One DAG to Rule Them All

In this paper, we present novel strategies for optimizing the performance of many binary image processing algorithms. These strategies are collected in an open-source framework, graphgen , that is able to automatically generate optimized C++ source code implementing the desired optimizations. Simply...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 7; pp. 3647 - 3658
Main Authors: Bolelli, Federico, Allegretti, Stefano, Grana, Costantino
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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Algorithms
chain-code
connected components labeling
decision tables
Decision trees
directed rooted acyclic graphs
Graphics processing units
Image compression
Image processing
Labeling
optimal decision trees
Optimization
Performance enhancement
Prediction algorithms
Source code
Task analysis
thinning
Two dimensional displays
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:In this paper, we present novel strategies for optimizing the performance of many binary image processing algorithms. These strategies are collected in an open-source framework, graphgen , that is able to automatically generate optimized C++ source code implementing the desired optimizations. Simply starting from a set of rules, the algorithms introduced with the graphgen framework can generate decision trees with minimum average path-length, possibly considering image pattern frequencies, apply state prediction and code compression by the use of directed rooted acyclic graphs (DRAGs). Moreover, the proposed algorithmic solutions allow to combine different optimization techniques and significantly improve performance. Our proposal is showcased on three classical and widely employed algorithms (namely Connected Components Labeling, Thinning, and Contour Tracing). When compared to existing approaches -in 2D and 3D-, implementations using the generated optimal DRAGs perform significantly better than previous state-of-the-art algorithms, both on CPU and GPU.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2021.3055337