  The field of computer vision has greatly matured in the past decade, and many
of the methods and techniques can be useful for astronomical applications. One
example is in searching large imaging surveys for objects of interest,
especially when it is difficult to specify the characteristics of the objects
being searched for. We have developed a method using contour finding and
convolution neural networks (CNNs) to search for Infrared Dark Clouds (IRDCs)
in the Spitzer Galactic plane survey data. IRDCs can vary in size, shape,
orientation, and optical depth, and are often located near regions with complex
emission from molecular clouds and star formation, which can make the IRDCs
difficult to reliably identify. False positives can occur in regions where
emission is absent, rather than from a foreground IRDC. The contour finding
algorithm we implemented found most closed figures in the mosaic and we
developed rules to filter out some of the false positive before allowing the
CNNs to analyze them. The method was applied to the Spitzer data in the
Galactic plane surveys, and we have constructed a catalog of IRDCs which
includes additional parts of the Galactic plane that were not included in
earlier surveys.
