Object Detection in Camouflaged Environment with Texture Statistical Features
Chennamsetty Pulla Rao1, A. Guruva Reddy2, C. B. Rama Rao3
1Chennamsetty Pulla Rao, Research Scholar, Department of ECE, JNTU, Kakinada (Andhra Pradesh), India.
2Dr. A. Guruva Reddy, Professor, Department of ECE, India.
3Dr. C. B. Rama Rao, Professor, Department of ECE, NIT Warangal (Telangana), India.
Manuscript received on 24 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 1339-1344 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B12510782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1251.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Texture Smoothing, DWT, GLCM, Statistical Characteristics.
Scope of the Article: Smart Learning Methods and Environments