S EBENEZER JULIET

@vit.ac.in

Associate Professor Senior School of Computer Science and Engineering
Vellore Institute of Technology Vellore

S EBENEZER JULIET


               Download Compact PDF  
https://researchid.co/juliet19594

RESEARCH INTERESTS

Digital Image Processing, Image Segmentation, Image Classification, and Image Compression
Video Processing-Object Detection and Tracking
Wireless Sensors Network- Scheduling and Optimal Route finding
Internet of Things-Security
25

Scopus Publications

114

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Performance analysis of deep convolutional neural network models for railway track damage detection
    Amritsai S, Ebenezer Juliet S, Dhanush Baalaji K, Joe Augustine Pavan, Sayuj Sunil, Tejus CS
    Engineering Research Express, 2026
    Ensuring the safety and security of the Railways - one of the world’s largest transportation networks is a critical priority. Deep learning technologies present a promising avenue for enhancing track maintenance by enabling automated damage detection. The proposed work focuses on the use of Convolutional Neural Networks to identify various types of railway track defects, including cracks, corrosion, and misalignments, which are common in the Indian rail network. The study involves a comparative analysis of several Convolutional Neural Network architectures, including ResNet50, VGG, DenseNet, MobileNet, AlexNet, ConvNet, and AdaptiveSSN, using a curated and pre-processed image dataset consisting of 12,000 images. The results highlight the strong performance of ResNet50 and DenseNet, achieving accuracy rates of 95.42% and 94.35%, respectively, followed by VGG with 92.60%. This work demonstrates the effectiveness of Convolutional Neural Networks based deep learning models in railway infrastructure monitoring and offers valuable guidance for researchers aiming to refine such models for enhanced damage detection. It also outlines future research directions to improve the automation and accuracy of railway safety systems.
  • EST–RBT–YOLOv7: a real-time object detection framework for intelligent transportation and engineering applications
    J. Thilagavathy, S Ebenezer Juliet, D. Kesavaraja
    Journal of the Chinese Institute of Engineers Transactions of the Chinese Institute of Engineers Series A, 2026
    Object detection (OD) is essential in computer vision that allows intelligent systems identify and localize objects accurately within digital images. It has become a basic element in applications such as autonomous driving, surveillance, medical diagnostics, robotics, and intelligent transportation systems. While existing OD models have shown promising results, balancing between detection accuracy and computational efficiency remains a major challenge. In this work, we present an enhanced OD model that integrates the Enhanced Swin Transformer (EST) with the YOLOv7 model. The EST provides a hierarchical vision transformer design capable of capturing long-range dependencies and contextual relations. Further, YOLOv7 is a high-speed detector known for its accuracy and low latency. Moreover, a ResNet Bottleneck Transformer (RBT) is embedded in the detection head, which combines the benefits of residual convolutional structures with transformer-based attention. This hybrid design allows for a deeper feature interaction and better region-level refinement, especially advantageous in cluttered traffic scenes. Experimental outcomes are performed on two benchmark datasets and proved that the suggested model outperformed existing methods by attaining mean average precision (mAP) 0.997 on KITTI and 0.985 RSUD20K. The outcomes proved the effectiveness of the approach in achieving high-performance OD suitable for practical deployment in resource-aware environments.
  • A Deep Neural Network Based Filtered BiFPN object Detector using Scale Integrated Channel Attention
    J.Thilagavathy, S.Ebenezer Juliet, D. Kesavaraja
    Proceedings of 2025 International Conference on Signal Processing Computation Electronics Power and Telecommunication Iconscept 2025, 2025
