# DAVIDSTUTZ

Check out the latest superpixel benchmark — Superpixel Benchmark (2016) — and let me know your opinion! @david_stutz
28thFEBRUARY2017

The proposed approach is a two-stage system consisting of a fully convolutional detection proposal system, and a discriminator to reduce false positives. In both cases, convolutional neural networks are generalized to 3D data in the straight-forward manner (by using 3D kernels for convolutions). The used architectures of both models are summarized in Figure 2, where $M$ denotes a max pooling layer, $C$ a convolutional layer and $FC$ a fully connected layer. For the fully convolutional network, the fully connected layers are reinterpreted as convolutional layers. Thus, the network can be trained on positive/negative crops of the 3D data and during testing be applied to full 3D volumes to produce probability volumes. After non-maximum suppression and thresholding, the probability volume is used to extract detection proposals. The discriminator is a general 3D convolutional neural network trained on crops including false positives obtained from the fully convolutional network. The full system and training procedure are illustrated in Figure 3.