Faculty Publications
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Publications by NITK Faculty
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Item An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions(Elsevier Ltd, 2016) Suresh, S.; Lal, S.Satellite image segmentation is challenging due to the presence of weakly correlated and ambiguous multiple regions of interest. Several bio-inspired algorithms were developed to generate optimum threshold values for segmenting such images efficiently. Their exhaustive search nature makes them computationally expensive when extended to multilevel thresholding. In this paper, we propose a computationally efficient image segmentation algorithm, called CSMcCulloch, incorporating McCulloch's method for lévy flight generation in Cuckoo Search (CS) algorithm. We have also investigated the impact of Mantegna?s method forlévy flight generation in CS algorithm (CSMantegna) by comparing it with the conventional CS algorithm which uses the simplified version of the same. CSMantegna algorithm resulted in improved segmentation quality with an expense of computational time. The performance of the proposed CSMcCulloch algorithm is compared with other bio-inspired algorithms such as Particle Swarm Optimization (PSO) algorithm, Darwinian Particle Swarm Optimization (DPSO) algorithm, Artificial Bee Colony (ABC) algorithm, Cuckoo Search (CS) algorithm and CSMantegna algorithm using Otsu's method, Kapur entropy and Tsallis entropy as objective functions. Experimental results were validated by measuring PSNR, MSE, FSIM and CPU running time for all the cases investigated. The proposed CSMcCulloch algorithm evolved to be most promising, and computationally efficient for segmenting satellite images. Convergence rate analysis also reveals that the proposed algorithm outperforms others in attaining stable global optimum thresholds. The experiments results encourages related researches in computer vision, remote sensing and image processing applications. © 2016 Elsevier Ltd. All rights reserved.Item Two-Dimensional CS Adaptive FIR Wiener Filtering Algorithm for the Denoising of Satellite Images(Institute of Electrical and Electronics Engineers, 2017) Suresh, S.; Lal, S.In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adaptive finite-impulse response (FIR) filters driven by an optimization algorithm to self-adjust the filter coefficients, with applications in different domains of research. For signal processing applications, FIR Wiener filters are commonly used for noisy signal restorations by computing the statistical estimates of the unknown signal. In this paper, a novel 2-D Cuckoo search adaptive Wiener filtering algorithm (2D-CSAWF) is proposed for the denoising of satellite images contaminated with Gaussian noise. Till date, study based on 2-D adaptive Wiener filtering driven by metaheuristic algorithms was not found in the literature to the best of our knowledge. Comparisons are made with the most studied and recent 2-D adaptive noise filtering algorithms, so as to analyze the performance and computational efficiency of the proposed algorithm. We have also included comparisons with recent adaptive metaheuristic algorithms used for satellite image denoising to ensure a fair comparison. All the algorithms are tested on the same satellite image dataset, for denoising images corrupted with three different Gaussian noise variance levels. The experimental results reveal that the proposed novel 2D-CSAWF algorithm outperforms others both quantitatively and qualitatively. Investigations were also carried out to examine the stability and computational efficiency of the proposed algorithm in denoising satellite images. © 2008-2012 IEEE.Item Transformer assisted framework for automated multi-class abnormality classification for video capsule endoscopy(Institute of Physics, 2025) Prabhu, M.M.; Kaliki, V.S.; Lal, S.Video Capsule Endoscopy (VCE) is a minimally invasive imaging technique used for diagnosing gastrointestinal (GI) disorders, enabling detailed visualization of the digestive tract. This study introduces CASCRNet, a novel and parameter-efficient deep learning architecture designed to enhance interpretability and computational efficiency in multi-class abnormality classification for VCE. CASCRNet integrates focal loss, Atrous Spatial Pyramid Pooling, and Shared Channel Residual blocks to improve feature extraction and address class imbalance. In addition to CASCRNet, this study conducts a comprehensive evaluation of several deep learning models, including ResNet50, DenseNet121, RCCGNet, Hiera, and AIMv2. Among these, AIMv2, a fine-tuned transformer-based model, achieved the highest overall performance, serving as a new benchmark for accuracy. The proposed framework demonstrates robust results on the Capsule Vision 2024 dataset and highlights the potential of both lightweight and transformer-based solutions to improve diagnostic efficiency and clinical workflow in gastrointestinal imaging. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.