Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item A Comparative Study of Illumination Invariant Techniques in Video Tracking Perspective(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2020) Asha, C.S.; Narasimhadhan, A.V.Object tracking is being utilized in the field of computer vision over decades for video surveillance, human–computer interaction and robotic applications. Even though the state-of-the-art tracking technology is rapidly growing, few issues are still challenging such as illumination variation, pose variation, scale changes, occlusion, etc. Among these challenges, sudden illumination variation is more complicated which is not solved completely. Most of the current trackers, indeed work under controlled illumination conditions in outdoor and indoor environments. In this work, we study the effect of adding the photometric normalization techniques prior to tracking in order to minimize the drift during abrupt light changes of the median flow tracker (MFT). The tracker under investigation is based on the optical flow method and achieved remarkable results in the tracking literature. However, it drifts off during sudden illumination variation. To resolve this problem, pre-processing technique is incorporated just before tracking. Hence, we present an experimental study of various pre-processing techniques to improve the accuracy of the MFT. A total of eight state-of-the-art normalization techniques are summarized and tested in video tracking perspective. The experiments are carried out with the video sequences obtained from the object tracking benchmark dataset posing sudden illumination change as a challenge to analyze the modified tracker. A comparative analysis indicates that the modified tracker outperforms the baseline tracker in terms of precision score and overlap score. © 2019 IETE.Item Performance evaluation of dimensionality reduction techniques on hyperspectral data for mineral exploration(Springer Science and Business Media Deutschland GmbH, 2023) C, D.; Shetty, A.; Narasimhadhan, A.V.With recent advances in hardware and wide range of applications, hyperspectral remote sensing proves to be a promising technology for analysing terrain. However, the sheer volume of bands, strong inter band correlation and redundant information makes interpretation of hyperspectral data a tedious task. Aforementioned issues can be addressed to a considerable extent by reducing the dimensionality of hyperspectral data. Though plethora of algorithms exist to downsize hyperspectral data, quality assessment of these techniques remains unanswered. Since Dimensionality Reduction (DR) is a special case of unsupervised learning, classification accuracy cannot be directly used to compare the performance of different dimensionality reduction techniques. As a consequence, a different type of goodness measure is essential which is expected to be easily interpretable, robust against outliers and applicable to most algorithms and datasets. In this paper, fifteen popular dimensionality reduction algorithms are reviewed, evaluated and compared on hyperspectral dataset for mineral exploration. The performance of various DR algorithms is tested on hyperspectral mineral data since the extensive study of DR for mineral mapping is scarce compared to land cover mapping. Also, DR techniques are evaluated based on coranking criteria which is independent of label information. This facilitates to demonstrate the robust technique for mineral mapping and also provides meaningful insight into topology preservation. These techniques play a vital role in mineral exploration since in field observation is expensive, time consuming and requires more man power. From experimental results it is evident that, deep autoencoders provide better embedding with a quality index value of 0.9938, when K = 120 compared to other existing nonlinear techniques. The conclusions presented are unique since previous studies have not evaluated the results qualitatively and comparison between conventional machine learning and deep learning algorithms is limited. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Machine learning techniques for periodontitis and dental caries detection: A narrative review(Elsevier Ireland Ltd, 2023) Radha, R.C.; Raghavendra, B.S.; Subhash, B.V.; Rajan, J.; Narasimhadhan, A.V.Objectives: In recent years, periodontitis, and dental caries have become common in humans and need to be diagnosed in the early stage to prevent severe complications and tooth loss. These dental issues are diagnosed by visual inspection, measuring pocket probing depth, and radiographs findings from experienced dentists. Though a glut of machine learning (ML) algorithms has been proposed for the automated detection of periodontitis, and dental caries, determining which ML techniques are suitable for clinical practice remains under debate. This review aims to identify the research challenges by analyzing the limitations of current methods and how to address these to obtain robust systems suitable for clinical use or point-of-care testing. Methods: An extensive search of the literature published from 2015 to 2022 written in English, related to the subject of study was sought by searching the electronic databases: PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and ScienceDirect. Results: