while maintaining processing speed.
Solution Approach 2:
The first pass performs preliminary labeling by assigning provisional labels to connected components and storing them in a labeled image. This preliminary action creates a reusable intermediate product that eliminates the need to re-analyze the original image data in subsequent operations, thereby reducing cache memory requirements for storing multiple copies of the original image.
2Measurement precision
If the entire image is read multiple times to complete analysis, then labeling accuracy is improved, but bandwidth usage increases
Solution Approach 1:
The patent divides the analysis into two passes where the first pass reads the original image to create labels, and the second pass reads only the compact labeled image (not the full original image) to finalize labeling. This segmentation ensures labeling accuracy is maintained while significantly reducing bandwidth usage by avoiding repeated reads of the entire original image.
Solution Approach 2:
The patent creates a labeled image as a compact copy that contains only the essential labeling information rather than the full original image data. This copy is then reused in the second pass, eliminating the need to repeatedly read the large original image while preserving labeling accuracy.
3Quantity of substance
If labels propagate one layer per iteration, then memory usage is reduced, but processing time increases
Solution Approach 1:
The patent segments the labeling process into two passes: the first pass performs comprehensive label propagation across the entire image to establish all connected components and their provisional labels, and the second pass finalizes the labels. This segmentation allows efficient use of memory by processing labels in organized stages rather than requiring all labels to be maintained simultaneously, while avoiding excessive iteration through the two-pass structure.
Data Source
AI summary
An apparatus may include a memory, a processor circuit, and a connected component labeling module. The connected component labeling module may be operative of the processor circuit to determine one or more connected components during reading of an image comprising a multiplicity of pixels from the memory, assign a label to a plurality of pixels of the multiplicity of pixels, generate one or more label connections for a respective one or more labels, each label connection linking a higher label to a lowest label for the same connected component, and write to the memory for each label of the one or more labels a lowest label as defined by the label connection for the each label after a label is assigned to each pixel.