Paper Info
Title
On GPU Connected Components and Properties: A Systematic Evaluation of Connected Component Labeling Algorithms and Their Extension for Property Extraction.
Abstract
Connected component labeling (CCL) is a fundamental image processing problem that has been studied in many platforms, including GPUs. A common approach to CCL performance analysis is studying the total processing time as a function of abstract image features, like the number of connected components or the fraction of foreground pixels, and input data usually includes synthetic images and segmented video datasets. In this paper, we develop on these ideas and propose an evaluation methodology for GPU CCL algorithms based on synthetic image patterns, addressing the nonexistence of a standard and reliable benchmark in the literature. Our methodology, applied on two important algorithms from existing literature, uncovers their data dependency with great detail, and allows us to model their processing time in three real-world video data sets as functions of abstract, high-level image concepts. We also apply our methodology for studying the memory and performance requirements of two strategies for computing connected component properties: an existing memory-hungry approach, and a new memory-preserving strategy.
Year
Venue
Field
2019
IEEE Trans. Image Processing
Kernel (linear algebra),Data dependency,Pattern recognition,Feature (computer vision),Image processing,Algorithm,Memory management,Artificial intelligence,Pixel,Connected component,Connected-component labeling,Mathematics
DocType
Volume
Issue
Journal
28
1
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Pedro Asad100.34
R. Marroquim2589.94
Andrea L. e L. Souza300.34