    Multi-scale object detection is one of the most algorithmically complex and computationally demanding problems in intelligent machine vision. Typical object detection techniques address memory optimization but compromise model accuracy and performance gain in terms of model complexity. Most of the existing methods provide complex architecture with a greater number of edges in convolutional neural network (CNN), which increases computation cost and fails to extract significant features effectively. To overcome this complexity, it has been proposed several key optimization techniques that improves the accuracy for the detector called as Scale Integrated Attention and Filtered BiFPN (SICA-FBiFPN) framework. First, it has been proved that improvement in accuracy has been done by replacing the existing Feature pyramid network (FPN) with a Filtered Bi-directional Feature Pyramid Network (FBiFPN) technique. FBiFPN effectively generates multi-scale feature with reduced number of edges in FPN. Secondly, the performance gain has been improved using proposed Scale Integrated Channel Attention (SICA) module. It also extracts significant features effectively and improves accuracy of detector of multiple spatial scales. It has been experimented on the MS COCO and PASCAL VOC that our proposed method outperforms the current state-of-the-art research. The performance metrics such as accuracy and parameters were evaluated. The experimental results on SICA-FBiFPN model achieves 76.7% mAP@0.5 Intersection over Union, 58.8 mAP on the MS COCO dataset. It achieves 85.5% mAP on Pascal VOC dataset.
  • Glaucoma Retinal Image Detection through the Segmentation of OD Using Modified Deep Learning Method
    J. Ruby Elizabeth, D. Kesavaraja, S. Ebenezer Juliet, S. Jagadeesh, S. Samsudeen Shaffi, R. Umanesan
    Eai Endorsed Transactions on Internet of Things, 2025
    Classifiers are the important processing module in any type of classification systems. This paper uses the proposed Modified LeNET (MLNET) classification architecture along with the standard LeNET to classify the retinal pictures into healthy cases and cases of glaucoma. This research work develops an automated computer aided system which has the following modules as preprocessing, Optic Disk (OD) segmentation, Feature computations and MLNET classification. The Glaucoma classification system has been functioned in two processing phases as training and testing. The training processing phase trains both healthy and Glaucoma retinal images from the known dataset using preprocessing, OD segmentation and feature computations from the segmented OD region. These features from the OD region have been further trained by the proposed MLNET classifier. The testing processing phase tests the unknown retinal image into either Glaucoma or healthy class through the sub processing modules of preprocessing, OD region segmentation and feature computations. The features from the OD region in the unknown test retinal image have been fed into the proposed MLNET classifier with respect to the previous training results.
  • A brief survey on particle swarm optimization in wireless sensor networks
    V. Ram Prabha, S. Ebenezer Juliet
    Aip Conference Proceedings, 2024
  • A deep learning model based glaucoma detection using retinal images
    J. Ruby Elizabeth, D. Kesavaraja, S. Ebenezer Juliet
    Journal of Intelligent and Fuzzy Systems, 2024
    The retinal illness that causes vision loss frequently on the globe is glaucoma. Hence, the earlier detection of Glaucoma is important. In this article, modified AlexNet deep leaning model is proposed to category the source retinal images into either healthy or Glaucoma through the detection and segmentations of optic disc (OD) and optic cup (OC) regions in retinal pictures. The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC regions are detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region are classified and trained by the suggested AlexNet deep leaning model. This model classifies the source retinal image into either healthy or Glaucoma. Finally, performance measures have been estimated in relation to ground truth pictures in regards to accuracy, specificity and sensitivity. These performance measures are contrasted with the other previous Glaucoma detection techniques on publicly accessible retinal image datasets HRF and RIGA. The suggested technique as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. AIM: Segmenting the OD and OC areas and classifying the source retinal picture as either healthy or glaucoma-affected. METHODS: The retinal images are preprocessed and OD region is detected and segmented using circulatory filter. Further, OC region is detected and segmented using K-means classification algorithm. Then, the segmented OD and OC region