Abstract
Detecting and classifying the target objects in the underwater videos is the primary and essential operation in this modern era. The present works proposed the pattern extraction, segmentation and classification techniques for target object detection in underwater images. These techniques were found to be lacking with the following limitations, such as inefficient computation, inaccurate results, and increased cost-efficiency. Thus, this work targets to introduce a new pattern extraction based classification system for processing underwater images. As the first and foremost thing, the input underwater picture is preprocessed for eliminating the noise by applying the Laplacian Bellman filtering technique. After that, the histogram equalization is utilized to enhance the quality of the source picture. Once the image is noise-free, the patterns are extracted from it with the help of the likelihood gradient pattern technique. Subsequently, the label formation and blob extraction processes are performed in an orderly manner to track the target object accurately. In this work, the novelty is seen implemented in both the preprocessing and the feature analysis stages by developing a novel technique. The significant advantages of this work are: yielding the improved image quality, by being the efficient texture pattern extractor, and they also do not require any additional information and adjustments. While under simulation, the fulfilment attained in the proposed techniques and the appropriate literature methodologies are evaluated and validated with the performance measures like accuracy, sensitivity, specificity, average time, precision, F1-measure, and the filtering features like entropy, contrast, and the correlation.
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"The dataset supporting the conclusions of this article are available in the [UNDERWATER BENCHMARK DATASET FOR TARGET DETECTION AGAINST COMPLEX BACKGROUND] from the link [https://f4k.dieei.unict.it/datasets/bkg_modeling/]." There are 14 videos of benchmark dataset classified into seven different classes of background modelling. The dataset comprises six types of sequences such as blurred, complex background, crowded, dynamic background and luminosity changes, camouflage foreground objects, and the hybrid courses are involved in the images. The PerLa tool (Kavasidis et al. 2014) is used to label the underwater video dataset and is represented as the ground truth. For each video, about 30 images were annotated and are provided as binary masks. In total, more than 3500 objects were labelled. The ground truths are masks completely black, which are meant to assess performance when only background moving objects are present in the image. When correctly classified (true positive), the target objects are 69,850.
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RS, CK, and AB conceived and designed the research. RS performed the experiments and analyzed the data. RS, CK, and AB wrote and edited the manuscript. All the authors read and approved the final manuscript.
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Rajasekar, M., Celine Kavida, A. & Anto Bennet, M. A pattern analysis based underwater video segmentation system for target object detection. Multidim Syst Sign Process 31, 1579–1602 (2020). https://doi.org/10.1007/s11045-020-00721-4
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DOI: https://doi.org/10.1007/s11045-020-00721-4