The initial electronic search yielded 1743 titles, and 55 studies were eventually included based on the selection criteria adopted in this review. Studies selected were on ML applications for the automatic detection of periodontitis and dental caries and related dental issues: Apical lessons, Periodontal bone loss, and Vertical root fracture. Conclusion: While most of the ML-based studies use radiograph images for the detection of periodontitis and dental caries, few pieces of the literature revealed that good diagnostic accuracy could be achieved by training the ML model even with mobile photos representing the images of dental issues. Nowadays smartphones are used in every sector for different applications. Training the ML model with as many images of dental issues captured by the smartphone can achieve good accuracy, reduce the cost of clinical diagnosis, and provide user interaction. © 2023 Elsevier B.V.Item Automatic method for contrast enhancement of natural color images(Korean Institute of Electrical Engineers, 2015) Shyam, L.; Narasimhadhan, A.V.The contrast enhancement is great challenge in the image processing when images are suffering from poor contrast problem. Therefore, in order to overcome this problem an automatic method is proposed for contrast enhancement of natural color images. The proposed method consist of two stages: in first stage lightness component in YIQ color space is normalized by sigmoid function after the adaptive histogram equalization is applied on Y component and in second stage automatic color contrast enhancement algorithm is applied on output of the first stage. The proposed algorithm is tested on different NASA color images, hyperspectral color images and other types of natural color images. The performance of proposed algorithm is evaluated and compared with the other existing contrast enhancement algorithms in terms of colorfulness metric and color enhancement factor. The higher values of colorfulness metric and color enhancement factor imply that the visual quality of the enhanced image is good. Simulation results demonstrate that proposed algorithm provides higher values of colorfulness metric and color enhancement factor as compared to other existing contrast enhancement algorithms. The proposed algorithm also provides better visual enhancement results as compared with the other existing contrast enhancement algorithms. © The Korean Institute of Electrical Engineers.Item Modified null space strategy to solve consensus problem(University of Kuwait, 2016) Sharma, A.; Kurapati, N.G.; Jagannath, R.P.K.; Wira, P.; Lal, S.; Narasimhadhan, A.V.In the domain of multi robot systems, several applications necessitate agreement of all the individual robots at consensus/rendezvous point. Such an agreement can only be achieved by means of a control strategy. However, presence of obstacles in the navigation-environment makes the achievement of control objective floundering. This paper accentuates the failure of extant null space based control strategy to circumvent rectangular obstacles by means of mathematical proofs and extensive simulation studies. To over-come these short-comings, a modified null space based control strategy is proposed to solve the consensus problem. Proposed control strategy is tested in a complex environment consisting of rectangular and concave obstacles by means of computer simulations. Finally, a qualitative comparative analysis is presented to contrast the differences between conventional null space based strategy and the proposed modified null space strategy. © 2016, University of Kuwait. All rights reserved.Item Robust infrared target tracking using discriminative and generative approaches(Elsevier B.V., 2017) Asha, C.S.; Narasimhadhan, A.V.The process of designing an efficient tracker for thermal infrared imagery is one of the most challenging tasks in computer vision. Although a lot of advancement has been achieved in RGB videos over the decades, textureless and colorless properties of objects in thermal imagery pose hard constraints in the design of an efficient tracker. Tracking of an object using a single feature or a technique often fails to achieve greater accuracy. Here, we propose an effective method to track an object in infrared imagery based on a combination of discriminative and generative approaches. The discriminative technique makes use of two complementary methods such as kernelized correlation filter with spatial feature and AdaBoost classifier with pixel intesity features to operate in parallel. After obtaining optimized locations through discriminative approaches, the generative technique is applied to determine the best target location using a linear search method. Unlike the baseline algorithms, the proposed method estimates the scale of the target by Lucas-Kanade homography estimation. To evaluate the proposed method, extensive experiments are conducted on 17 challenging infrared image sequences obtained from LTIR dataset and a significant improvement of mean distance precision and mean overlap precision is accomplished as compared with the existing trackers. Further, a quantitative and qualitative assessment of the proposed approach with the state-of-the-art trackers is illustrated to clearly demonstrate an overall increase in performance. © 2017 Elsevier