Advances on watershed processing on GPU architecture

Authors:
Andrá Körbes;Giovani Bernardes Vitor;Roberto de Alencar Lotufo;Janito Vaqueiro Ferreira
Affiliations:
University of Campinas, School of Electrical and Computer Engineering, Campinas - SP, Brazil;University of Campinas, School of Mechanical Engineering, Campinas - SP, Brazil;University of Campinas, School of Electrical and Computer Engineering, Campinas - SP, Brazil;University of Campinas, School of Mechanical Engineering, Campinas - SP, Brazil
Venue:
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Year:
2011

Citing 6
Cited 2

Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Topographic distance and watershed lines

Signal Processing - Special issue on mathematical morphology and its applications to signal processing
The watershed transform: definitions, algorithms and parallelization strategies

Fundamenta Informaticae - Special issue on mathematical morphology
Parallel Asynchronous Watershed Algorithm-Architecture

IEEE Transactions on Parallel and Distributed Systems
Analysis of the Watershed Algorithms Based on the Breadth-First and Depth-First Exploring Methods

SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Parallel graph component labelling with GPUs and CUDA

Parallel Computing

Streamed watershed transform on GPU for processing of large volume data

Proceedings of the 28th Spring Conference on Computer Graphics
Efficient 2D and 3D watershed on graphics processing unit: block-asynchronous approaches based on cellular automata

Computers and Electrical Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an overview on the advances of watershed processing algorithms executed on GPU architecture. The programming model, memory hierarchy and restrictions are discussed, and its influence on image processing algorithms detailed. The recently proposed algorithms of watershed transform for GPU computation are examined and briefly described. Its implementations are analyzed in depth and evaluations are made to compare them both on the GPU, against a CPU version and on two different GPU cards.