classified are and trained by the suggested AlexNet deep leaning model. RESULTS: The suggested method as described in this work achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset. CONCLUSION: This article proposes the modified AlexNet deep learning models for the detections of Glaucoma utilizing retinal images. The OD region is detected using circulatory filter and OC region is detected using k-means classification algorithm. The detected OD and OC regions are utilized to classify the retinal images into either healthy or Glaucoma using the suggested AlexNet model. The proposed method obtains 100% Sey, 93.7% Spy and 96.6% CA on HRF dataset retinal images. The proposed AlexNet method obtains 97.7% Sey, 98% Spy and 97.8% CA on RIGA dataset retinal images. The proposed method stated in this article achieves 91.6% GDR for mild case and also achieves 100% GDR for severe case on HRF dataset. The suggested method as described in this work achieves 97.7% GDR for mild case and also achieves 100% GDR for severe case on RIGA dataset.
  • Artificial Intelligence Techniques for River Water Quality Analysis Using IoT and Remote Sensing
    K. Manimala, E. Fantin Irudaya Raj, S. Ebenezer Juliet
    Artificial Intelligence Techniques for Sustainable Development, 2024
    The sediment concentration in any water resource affects the water’s transparency, quality, and turbidity. Satellite remote sensing is the faster way to identify the sediment concentration to determine water quality compared to the usual manual process of collecting water samples and analyzing them in the lab. Satellite remote sensing assessment is problematic in determining if the predicted outcomes align with the real conditions within the water body. The accuracy of remote sensing-based Water Quality (WQ) needs to be improved regarding both temporal and spatial resolution. IoT-based sensors and ground truth data can support remote sensing-based WQ analysis. Scientists have identified poor water quality by autonomously measuring the essential physical, biological, and chemical PCB parameters thanks to the advancement of water quality observing technology. Sensorized buoys, underwater robots, multisensory e-fish, electronic skin (e-Skin), and boats have been utilized for data warehousing and in-depth monitoring. There is a disconnection between the quality of data gathered by autonomous sensors and data analysis, creating a gap between the technology and the end user. The use of a network of sensors and Geographic Information Systems (GISs) could improve the autonomous WQ observing technology by providing real-time water-related information through appropriate analysis methods. Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely deployed to properly analyze the data, provide alert signals, and predict the future WQ in advance. In addition to sediment deposition analysis, it is important to identify the presence of harmful algal blooms to safeguard the water bodies for the survival of the human community. The WQ parameters like pH, Salinity, Turbidity, Temperature, chlorophyll, Suspended Matter, and Dissolved Oxygen (DO) are used to determine water quality. Data collected from satellite images like Sentinel-2(2A and 2B), Landsat-8 OLI-(L1 and L2), IRS, LISS (III, IV), RESOURSESAT 1, IKONOS, QUICKBIRD, SPOT, RISAT 1, CARTOSAT and MODIS with data bands like R, G, B, Near-Infrared, and Mid-Infrared are commonly used for estimating the quality of water. The proposed work focuses on the necessity of WQ analysis to provide a green environment, the AI methodologies suggested in the literature, the research gap, and the need to design a predictive model based on the data collected from remote sensing and IoT sensors to determine the future status of the river. Finally, a case study is made in the Thamirabharani River for observing the water quality analysis, in all seasons of the year in Tamil Nadu, India for the two southern districts Tirunelveli and Thoothukudi which play a major role in human survival and socio-economic development is presented.
  • Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network
    I. Vallirathi, S. Ebenezer Juliet
    Computer Systems Science and Engineering, 2023
