Setting an attention region for convolutional neural networks using region selective features, for recognition of materials within glass vessels

Abstract

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.

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Cited by in corpus (4)

• A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification
• Hierarchical semantic segmentation using modular convolutional neural networks
• Computer vision for liquid samples in hospitals and medical labs using hierarchical image segmentation and relations prediction
• Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset