In computer vision, image segmentation is the process of partitioning a digital image into multiple segments . The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.
Fully Convolutional Networks for Semantic Segmentation
A fully convolutional indicates that the neural network is composed of convolutional layers without any fully-connected layers usually found at the end of the network. To our knowledge, the idea of extending a convnet to arbitrary-sized inputs first appeared in Matan et al , which extended the classic LeNet to recognize strings of digits, because their net was limited to one-dimensional input strings, Matan et al. used Viterbi decoding to obtain their outputs. In the follow wiki text, a fully convolutional network presented by UC Berkeley is introduced , the net is trained end-to-end, pixel-to-pixel on semantic segmentation. And also this was the first work to train FCNs end-to-end (1) for pixelwise prediction and (2) from supervised pre-training.
 Badrinarayanan, Vijay, Ankur Handa, and Roberto Cipolla. "Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling." arXiv preprint arXiv:1505.07293 (2015).
 O. Matan, C. J. Burges, Y. LeCun, and J. S. Denker. Multi-digit recognition using a space displacement neural network.In NIPS, pages 488–495. Citeseer, 1991.