Studies in robust approaches to object detection in high-clutter background

Object detection approaches need to perform accurately and robustly over a wide range of scenes. It would be quite valuable if one can devise a performance index for an object detection approach as a function of the nature of a particular scene. Basically this requires an ability to derive a quantitative measure for the 'clutter' observed in an image. Most images of interest are texture-rich i.e. the important perceptual properties are based upon the spatial arrangements of simple patterns which might be regular in nature. As a result, it is natural to utilize texture analysis based operators to define the measure of image quality of 'clutter' that is being sought. It has been proven that the gray level cooccurence (GLC) matrices of an image embody important texture information, and the image can indeed be reconstructed from these matrices. Hence it is proposed that GLC-based measures be derived and used to quantify image quality. Current approaches are based on only one of several important perceptually meaningful measures which can be computed from GLC matrices. Prior work done in this area is assessed in this paper. The derivation of the image quality measures from GLC matrices is currently being researched. This paper presents a discussion of these issues along with the objectives and results of an ongoing study involving object detection in high resolution aerial images.