B.V.Item Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique(Elsevier Ltd, 2018) Yamanakkanavar, Y.; Madipalli, P.; Rajan, J.; Kumar, P.K.; Narasimhadhan, A.V.Cardiovascular diseases are the third leading cause of death worldwide. The primitive indication of the possible onset of a cardiovascular disease is atherosclerosis, which is the accumulation of plaque on the arterial wall. The intima-media thickness (IMT) of the common carotid artery is an early marker of the development of cardiovascular disease. The computation of the IMT and the delineation of the carotid plaque are significant predictors for the clinical diagnosis of the risk of stroke. For a robust diagnosis, carotid ultrasound images must be free from speckle noise. To address this problem, we use state-of-the-art despeckling and enhancement methods in this work. Many edge-based methods for IMT estimation have been proposed to overcome the limitations of manual segmentation. In this paper, we present a fully automated region-of-interest (ROI) extraction and a threshold-based segmentation of the intima media complex (IMC) using a wind driven optimization (WDO) technique. A quantitative evaluation is carried out on 90 carotid ultrasound images of two different datasets. The obtained results are compared with those of state-of-the-art techniques such as a model-based approach, a dynamic programming method, and a snake segmentation method. The experimental analysis shows that the proposed method is robust in measuring the IMT in carotid ultrasound images. © 2017 Elsevier LtdItem Carotid wall segmentation in longitudinal ultrasound images using structured random forest(Elsevier Ltd, 2018) Yamanakkanavar, Y.; Asha, C.S.; Teja A, H.S.; Narasimhadhan, A.V.Edge detection is a primary image processing technique used for object detection, data extraction, and image segmentation. Recently, edge-based segmentation using structured classifiers has been receiving increasing attention. The intima media thickness (IMT) of the common carotid artery is mainly used as a primitive indicator for the development of cardiovascular disease. For efficient measurement of the IMT, we propose a fast edge-detection technique based on a structured random forest classifier. The accuracy of IMT measurement is degraded owing to the speckle noise found in carotid ultrasound images. To address this issue, we propose the use of a state-of-the-art denoising method to reduce the speckle noise, followed by an enhancement technique to increase the contrast. Furthermore, we present a novel approach for an automatic region of interest extraction in which a pre-trained structured random forest classifier algorithm is applied for quantifying the IMT. The proposed method exhibits IMTmean ± standard deviation of 0.66mm ± 0.14, which is closer to the ground truth value 0.67mm ± 0.15 as compared to the state-of-the-art techniques. © 2018 Elsevier LtdItem A robust framework for quality enhancement of aerial remote sensing images(Elsevier B.V., 2018) Karuna Kumari, E.; Das, D.; Suresh, S.; Lal, S.; Narasimhadhan, A.V.This paper proposes a robust framework for quality restoration of remotely sensed aerial images. Proposed framework works in three steps: (1) Efficient color balancing and saturation adjustment, (2) Efficient color restoration, (3) Modified contrast enhancement using particle swarm optimization (PSO). In order to show the robustness, step-wise results of proposed framework is illustrated. Several aerial images from two publically available datasets are tested to support the robustness of the proposed framework over existing image quality restoration methods. The experimental results of proposed framework and other existing quality restoration methods are compared in terms of NIQMC, BIQME, MICHELSON, DE, EME and PIXDIST along with visual experimental results. Based on experimental results conducted on several aerial images suggest that the proposed framework is outperform over existing quality restoration methods. © 2018 Elsevier B.V.Item Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine(Springer Verlag, 2019) Yamanakkanavar, Y.; Hema Sai Teja, A.; Narasimhadhan, A.V.The intima media thickness (IMT) of common carotid artery is a reliable measure of cardiovascular diseases. The quantification of IMT is the biomarker for clinical diagnosis of the risk of stroke. For robust measurement of IMT, the ultrasound carotid images must be free of speckle noise. To reduce the effect of speckle noise in the carotid ultrasound image, we propose to use Bayesian least square estimation filter. In addition, the enhancement step based on total variation-L1(TV-L1) norm is performed to improve the robustness. Further more, we present a fully automated region of interest and segmentation of intima media complex based on support vector machine. The quantitative evaluation is carried out on 49 carotid ultrasound images. The proposed algorithm is compared with gradient-based methods like model based, dynamic programming, snake algorithm, and classifier-based segmentation using a neural network algorithm. The performance of the experimental result shows that the proposed method is robust in quantifying the IMT in carotid ultrasound images. © 2018, King Fahd University of Petroleum & Minerals.
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