Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Face recognition from an arbitrary image has been a standout amongst the most considered topic in image processing and computer vision. The human face is a convoluted multidimensional visual model and henceforth it is exceptionally hard to build up a computational model to recognize the face. This paper presents an approach depending on the attributes extracted from the image to identify the human face. The proposed approach combines both morphological image processing techniques and cascade object detector capabilities. This method is effective in face detection for arbitrary images. © 2021, Springer Nature Singapore Pte Ltd.

School of Computer Science and Engineering

Chennai Campus

World University root node

Advances in Intelligent Systems and Computing

An Improved Approach for Face Detection

Due to the recent advances in intelligent transportation systems (ITS), Automatic Train System (ATS) gained significant attention among the research community. An effective ATS offers the whole railway network to operate in a safe, cost-effective and proficient manner against sudden disturbances like temporary platform blockages. Numerous Train Timetable Rescheduling (TTR) models have been presented for managing unforeseen events which might disturb the timetable. The main aim of an effective TTR model is to reduce power utilization by consuming the entire benefits of reproductive braking energy under a random situation. In this view, this paper presents a new TTR model to optimize the energy of metro systems by the incorporation of improved genetic algorithm (IGA) and long short term memory (LSTM) based recurrent neural network (RNN). The proposed method incorporates three different models, namely controller, timetable, and energy models. The proposed method requires minimum time to recompute a new schedule and offers effective solutions instantly after a random disturbance happens. The performance validation of the proposed IGSA-LSTM model is simulated using Chennai Metro Train Station. The proposed method significantly reduces the energy consumption of metro train and reaches to a minimum average energy utilization of 696 kWh. © 2020 Elsevier B.V.

Physical Communication

Deep learning based energy efficient optimal timetable rescheduling model for intelligent metro transportation systems

Pan sharpening technique is a widely used image processing technique which combines the data available from various sensors and exploits its varied capabilities. In this study, the efficiency of four diverse pan sharpening methods namely High Pass filter, Modified Intensity Hue Saturation, Ehlers fusion and Hyperspectral Colour Sharpening was evaluated. The pan sharpening approaches are applied to Landsat 8 imagery of an urban area. The spatial and spectral quality of the fused images is assessed using different indices like Bias, RMSE, Correlation Coefficient and ERGAS. The fused images obtained have improved spatial resolution and visual appearance compared to the original MS image. The fused images have a spatial resolution comparable to that of the PAN image. According to visual analysis, Modified IHS method yielded a fused image with better visual interpretability. The statistical analysis shows that the high pass filter is the most suitable pan sharpening method for this dataset. On testing for Bias, RMSE, ERGAS and CC, the high pass filter method performed best followed by Modified Intensity Hue saturation, Ehlers fusion and Hyperspectral Colour Sharpening while Ehlers fusion showed a higher correlation, compared to Modified IHS. © 2019 IEEE.

2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019

Comparison of Different Pan Sharpening Techniques using Landsat 8 Imagery

With advances in remote sensing (RS) technology and platforms, more and more high-quality and fine spatial resolution satellite images are available. Manual method of feature extraction from remote sensing imagery is a tedious and time-consuming process. Thus automated and replicable technique plays vital role in updating lake database to evaluate the spatial and temporal evolution of lakes and ponds especially for vastly growing urban areas. This research work presents an artificial neural network (ANN) computed threshold value-based mathematical morphology (MM)-driven approach for extraction of lakes from satellite imageries with better accuracy. Accuracy of developed methodology has been assessed with the ground truths of the study area revealing better performance with different data-sets compared to existing methods. On an average scale for all data-sets used, the proposed algorithm is able to extract lakes with 99.47% accuracy and 0.9397 correlation coefficient (MCC). The existing classification method exhibited an accuracy of 98.75% and correlation coefficient of 0.89049. Similarly, the existing threshold-driven method has 99.31% accuracy and 0.90374 correlation coefficient. Maintenance of actual size and shape of the lakes, run-time control over structuring elements, semi-automation, faster processing, and single band adaptability are features of this work. © 2017 Indian Society for Hydraulics.

ISH Journal of Hydraulic Engineering

An ANN supported mathematical morphology based algorithm for lakes extraction from satellite images

The detection and extraction of water bodies from satellite imagery is very important and useful for several planning and developmental activities such as shoreline identification, mapping riverbank erosion, watershed extraction and water resource management. Popular techniques for water body extraction like those based on the normalized difference water index (NDWI) require reflectance information in the green and near-infrared (NIR) bands of the light spectrum. Moreover, some commonly used approaches may perform differently according to the spatial resolution of the images. In this regard, mathematical morphological (MM) techniques for image processing have been employed for spatial feature extraction as they preserve edges and shapes. This study proposes a flexible MM driven approach which is very effective for the extraction of water bodies from several satellite images with different spatial resolution. MM provides effective tools for processing image objects based on size and shape and is particularly adapted for water bodies that have typically specific spatial characteristics. In greater details, the proposed extraction algorithm preserves the actual size and shape of the water bodies since it is based on morphological operators based on geodesic reconstruction. Moreover, the choice of the filter size (called structural element (SE) in MM) in the proposed algorithm is done dynamically allowing one to retain the most precise results from different set of inputs images of different spatial resolution and swath. The availability of more than one spectral band of satellite imagery is not necessary for the proposed algorithm as it utilizes only a single band for its computation. This makes it convenient to apply in single band imageries obtained from satellites such as Cartosat thereby making the proposed approach effective over commonly used methods. The accuracy assessment was carried out and compared with the maximum likelihood (ML) classifier and methods based on spectral indices. In all the five test datasets, extraction accuracy of the proposed MM approach was significantly higher than that of spectral indices and ML methods. The results drawn from visual and qualitative assessments indicated its capability and efficiency in water body extraction from different satellite images. © 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)

ISPRS Journal of Photogrammetry and Remote Sensing

An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images

Shoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to accomplish the complex task of automatic extraction of features, such as shoreline, has considerably increased. Automated feature extraction can drastically minimize the time and cost of data acquisition and database updating. Effective and fast approaches are essential to monitor coastline retreat and update shoreline maps. Here, we present a flexible mathematical morphology-driven approach for shoreline extraction algorithm from satellite imageries. The salient features of this work are the preservation of actual size and shape of the shorelines, run-time structuring element definition, semi-automation, faster processing, and single band adaptability. The proposed approach is tested with various sensor-driven images with low to high resolutions. Accuracy of the developed methodology has been assessed with manually prepared ground truths of the study area and compared with an existing shoreline classification approach. The proposed approach is found successful in shoreline extraction from the wide variety of satellite images based on the results drawn from visual and quantitative assessments. © 2017 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.

Fulltext

Geo-Spatial Information Science

A novel mathematical morphology based algorithm for shoreline extraction from satellite images

Digital Elevation Model (DEM) is a numerical representation of topography and is made up of equal-sized grid cells, each with a value of elevation the DEMs that were generated from the spot heights by general interpolation techniques namely Inverse Distance Weighted (IDW), Kriging, Topo to Raster, Natural Neighbor (NN) and Spline approaches have been compared the relative performance of each method depends on various ground parameters and spatial distribution of sampling points. In this research investigation, performance of the above mentioned five interpolation methods have been evaluated by generating and validating the DEM from Differential Global Positioning System (DGPS) data in the ArcGIS software. With respect to our sample observations' spatial distribution and densities, the investigation results have shown that IDW method is giving better performance in plane and mild slope area, Natural Neighbor provides better performance in steep slope and whole area as compared to other methods. © 2014 IEEE.