发明名称 Cross-trained convolutional neural networks using multimodal images
摘要 Embodiments of a computer-implemented method for training a convolutional neural network (CNN) that is pre-trained using a set of color images are disclosed. The method comprises receiving a training dataset including multiple multidimensional images, each multidimensional image including a color image and a depth image; performing a fine-tuning of the pre-trained CNN using the depth image for each of the plurality of multidimensional images; obtaining a depth CNN based on the pre-trained CNN, wherein the depth CNN is associated with a first set of parameters; replicating the depth CNN to obtain a duplicate depth CNN being initialized with the first set of parameters; and obtaining a depth-enhanced color CNN based on the duplicate depth CNN being fine-tuned using the color image for each of the plurality of multidimensional images, wherein the depth-enhanced color CNN is associated with a second set of parameters.
申请公布号 US9633282(B2) 申请公布日期 2017.04.25
申请号 US201514813233 申请日期 2015.07.30
申请人 Xerox Corporation 发明人 Sharma Arjun;Kompalli Pramod Sankar
分类号 G06K9/62;G06K9/46;G06K9/48 主分类号 G06K9/62
代理机构 Jones Robb PLLC 代理人 Jones Robb PLLC
主权项 1. A computer-implemented method for training a convolutional neural network (CNN) that is pre-trained using a set of color images, the method comprising: receiving, using an input module of a system memory, a training dataset including a plurality of multidimensional images, each multidimensional image including a color image and a depth image; performing, using a processor, a fine-tuning of the pre-trained CNN using the depth image for each of the plurality of multidimensional images; obtaining, using a cross-trained CNN module in the system memory, a depth CNN based on the pre-trained CNN, the depth CNN includes at least one convolutional layer in communication with an ultimate fully-connected layer via a penultimate fully-connected-layer, wherein the depth CNN is associated with a first set of parameters; replicating, using the cross-trained CNN module, the depth CNN to obtain a duplicate depth CNN being initialized with the first set of parameters; and obtaining, using the cross-trained CNN module, a depth-enhanced color CNN based on the duplicate depth CNN being fine-tuned using the color image for each of the plurality of multidimensional images, the depth-enhanced color CNN includes at least one convolutional layer in communication with an ultimate fully-connected layer of the depth-enhanced color CNN via a penultimate fully-connected-layer of the depth-enhanced color CNN, wherein the depth-enhanced color CNN is associated with a second set of parameters.
地址 Norwalk CT US