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Author: search.filters.author.Lone, Z.A.Subject: search.filters.subject.Object detection

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    Object detection in hyperspectral images
    (Elsevier Inc., 2022) Lone, Z.A.; Pais, A.R.
    Object Detection is a task of estimating and locating an object precisely in an image. It is a fundamental problem in computer vision and has been studied extensively in low dimensional images like RGB, grayscale, etc. High dimensional images like Hyperspectral images (HSI) contain ample information and are very powerful in enhancing the fine spectral differences between different objects. The advancement in spectral sensor technologies is making hyperspectral data more readily available, making it a promising technology for image analysis tasks. HSI has been explored in the fields of remote sensing, biomedical imaging, mineral classification, goods quality assessment, and object detection etc. The research concerning object detection in HSI has been gathering pace in recent times. This survey paper is an attempt to create a resource for researchers in the field. This paper provides a comprehensive review of both Supervised and Salient object detection. Moreover, a collection of important datasets is mentioned. We conclude the paper by mentioning research challenges and the future directions for the research in the field. © 2022 Elsevier Inc.
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    Salient object detection in HSI using MEV-SFS and saliency optimization
    (Springer Science and Business Media Deutschland GmbH, 2025) Lone, Z.A.; Pais, A.R.
    The existing methods in salient object detection (SOD) in hyperspectral images (HSI) have used different priors like center prior, boundary prior to procure cues to find the salient object. These methods fail, if the salient object is slightly touching the boundary. So, we extrapolate boundary connectivity, a measure to check if the object touches the boundary. The salient object is obtained by using background and foreground cues, which are calculated using boundary connectivity and contrast map, respectively. Also, to reduce the information redundancy and hence time complexity, we select top three most informative bands using different feature selection and feature extraction algorithms. The proposed algorithm is tested on HS-SOD dataset. It is observed that the proposed algorithm performs better than the state-of-the-art techniques in almost all the metrics, such as Precision (0.57), Recall (0.46), f1 score (0.51), CC (0.43), NSS (2.13), and MAE (0.09). In addition, we performed a comparative analysis of four different feature selection (MEV-SFS, OPBS) and feature extraction (PCA, MNF) algorithms in the context of SOD in HSI. We observed that feature selection algorithms are computationally efficient with OPBS and MEV-SFS taking about 7.98 and 8.34 s on average to reduce the feature space, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.