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.

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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