A Fast Learning Algorithm for Image Segmentation with Max-Pooling Convolutional Networks

02/07/2013
by   Jonathan Masci, et al.
0

We present a fast algorithm for training MaxPooling Convolutional Networks to segment images. This type of network yields record-breaking performance in a variety of tasks, but is normally trained on a computationally expensive patch-by-patch basis. Our new method processes each training image in a single pass, which is vastly more efficient. We validate the approach in different scenarios and report a 1500-fold speed-up. In an application to automated steel defect detection and segmentation, we obtain excellent performance with short training times.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset