BITS Faculty Publications
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Item Enhanced multispectral band-to-band registration using co-occurrence scale space and spatial confined ransac guided segmented affine transformation(IEEE, 2024-11) Rohil, Mukesh KumarBand-to-Band Registration (BBR) is a pre-requisite image processing operation essential for specific remote sensing multispectral sensors. BBR aims to align spectral wavelength channels at sub-pixel level accuracy over each other. The paper presents a novel BBR technique utilizing Co-occurrence Scale Space (CSS) for feature point detection and Spatial Confined RANSAC (SC-RANSAC) for removing outlier matched control points. Additionally, the Segmented Affine Transformation (SAT) model reduces distortion and ensures consistent BBR. The methodology developed is evaluated with Nano-MX multispectral images onboard the Indian Nano Satellite (INS-2B) covering diverse landscapes. BBR performance using the proposed method is also verified visually at a 4X zoom level on satellite scenes dominated by cloud pixels. The band misregistration effect on the Normalized Difference Vegetation Index (NDVI) from INS-2B is analyzed and cross-validated with the closest acquisition Landsat-9 OLI NDVI map before and after BBR correction. The experimental evaluation shows that the proposed BBR approach outperforms the state-of-the-art image registration techniques.Item Advanced Intelligent Tutoring Systems: Featuring the Learning Management Systems of the Future(Springer, 2024) Rohil, Mukesh KumarAn Intelligent Tutoring System (ITS) is employed with two goals in mind: (1) to deliver one-on-one smart teaching guidance that is superior to standard computer-assisted education and a skilled human instructor, and (2) to establish appropriate guidelines for designing and evaluating models of the academic process. The “intelligence” of an ITS is derived from the use of artificial intelligence techniques in four interacting components, namely (1) the knowledge base (to represent and manipulate domain knowledge), (2) the student's model (to represent student's current academic state), (3) Pedagogical Model (to incorporate and use the teaching strategies), and (4) User Interface Model (to make the system usable, and to establish effective communication between the user and the system). On the other hand, a Learning Management System (LMS), an online (may be web-based)-integrated software, is used for creating, delivering, tracking, and reporting educational courses or training resources and also helps in assessing their outcomes. In this paper, we discuss various existing Intelligent Tutoring Systems and systematically present the comparison of an ITS with a LMS. We emphasise that the ITS usage should be encouraged because of number of advantages, including improving both the learning curve and the learning experience, associated with an ITS over an LMS.Item SPRINT: Spectra Preserving Radiance Image Fusion Technique using holistic deep edge spatial attention and Minnaert guided Bayesian probabilistic model(Elsevier, 2023-04) Rohil, Mukesh KumarRemote Sensing Image Fusion produces a high resolution multispectral image by merging panchromatic image with the corresponding low resolution multispectral counterpart. The limitation of the existing image fusion techniques is that it lacks in maintaining the spectral characteristics of the multispectral image in the fused output. The motivation of our proposed work is to develop balanced and robust image fusion method, named Spectra Preserving Radiance Image Fusion Technique (SPRINT). The technique developed extracts deep edge map from medoid intensity matched image using Holistic Nested Edge Detection (HNED). Minnaert function is applied on denoised panchromatic radiance image along with Digital Elevation Model (DEM) and solar angles’ computation to determine the surface topography. SPRINT’s core design emphasizes on holistic deep edges for spatial attention and terrain guidance using minnaert parameter at each image pixel in a Spectra Preserving Bayesian Probabilistic Model. The unique data pre-processing engine generates fusion ready representative datasets to trigger SPRINT processing workflow. The Indian Cartosat-1 panchromatic and Resourcesat-2/2A multispectral sensors’ datasets along with IKONOS and USGS-NASA’s Landsat-8 OLI images covering diverse landscapes are used for image fusion evaluation and assessment. It has been found that SPRINT’s fusion performance is superior to the state-of-the-art (SOTA) image fusion methods in terms of both visual effects and quantitative metrics. The Normalized Difference Vegetation Index (NDVI) using SPRINT’s fused radiance image tends to have negligible deviation at various classes with respect to reference NDVI. It has been observed that SPRINT derived surface reflectance values have close agreement with original reflectance measurements.Item Exploring Possible Applications of ORB SLAM 2 in Education, Healthcare, and Industry: Insights into the Challenges, Features, and Effects(IEEE, 2023) Rohil, Mukesh KumarIn this research we explore the ORB SLAM 2 which is a state-of-the-art algorithm for Simultaneous Localization and Mapping (SLAM) and which has been a cornerstone of SLAM Algorithm developments. In this paper, we highlight and provide insight into the possible applications of ORB SLAM 2 in the domains of Education, Industry and Healthcare. During the explanation of the working of ORB SLAM 2, we highlight the challenges, its features and its effects. We explore on how ORB-SLAM 2 can be used for a variety of purposes, including in Autonomous Car Driving. Robotics, mapping the surroundings, Augmented and Virtual Reality.Item MIRACLE: multi-satellite Island image registration using anisotropic coherence locality enhanced nonlinear diffusion and Mahalanobis distance guided marginalization(Taylor & Francis, 2023-07) Rohil, Mukesh KumarFeature in an image plays a crucial role for geometric image registration. The geo-registration problem becomes difficult for scanty feature islands’ images captured by high and medium spatial resolution remote sensing satellites over deep ocean water. The article presents an automatic multi-satellite image registration methodology for scanty feature island scenes, termed as MIRACLE. In data pre-processing stage, the multi-spectral and reference image are enhanced using anisotropic coherence for better localized feature demarking of island regions. The input multi-spectral images are transformed using Principal Component Analysis (PCA) to maximize the variance information for improved feature matching with reference image. Enhanced features are detected and described using nonlinear diffusion filtering, and the matched control points are pruned using Mahalanobis distance guided marginalization optimization technique. The estimated affine parameters are applied to generate multi-satellite co-registered data products. MIRACLE is evaluated with multi-temporal Indian Resourcesat multi-spectral images and NASA-USGS Landsat−8 OLI panchromatic images that span from 5.0-metre spatial resolution to 15.0-metre spatial resolution and cover the Lakshadweep islands in deep ocean. The visual quality assessment indicates that different island regions are aligned at sub-pixel level registration accuracy. The matching accuracy of MIRACLE is quantified for multi-resolution images and is found to have 2.6% improvement in Correct Matching Ratio (CMR) as compared to the state-of-the-art feature based image registration techniques. The average Root Mean Square Error (RMSE) of island regions after precise geometric correction is found to be 0.45 pixel.Item Utilization of Augmented Reality Visualizations in Healthcare Education: Trends and Future Scope(IEEE, 2023) Rohil, Mukesh KumarAugmented reality (AR) is a recent technology with applications in multiple sectors, including the healthcare industry. AR incorporates (or overlays) digital or computer-generated data such as audio, video, images, and contact or haptic sensations into a real-time setting. Innovations in augmented reality can assist medical personnel in diagnosing, treating, and operating on patients more precisely. However, healthcare differs from other disciplines because it focuses on something that is unique, unpredictable, simultaneously extremely complex, and fragile: The Human Body. This paper aims to comprehend the application of augmented reality and visualizations in medical and healthcare education and practice. The novelty of the paper is that in addition to providing an overview of recent successful augmented reality applications in healthcare, we emphasize that educational use of these in the present form (or after appropriate adaptation) provide benefits to teachers, students and working healthcare professionals. some of the key benefits are fast learning and better knowledge retentionItem CLIM: Co-occurrence with Laplacian Intensity Modulation and Enhanced Color Space Transform for Infrared-Visible Image Fusion(Elsevier, 2023-12) Rohil, Mukesh KumarThermal infrared and multispectral visible remote sensing image fusion combines thermal image information with corresponding visible scene content to generate a better representative fused image. Thermal images can distinguish targets using difference in thermal radiation measurements, whereas visible images contain better texture detail in multispectral wavelength bands. The article presents a novel methodology named CLIM to sharpen coarser spatial resolution multispectral remote sensing images using