Convolutional Deep Belief Networks for Single-Cell/Object Tracking in Computational Biology and Computer Vision

Figure 1

Illustration of how the proposed CDBNTracker constructs an appearance model from a convolutional deep belief network. The raw input image is fed to a 2-stage convolutional deep belief network consisting of two max-pooling CRBMs and one fully connected layer. Each CRBM contains a filter bank layer and a probabilistic max-pooling layer, respectively. The outputs of the second stage are followed by one fully connected layer with 192 units.