Sensors for Desert Surveillance

Chauhan, Bharti; David, E; Datta, P. K.

doi:10.14429/dsj.55.2012

Cited by 2 publications

(1 citation statement)

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“…The quality of surveillance images is low due to several reasons such as: movement of objects [8]; use of low resolution cameras [9]; and environmental factors [10]. The environmental factors include rainfall, fog and snow [11,10,12]. These factors obscure object details and create noise which badly affects the performance of object detection and the recognition process [13].…”

Section: Introductionmentioning

confidence: 99%

An efficient image retrieval scheme for colour enhancement of embedded and distributed surveillance images

et al. 2016

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a b s t r a c tFrom the past few years, the size of the data grows exponentially with respect to volume, velocity, and dimensionality due to wide spread use of embedded and distributed surveillance cameras for security reasons. In this paper, we have proposed an integrated approach for biometric-based image retrieval and processing which addresses the two issues. The first issue is related to the poor visibility of the images produced by the embedded and distributed surveillance cameras, and the second issue is concerned with the effective image retrieval based on the user query. This paper addresses the first issue by proposing an integrated image enhancement approach based on contrast enhancement and colour balancing methods. The contrast enhancement method is used to improve the contrast, while the colour balancing method helps to achieve a balanced colour. Importantly, in the colour balancing method, a new process for colour cast adjustment is introduced which relies on statistical calculation. It adjusts the colour cast and maintains the luminance of the image. The integrated image enhancement approach is applied to the enhancement of low quality images produced by surveillance cameras. The paper addresses the second issue relating to image retrieval by proposing a content-based image retrieval approach. The approach is based on the three features extraction methods namely colour, texture and shape. Colour histogram is used to extract the colour features of an image. Gabor filter is used to extract the texture features and the moment invariant is used to extract the shape features of an image. The use of these three algorithms ensures that the proposed image retrieval approach produces results which are highly relevant to the content of an image query, by taking into account the three distinct features of the image and the similarity metrics based on Euclidean measure. In order to retrieve the most relevant images, the proposed approach also employs a set of fuzzy heuristics to improve the quality of the results further. The results show the proposed approaches perform better than the well-known existing approaches.

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Section: Introductionmentioning

confidence: 99%