relatively higher spatial resolution broadband thermal infrared image. The boundary-preserving information is extracted from high resolution thermal infrared image using co-occurrence image filter, and is combined with Laplacian of Gaussian based sharpened image to extract salient features for injection. In addition, visible image is transformed to IHS color space, and intensity component is enhanced using CLAHE and inverse transformation to generate enhanced visible image for fusion. The procedure developed is evaluated with Indian Nano Satellite (INS) broadband thermal infrared images available at a spatial resolution of 175 m with same day acquisition MODIS multispectral visible images available at a relatively coarser spatial resolution of 500 m. The nearest acquisition of Landsat-8 thermal infrared images with MODIS multispectral visible images is also used for infrared–visible multi-modal image fusion. The CLIM fused image confirms that distinct features such as dam, ship docking zones and refinery regions, are better demarked and semantically more meaningful in comparison with individual thermal infrared and multispectral visible image. The proposed CLIM approach is compared with, and found to perform better than state-of-the-art image fusion techniques, both visually and quantitatively.Item Advanced 3D Modeling for Augmented Reality Visualizations in Engineering Education: Issues, Challenges, and Future(IEEE, 2023) Rohil, Mukesh KumarAugmented Reality (AR), the superimposing of a virtual object on the view of the real environment, assists in visualizing engineering concepts in real-time. Computer graphics-based 3D modeling techniques evolved over the past five decades are being used in AR systems. Representations of 3D objects using polygons, surface patches, points (or voxels), and lines are insufficient to visualize the intricacies of objects having curved surfaces. To enable high-level control and accuracies pertaining to 3D representational details of 3D models, advanced 3D modeling approaches are needed. In this paper, we discuss (along with their suitability, examples, and advantages) several advanced 3D modeling techniques for AR visualizations. We emphasize that the use of AR visualizations for teaching and learning can help learners with a better understanding of concepts in engineering education. Additionally, we discuss some issues, challenges, and the future scope of use of AR in Engineering Education.Item VOEDHgesture: A Multi-Purpose Visual Odometry/ Simultaneous Localization and Mapping and Egocentric Dynamic Hand Gesture Data-Set for Virtual Object Manipulations in Wearable Mixed Reality(SciTech, 2024) Rohil, Mukesh KumarVisual Odometry/ Simultaneous Localization and Mapping (VO/ SLAM) and Egocentric hand gesture recognition are the two major technologies for wearable computing devices like AR (Augmented Reality)/ MR (Mixed Reality) glasses. However, the AR/MR community lacks a suitable dataset for developing both hand gesture recognition and RGB-D SLAM methods. In this work, we use a ZED mini Camera to develop challenging benchmarks for RGB-D VO/ SLAM tasks and dynamic hand gesture recognition. In our dataset VOEDHgesture, we collected 264 sequences using a ZED mini camera, along with precisely measured and time-synchronized ground truth camera positions, and manually annotated the bounding box values for the hand region of interest. The sequences comprise both RGB and depth images, captured at HD resolution (1920 × 1080) and recorded at a video frame rate of 30Hz. To resemble the Augmented Reality environment, the sequences are captured using a head-mounted ZED mini camera, with unrestrictedItem An elliptical sampling based fast and robust feature descriptor for image matching(Springer, 2024-01) Rohil, Mukesh KumarLocal features of an image provide a robust way of image matching if they are invariant to large variations in scale, viewpoint, illumination, rotation, and affine transformations. In this paper, we propose a novel feature descriptor based on circular and elliptical local sampling of image pixels to attain fast and robust results under varying imaging conditions. The proposed descriptor is tested on a standard benchmark dataset comprising of images with varying imaging conditions and compression quality. Results show that the proposed method generates sufficient or more number of stable and correct matches between an image pair (original image and distorted image) as compared to SIFT with a speedup of 1.6 on average basis. The paper also discusses the reason of choosing SIFT descriptor for comparison and its efficacy in different scenarios. The paper also reasons the robustness of hand crafted feature descriptors and why they hold an upper hand among many other deep learning methods.