Efficient, High-Quality Image Contour Detection

TitleEfficient, High-Quality Image Contour Detection
Publication TypeConference Paper
Year of Publication2010
AuthorsCatanzaro, B., Su B. - Y., Sundaram N., Lee Y., Murphy M., & Keutzer K.
Abstract

Image contour detection is fundamental to many image
analysis applications, including image segmentation, object
recognition and classification. However, highly accurate
image contour detection algorithms are also very computa-
tionally intensive, which limits their applicability, even for
offline batch processing. In this work, we examine efficient
parallel algorithms for performing image contour detec-
tion, with particular attention paid to local image analysis
as well as the generalized eigensolver used in Normalized
Cuts. Combining these algorithms into a contour detector,
along with careful implementation on highly parallel, com-
modity processors from Nvidia, our contour detector pro-
vides uncompromised contour accuracy, with an F-metric
of 0.70 on the Berkeley Segmentation Dataset. Runtime is
reduced from 4 minutes to 1.8 seconds. The efficiency gains
we realize enable high-quality image contour detection on
much larger images than previously practical, and the al-
gorithms we propose are applicable to several image seg-
mentation approaches. Efficient, scalable, yet highly accu-
rate image contour detection will facilitate increased per-
formance in many computer vision applications.

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