A pre-trained convolutional neural network is trained to accurately identify threats in x-ray images of luggage. The x-ray images are filtered according to a threshold to separate out the outlines of dense objects prior to the analysis by the trained convolutional neural network.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for identifying weapons in luggage, comprising: an x-ray imaging system for producing x-ray images of luggage; a processor configured to apply a threshold to the x-ray images to separate out outlines of dense objects that pass the threshold; and a convolutional neural network for analyzing the outlines of the dense objects to identify weapons in the x-ray images. 2. The system of claim 1, wherein the convolutional neural network is a pre-trained convolutional neural network. 3. The system of claim 2, wherein the pre-trained convolutional neural network is further trained on a database of images of knives and handguns. 4. The system of claim 3, wherein the database of images of knives and handguns include partial images of the knives and handguns that do not include a complete outline of the knives and handguns. 5. The system of claim 1, wherein the x-ray imaging system is a dual-beam x-ray imaging system. 6. The system of claim 1, wherein the processor is further configured to apply a minimum size requirement to the separated items. 7. The system of claim 6, wherein the processor is further configured to apply a smoothing filter to the outlines of the separated items. 8. The system of claim 7, wherein the smoothing filter is a Gaussian filter. 9. The system of claim 1, wherein the convolutional neural network further comprises a graphics processing unit. 10. A method, comprising: obtaining a pre-trained convolutional neural network that is pre-trained on an image database that does not include images of knives and handguns; training the pre-trained convolutional neural network on a database of knife and handgun images to provide a trained convolutional neural network that distinguishes between knives and handguns; x-raying luggage to produce x-ray images of the luggage; and identifying knives and handguns in the x-ray images using the trained convolutional neural network. 11. The method of claim 10, wherein training the pre-trained convolutional neural network comprises a recursive training in which incorrectly-identified unseen images of knives and handguns are not added to the database, and wherein correctly identified unseen images are added to the database. 12. The method of claim 10, wherein training the pre-trained convolutional neural network comprises further training the pre-trained convolutional neural network on partial images of knives and handguns that do not contain a complete outline of the knives and handguns. 13. The method of claim 12, training the pre-trained convolutional neural network comprises further training the pre-trained convolutional neural network on images of just a handgun trigger. 14. The method of claim 10, wherein x-raying the luggage comprising producing a first x-ray image across a first plane for the luggage and producing a second x-ray image in a different plane for the luggage. 15. The method of claim 10, wherein identifying knives and handguns in the x-ray images using the trained convolutional neural network occurs in a processor remote from the x-raying of the luggage. 16. The method of claim 15, further comprising: applying a threshold to the x-ray images to separate out an outline of dense objects within the x-ray images, wherein identifying knives and handguns in the x-ray images using the trained convolutional neural network comprises using the trained convolutional neural network on the outlines of the dense objects.