  Convolutional neural networks have emerged as the leading method for the
classification and segmentation of images. In some cases, it is desirable to
focus the attention of the net on a specific region in the image; one such case
is the recognition of the contents of transparent vessels, where the vessel
region in the image is already known. This work presents a valve filter
approach for focusing the attention of the net on a region of interest (ROI).
In this approach, the ROI is inserted into the net as a binary map. The net
uses a different set of convolution filters for the ROI and background image
regions, resulting in a different set of features being extracted from each
region. More accurately, for each filter used on the image, a corresponding
valve filter exists that acts on the ROI map and determines the regions in
which the corresponding image filter will be used. This valve filter
effectively acts as a valve that inhibits specific features in different image
regions according to the ROI map. In addition, a new data set for images of
materials in glassware vessels in a chemistry laboratory setting is presented.
This data set contains a thousand images with pixel-wise annotation according
to categories ranging from filled and empty to the exact phase of the material
inside the vessel. The results of the valve filter approach and fully
convolutional neural nets (FCN) with no ROI input are compared based on this
data set.
