Camouflage Defect Identification: A Novel Approach

Bhajantri, Nagappa U.; Nagabhushan, P.

doi:10.1109/icit.2006.34

Cited by 61 publications

(14 citation statements)

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“…This omission means that the confidence and extendibility of current models and metrics are low, falling short in ability to cope with high dynamic range (i.e. natural) (Bhajantri and Nagabhushan, 2006;Hecker, 1992;Sengottuvelan et al, 2008), semi-automatic labelling or tracking of the target (Chandesa et al, 2009), non-probabilistic and non-scalable distance metrics to high dimensional data or multiple observations given many images (Birkemark, 1999;Heinrich and Selj, 2015;Kiltie et al, 1995). Human behavioural data needs to be recorded to assess the coherence between human and model observers.…”

Section: Introductionmentioning

confidence: 99%

Camouflage assessment: Machine and human

Volonakis

Matthews

Liggins

et al. 2018

Computers in Industry

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A vision model is designed using low-level vision principles so that it can perform as a surrogate human observer. In a camouflage assessment task, using military patterns in an outdoor environment, human performance at recognition and detection is compared with the surrogate human observer. This involved field data acquisition and subsequent image calibration, a human experiment, and the design of the vision model. Human and machine performance, at recognition and detection, of military patterns in two environments was found to correlate highly.

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Section: Introductionmentioning

confidence: 99%

Camouflage assessment: Machine and human

Volonakis

Matthews

Liggins

et al. 2018

Computers in Industry

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“…Bhajantri and Nagabhushan 2 employed a grey level cooccurrence matrix to identify camouflaged defects within a small region of an image. 2 Gilmore et al 3 proposed a vision model and a way to use that model to compare different synthetic images to understand the factors affecting target conspicuity. 3 Muller presented a computer-aided method to assess camouflage in real-time.…”

Section: Introductionmentioning

confidence: 99%

Developing a similarity index for static camouflaged target detection

Lin

Chang

2013

The Imaging Science Journal

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Measurement of camouflage performance is of fundamental importance for military stealth applications. The goal of camouflage assessment algorithms is to automatically assess the effectiveness of camouflage in agreement with human detection responses. We propose a new method for computational evaluation of camouflage effectiveness. We evaluate the performance of the new method and other existing algorithms in a comparative setting which utilised an extensive human visual experiment. The results show that the new method is correlated with the human target searching results with higher correlations than the existing algorithms.

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“…Neider and Zelinsky discussed in [29] the detection of camouflaged targets by looking through the distracters or by scrutinizing the target-similar background. In [3], Bhajantri and Nagabhushan proposed a technique to detect the camouflaged defect. Here, co-occurrence matrix-based texture features are computed within a small image region.…”

Section: Texture Featuresmentioning

confidence: 99%

Camouflaged Object Detection and Tracking: A Survey

Mondal

2020

Int. J. Image Grap.

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Moving object detection and tracking have various applications, including surveillance, anomaly detection, vehicle navigation, etc. The literature on object detection and tracking is rich enough, and there exist several essential survey papers. However, the research on camouflage object detection and tracking is limited due to the complexity of the problem. Existing work on this problem has been done based on either biological characteristics of the camouflaged objects or computer vision techniques. In this paper, we review the existing camouflaged object detection and tracking techniques using computer vision algorithms from the theoretical point of view. This paper also addresses several issues of interest as well as future research direction in this area. We hope this paper will help the reader to learn the recent advances in camouflaged object detection and tracking.

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Camouflage Defect Identification: A Novel Approach

Cited by 61 publications

References 1 publication

Camouflage assessment: Machine and human

Camouflage assessment: Machine and human

Developing a similarity index for static camouflaged target detection

Camouflaged Object Detection and Tracking: A Survey

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