In this paper, we use an adaptive AR (Auto regressive) model to optimally filter background texture from images. The filter maps background texture into minimum variance locally white noise. Ad additive point target signal, however, is unaffected by the filter thereby effectively maximizing the signal to noise/clutter ratio. Thus, filter output thresholding can detect anomalous pixels in the images. Additionally the paper introduces a false alarm rejection scheme based on the intersection of a Four Quadrant AR (Quad-AR) filter. The paper addresses the implicit background assumptions in this approach and the median filter approach to small target detection. An example application of the filter to infrared images of missiles immersed in intense sea glint is presented. The AR filter performance is compared to a median filter performance. It is shown that for the infrared sub-pixel missile over sea problem, the Quad-AR approach is substantially better than previous approaches.
Optimal point target detection using adaptive auto regressive background prediction
Signal and Data Processing of Small Targets 2000 ; 2000 ; Orlando,FL,United States
Proc. SPIE ; 4048
2000-07-13
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Depth Recovery Using an Adaptive Color-Guided Auto-Regressive Model
British Library Conference Proceedings | 2012
|Facial Expression Recognition Using Auto-Regressive Models
British Library Conference Proceedings | 2006
|Weather-Weighted Periodic Auto Regressive Models for Sector Demand Prediction
British Library Conference Proceedings | 2009
|Aviation Accident Prediction Based on Auto-Regressive Integrating Moving Average Method
Trans Tech Publications | 2013
|