Automatic object detection is one of the basic tasks performed by an image understanding system. Object detection approaches need to perform accurately and robustly over a wide range of scenes. Although a number of detection approaches have been developed and reported, a need remains for standards by which to judge the relative merits of such approaches. Image characteristics, object characteristics, and detection methodology are recognized as the main variables affecting object detection. A basis for their quantitative analysis and evaluation is developed. This research keeps object detection methodology constant while varying image and object characteristics to develop a set of quantiative standards. This requires an ability to derive a quantitative measure for the "clutter" observed in an image. A performance index for object detection approaches, as a function of scene nature, is valuable. Current approaches to image clutter or quality characterization are studied and a new measure based on image texture content and object characteristics is proposed. An extensive set of experimental studies is utilized to evaluate this texture-based image clutter (TIC) measure. TIC is shown to be better suited than other reported clutter measures because of its ability to accurately quantify perceptual effects and to serve as a robust indicator ofthe object detection and false alarm rates as a function of image clutter.