Object detection in camouflaged environment with texture statistical features

Abstract

In camouflage image foreground will be hidden in the background image. Camouflage images can be natural and artificial. Detection of such hidden objects becomes difficult for a machine vision system and takes much time to detect and recognize whereas it is not difficult for Human Perception. Detection process involves two phases; feature search that helps in grasping the characteristic entities of an image like colour, shape, texture, pattern etc., and conjunction search which is useful for recognition of clues from multiple features. The background of the camouflage image may be uniform or non-uniform characteristic entities. Different operations can be performed on characteristic entities to make the background disappear in order to detect the foreground image. In this paper, survey on DE camouflaging methods and framework is proposed to detect objects in camouflage environments. Under this framework, textural smoothing followed by statistical characteristics is used to detect the camouflaged objects that will show a better performance based on statistical features. De-camouflaging can be used in war field, where soldiers hide themselves from the enemies with the texture similar to that of their background and decamouflaging is used to reveal the camouflages in the background.