Journal Articles
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/19884
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Item Automatic shadow removal algorithm for VOP, DWT based watermarking algorithm for VOP and generation of super resolved VOP(2011) Pais, A.R.; D'Souza, J.; Reddy, R.M.; Hari Krishna, P.Removal of shadow from Video Object Planes (VOPs) will assist in surveillance applications for comprehensive detection of activities. We have proposed a method for removal of shadows from the VOP. Also noise removal is done using existing methods from the VOP. To authenticate the surveillance VOP, digital watermarking is used. We have proposed digital watermarking using localized Biorthogonal wavelets for VOP. Super-resolved VOP is generated using multi-frame method. Edge model based super resolution method is used to get the better results. Also the effect of digital watermarking is studied for the super-resolved VOP. A number of test cases have been proposed and found out a best method for video surveillance application. Our proposed super resolution (SR) method gives better results than bilinear and bi-cubic methods.Item A new non-local maximum likelihood estimation method for Rician noise reduction in magnetic resonance images using the Kolmogorov-Smirnov test(Elsevier, 2014) Rajan, J.; den Dekker, A.J.; Sijbers, J.Denoising algorithms play an important role in the enhancement of magnetic resonance (MR) images. Effective denoising is vital for proper analysis and accurate quantitative measurements from MR images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising MR images. Among the ML based methods, the recently proposed non-local maximum likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non-local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation resulting in over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive and statistically supported way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness. © 2013 Elsevier B.V.Item A nonlocal maximum likelihood estimation method for enhancing magnetic resonance phase maps(Springer London, 2017) Sudeep, P.V.; Ponnusamy, P.; Kesavadas, C.; Sijbers, J.; den Dekker, A.J.; Rajan, J.A phase map can be obtained from the real and imaginary components of a complex valued magnetic resonance (MR) image. Many applications, such as MR phase velocity mapping and susceptibility mapping, make use of the information contained in the MR phase maps. Unfortunately, noise in the complex MR signal affects the measurement of parameters related to phase (e.g, the phase velocity). In this paper, we propose a nonlocal maximum likelihood (NLML) estimation method for enhancing phase maps. The proposed method estimates the true underlying phase map from a noisy MR phase map. Experiments on both simulated and real data sets indicate that the proposed NLML method has a better performance in terms of qualitative and quantitative evaluations when compared to state-of-the-art methods. © 2016, Springer-Verlag London.Item Non-local means image denoising using shapiro-wilk similarity measure(Institute of Electrical and Electronics Engineers Inc., 2018) Yamanappa, W.; Sudeep, P.V.; Sabu, M.K.; Rajan, J.Most of the real-time image acquisitions produce noisy measurements of the unknown true images. Image denoising is the post-acquisition technique to improve the signal-to-noise ratio of the acquired images. Denoising is an essential pre-processing step for different image processing applications such as image segmentation, feature extraction, registration, and other quantitative measurements. Among different denoising methods proposed in the literature, the non-local means method is a preferred choice for images corrupted with an additive Gaussian noise. A conventional non-local means filter (CNLM) suppresses noise in a given image with minimum loss of structural information. In this paper, we propose modifications to the CNLM algorithm where the samples are selected statistically using Shapiro-Wilk test. The experiments on standard test images demonstrate the effectiveness of the proposed method. © 2013 IEEE.Item A synergistic combination of Asymptotic Computational Fluid Dynamics and ANN for the estimation of unknown heat flux from fin heat transfer(Elsevier B.V., 2018) Kumar, H.; Gnanasekaran, N.This paper deals with conjugate heat transfer from a rectangular fin. The problem consists of mild steel (250 × 150 × 6 mm) fin placed vertically on aluminium base (250 × 150 × 4 mm). The aluminium plate is subjected to an unknown heat flux at the base. The fin set-up is modelled using ANSYS fluent 14.5. The fin geometry is surrounded by extended domain filled with air so as to account for natural convection conjugate heat transfer. Grid independence study is carried out to fix the number of grids. A simple correlation using Asymptotic Computational Fluid Dynamics (ACFD) is developed and the same is used as a forward model to obtain the temperature distribution considering heat flux as the input. The problem is treated as an inverse problem in which a non-iterative method, ANN is used as the inverse model to estimate the unknown heat flux from the information of temperature. The results of the forward model and the ANN predicted values are in close agreement with error less than 1%. Effect of noise on the unknown parameter is also studied extensively. © 2017 Faculty of Engineering, Alexandria UniversityItem PRIDES: A Probabilistic Model for Recurrent User Interest Drift Identification in Session-Based Recommendation(Institute of Electrical and Electronics Engineers Inc., 2025) Chaitanya, V.S.; Santhi Thilagam, P.S.A Session-Based Recommendation (SBR) identifies correlations among session interactions to understand user preferences and generate appropriate recommendations. A key challenge in this context is the dynamic change in user preferences, particularly when preferences disappear and reappear within a session, a phenomenon referred to as Recurrent User Interest Drift (RUID). Effectively capturing RUID is significant for aligning recommendations with ongoing user preferences. Existing SBR approaches often misclassify user preferences that differ from other session interactions as noise (unintentional interactions), relying on dwell time (the amount of time a user spends viewing an item) or neighboring sessions, thereby overlooking their potential reappearance as RUID later in the session. To the best of our knowledge, this work is the first to address the challenge of identifying RUID in SBR. The proposed approach assigns probabilistic scores to each interaction by considering its similarity to the immediate previous interaction, its inclusion among popular items (items with a higher number of interactions), its similarity to previous interactions, and the dwell time. As user preference may reappear anytime during the session, and RUID identification requires analyzing subsequent interactions, a list-based approach is used to retain these interactions until the session ends, enabling effective RUID identification. The matrix factorization-based attentive session encoder incorporates both short-term (ongoing) preferences and long-term (historical) preferences to generate personalized recommendations. Experimental results on three benchmark datasets, Yoochoose, Last.fm, and Gowalla, show that our method outperforms 14 state-of-the-art baselines, achieving an improvement of 2.28% in recall@20 and 1.39% in Mean Reciprocal Rank (MRR@20) on Yoochoose, 3.58% in recall@20 and 2.70% in MRR@20 on Last.fm, and 5.35% in recall@20 and 4.17% in MRR@20 on Gowalla datasets. © 2013 IEEE.