Abstract
Segmentation plays a functional role in most of the image processing operations. In applications like object recognition systems, the efficiency of segmentation must be assured. Most of the existing segmentation techniques have failed to filter shadows and reflections from the image and the computation time required is marginally high to use in real time applications. This paper proposes a novel method for an unsupervised segmentation of foreground objects from a non-uniform image background. With this approach, false detections due to shadows, reflections from light sources and other noise components can be avoided at a fair level. The algorithm works on an adaptive thresholding, followed by a series of morphological operations in low resolution downsampled image and hence, the computational overhead can be minimized to a desired level. The segmentation mask thus obtained is then upsampled and applied to the full resolution image. So the proposed technique is best suited for batch segmentation of high-resolution images.
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Niyas, S., Reshma, P., Thampi, S.M. (2016). A Color Image Segmentation Scheme for Extracting Foreground from Images with Unconstrained Lighting Conditions. In: Corchado Rodriguez, J., Mitra, S., Thampi, S., El-Alfy, ES. (eds) Intelligent Systems Technologies and Applications 2016. ISTA 2016. Advances in Intelligent Systems and Computing, vol 530. Springer, Cham. https://doi.org/10.1007/978-3-319-47952-1_1
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