    Wireless Sensor Network (WSNs) is an infrastructure-less wireless network deployed in an increasing number of wireless sensors in an ad-hoc manner. As the sensor nodes could be powered using batteries, the development of WSN energy constraints is considered to be a key issue. In wireless sensor networks (WSNs), wireless mobile chargers (MCs) conquer such issues mainly, energy shortages. The proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network (WRSN), which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the sensor. In this algorithm, each node gets sorted using the K-means technique, in which the data gets distributed into various clusters. The mobile charges execute a Short Hamiltonian cycle opposite direction to reach each cluster’s anchor point. The position of the anchor points is calculated based on the energy distribution using the base station. In this case, the network will act as a spare MC, so that one of the two MCs will run out of energy before reaching the BS. After the current tours of the two MCs terminate, regression analysis for energy prediction initiates, enabling the updating of anchor points in the upcoming round. Based on the findings of the regression-based energy prediction model, the recommended algorithm could effectively refill network energy.
  • Prediction of Cardio Vascular Disease from Retinal Fundus Images Using Machine Learning
    M. Sopana Devi, S. Ebenezer Juliet
    Lecture Notes in Networks and Systems, 2023
  • GAIN ENHANCEMENT AND COUPLING REDUCTION IN FOUR PORT E-SHAPED PATCH ANTENNA ARRAY WITH DGS FOR SYNTHETIC APERTURE RADAR AND MIMO APPLICATIONS
    Journal of Environmental Protection and Ecology, 2023
  • Building Footprint Semantic Segmentation using Bi-Channel Bi-Spatial (B2-CS) LinkNet
    C. Jenifer Grace Giftlin, S. Jenicka, S. Ebenezer Juliet
    Journal of the Indian Society of Remote Sensing, 2022
  • Pedestrian tracking in thermal videos using TFM (tri-feature matrix)
    D. Sasireka, S. Ebenezer Juliet
    Pattern Analysis and Applications, 2021
  • Improved compound image segmentation using automatic pixel block classification with SVM
    Ebenezer Juliet Selwyn, Selvi Shunmuga Velayutham, Jemi Florinabel Deva George
    Iet Image Processing, 2020
  • Performance Evaluation of Frequency Transform Based Block Classification of Compound Image Segmentation Techniques
    Ebenezer Juliet Selwyn, D. Jemi Florinabel
    Journal of the Institution of Engineers India Series B, 2018
  • Multi structure morphological inpainting for the recovery of damaged digitised photographs
    D. Jemi Florinabel, S. Ebenezer Juliet, V. Sadasivam
    International Journal of Signal and Imaging Systems Engineering, 2014
  • Effective layer-based segmentation of compound images using morphology
    S. Ebenezer Juliet, V. Sadasivam, D. Jemi Florinabel
    Journal of Real Time Image Processing, 2014
  • Fast block prediction-based coding of compound images by exploiting edge orientation
    S. Ebenezer Juliet, V. Sadasivam, D. Jemi Florinabel
    International Journal of Signal and Imaging Systems Engineering, 2013
  • Fast orientation-driven multi-structure morphological inpainting
    Jemi Florinabel Deva, Ebenezer Juliet Selwyn, Sadasivam Velayuthum
    International Journal of Computers and Applications, 2012
  • Efficient block prediction-based coding of computer screen images with precise block classification
    S. Ebenezer Juliet, D. Jemi Florinabel
    Iet Image Processing, 2011
  • MULTIORIENTATION-BASED MULTISTRUCTURE MORPHOLOGICAL INPAINTING
    D. JEMI FLORINABEL, S. EBENEZER JULIET, V. SADASIVAM
    International Journal of Image and Graphics, 2011
  • Fast orientation driven multi structure morphological inpainting
    D. Jemi Florinabel, S. Ebenezer Juliet, V. Sadasivam
    Proceedings of SPIE the International Society for Optical Engineering, 2011
  • Combined frequency and spatial domain-based patch propagation for image completion
    D. Jemi Florinabel, S. Ebenezer Juliet, V. Sadasivam
    Computers and Graphics Pergamon, 2011
  • Efficient coding of computer screen images with precise block classification using wavelet transform
    Journal of the Institution of Engineers India Part CP Computer Engineering Division, 2010
  • Simplified DCT based segmentation with efficient coding of computer screen images
    S. Ebenezer Juliet, D. Jemi Florinabel, V. Sadasivam
    1st International Conference on Internet Multimedia Computing and Service Icimcs 2009, 2009
  • Multi Echelon Gabor Orientation Driven Morphological Inpainting based Recovery of Digitized Paintings
    Journal of the Institution of Engineers India Part CP Computer Engineering Division, 2009

