  This study formulates the IR target detection as a binary classification
problem of each pixel. Each pixel is associated with a label which indicates
whether it is a target or background pixel. The optimal label set for all the
pixels of an image maximizes aposteriori distribution of label configuration
given the pixel intensities. The posterior probability is factored into (or
proportional to) a conditional likelihood of the intensity values and a prior
probability of label configuration. Each of these two probabilities are
computed assuming a Markov Random Field (MRF) on both pixel intensities and
their labels. In particular, this study enforces neighborhood dependency on
both intensity values, by a Simultaneous Auto Regressive (SAR) model, and on
labels, by an Auto-Logistic model. The parameters of these MRF models are
learned from labeled examples. During testing, an MRF inference technique,
namely Iterated Conditional Mode (ICM), produces the optimal label for each
pixel. The detection performance is further improved by incorporating temporal
information through background subtraction. High performances on benchmark
datasets demonstrate effectiveness of this method for IR target detection.
