Automatic target detection (ATD) is a very challenging problem for the Army in ground-to-ground scenarios using
infrared (IR) sensors. I propose an ATD algorithm based on vector quantization (VQ). VQ is typically used for image
compression where a codebook is created using the Linde Buzo Gray (LBG) algorithm from an example image. The
codebook will be trained on clutter images containing no targets thus creating a clutter codebook. The idea is to encode
and decode new images using the clutter codebook and calculate the VQ error. The error due solely to the compression
will be approximately consistent across the image. In the areas that contain new objects in the scene (objects the
codebook has not been trained on) we should see the consistent compression error plus an increased "non-training error"
due to the fact that pixel blocks representing the new object are not included in the codebook. After the decoding
process, areas in the image with large overall error will correlate to pixel blocks not in the codebook. The Kolomogorov-Smirnov distance is used to classify new objects from a reference clutter error distribution. The VQ algorithm trains on
clutter so it will never have a problem with new targets like many "trained algorithms". The algorithm is run over a data
set of images and the results show that the VQ detection algorithm performs as well as the Army benchmark algorithm.