Abstract
Computer vision is the way in which the computer perceives a certain image. Background and foreground detection of an image are based on the concept of computer vision. Traditional approaches in background and foreground detection of an image imply clustering algorithms like K-means clustering, Gaussian mixture model to compute the result in the spatial domain. In spatial domain, we take into account the pixels of an image to classify them as background or foreground. In this paper, we have reviewed the process already been done in spatial domain, then we study some of the state-of-the-art background detection techniques in digital image processing and propose a novel approach to shift the domain of input images to frequency while applying the same algorithms for detection. We have used the concept in which we take into consideration the frequency rather than pixels of an image. The concept of fast Fourier transform (FFT) has been used to determine the frequency. With this solution concept, we aim at reducing the variance in input image by smoothing out the frequency domain image and experimentally demonstrate that the transition into the frequency domain outperforms the majority of techniques employed in spatial domain for background detection.
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Kapoor, S., Pillai, M.S., Nagpal, A. (2021). Effective Background and Foreground Segmentation Using Unsupervised Frequency Domain Clustering. In: Abraham, A., Castillo, O., Virmani, D. (eds) Proceedings of 3rd International Conference on Computing Informatics and Networks. Lecture Notes in Networks and Systems, vol 167. Springer, Singapore. https://doi.org/10.1007/978-981-15-9712-1_8
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DOI: https://doi.org/10.1007/978-981-15-9712-1_8
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