RECENT SCHOLAR PUBLICATIONS

  • A Deep Neural Network Based Filtered BiFPN object Detector using Scale Integrated Channel Attention
    J Thilagavathy, SE Juliet, D Kesavaraja
    2025 International Conference on Signal Processing, Computation, Electronics … , 2025
    2025
  • A Secure IOT‐Based Cluster Formation and Optimal Path Selection Using Fuzzy Rules and Metaheuristic Optimization
    DJJD Daniel, S Jagadeesh, LB Ebenezer, SE Juliet
    Security and Privacy 8 (6), e70115 , 2025
    2025
  • Deep Learning based Multi-face Recognition System for Automatic Attendance Registering in Classrooms
    AP Chawla, SE Juliet, A Suman, A Khan, G Manikandan
    6th International Conference on Deep Learning, Artificial Intelligence and … , 2025
    2025
    Citations: 2
  • A deep learning model based glaucoma detection using retinal images
    J Ruby Elizabeth, D Kesavaraja, SE Juliet
    Journal of Intelligent & Fuzzy Systems 48 (6), 823-834 , 2025
    2025
    Citations: 2
  • Glaucoma Retinal Image Detection through the Segmentation of OD Using Modified Deep Learning Method
    JR Elizabeth, D Kesavaraja, SE Juliet, S Jagadeesh, SS Shaffi, ...
    EAI Endorsed Transactions on Internet of Things 11 , 2024
    2024
  • A Novel Optic Disc and Optic Cup Segmentation in a Fundus Image using CNN based hybrid CAG-SEPPSM
    JR Elizabeth, D Kesavaraja, SE Juliet
    2023
  • Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network.
    I Vallirathi, SE Juliet
    Computer Systems Science & Engineering 44 (3) , 2023
    2023
    Citations: 3
  • Proceedings of International Conference on Recent Trends in Computing
    MSDSE Juliet
    Springer- Proceedings of International Conference on Recent Trends in … , 2023
    2023
  • Prediction of Cardio Vascular Disease from Retinal Fundus Images Using Machine Learning
    MSDSE Juliet
    Springer- Proceedings of International Conference on Recent Trends in … , 2023
    2023
    Citations: 2
  • Building Footprint Semantic Segmentation using Bi-Channel Bi-Spatial (B 2 -CS)
    C Jenifer Grace Giftlin, S Jenicka, S Ebenezer Juliet
    Journal of the Indian Society of Remote Sensing 50 (10), 1841-1854 , 2022
    2022
    Citations: 2
  • Building Footprint Semantic Segmentation using Bi-Channel Bi-Spatial (B2-CS)[Formula: see text]
    CJG Giftlin, S Jenicka, SE Juliet
    2022
  • Novel TMV Based Moving Object Detection and Tracking in Thermal Videos.
    DDK S.Ebenezer Juliet, Dr. D. Jeyabharathi
    .D.Sasireka,Dr. 2 nd International Conference on Sustainable Materials and … , 2022
    2022
  • Analysis of Rectangular Microstrip Patch Antenna with Improved Bandwidth and Reduced Return Loss Using Slot and Filters,
    SEJ J.Vanitha
    International Conference on Emerging Trends in Engineering, Management and … , 2022
    2022
  • A Methodology for Building Segmentation from Remotely Sensed Images using FCM_ML
    SE C J G Giftlin., S Jenicka .
    Journal of physics:Conference series –IO , 2022
    2022
  • A secure data storage architecture for internet of medical things (iomt) using an adaptive Gaussian mutation based sine cosine optimization algorithm and fuzzy-based secure …
    DJ Joel Devadass Daniel, S Ebenezer Juliet
    Journal of Medical Imaging and Health Informatics 11 (12), 2883-2890 , 2021
    2021
    Citations: 4
  • Pedestrian tracking in thermal videos using TFM (tri-feature matrix)
    D Sasireka, SE Juliet
    Pattern Analysis and Applications 24 (2), 831-842 , 2021
    2021
  • Covid-19 Risk Prediction Using Machine Learning Techniques
    SEJ M. Angel
    International Conference on Advances in Materials,  Computing and … , 2021
    2021
  • Lightweight Solutions for Securing IoT Based Healthcare System
    SEJ P. Jeyadurga
    International Conference on Advances in Materials, Computing and … , 2021
    2021
  • Dual Band Microstrip Patch Antenna for Wireless Applications
    SEJ J.Vanitha
    International Journal of Scientific Research in Science, Engineering and … , 2021
    2021
  • Covid-19 Risk Prediction Using Machine Learning Techniques
    MASE Juliet
    International Journal of Scientific Research in Science and Technology 9 (1 … , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Combined frequency and spatial domain-based patch propagation for image completion
    DJ Florinabel, SE Juliet, V Sadasivam
    Computers & Graphics 35 (6), 1051-1062 , 2011
    2011
    Citations: 28
  • Efficient block prediction-based coding of computer screen images with precise block classification
    S Ebenezer Juliet, D Jemi Florinabel
    IET Image Processing 5 (4), 306-314 , 2011
    2011
    Citations: 21
  • Effective layer-based segmentation of compound images using morphology
    SE Juliet, V Sadasivam, DJ Florinabel
    Journal of real-time image processing 9 (2), 299-314 , 2014
    2014
    Citations: 14
  • Efficient Coding of Computer Screen Images with Precise Block Classification using Wavelet Transform
    DJ Florinabel, SE Juliet, V Dr Sadasivam
    Volume 91, May 2010 , 2010
    2010
    Citations: 6
  • Non-iterative morphological erosion of missing image information using dynamic structuring element
    DJ Florinabel, SE Juliet, V Sadasivam
    International Conference on Sensor, Security, Software and Intelligent … , 2009
    2009
    Citations: 5
  • A secure data storage architecture for internet of medical things (iomt) using an adaptive Gaussian mutation based sine cosine optimization algorithm and fuzzy-based secure …
    DJ Joel Devadass Daniel, S Ebenezer Juliet
    Journal of Medical Imaging and Health Informatics 11 (12), 2883-2890 , 2021
    2021
    Citations: 4
  • Improved compound image segmentation using automatic pixel block classification with SVM
    E Juliet Selwyn, SS Velayutham, JFD George
    IET Image Processing 14 (8), 1605-1613 , 2020
    2020
    Citations: 4
  • A survey on various segmentation methods in medical imaging
    JR Elizabeth, SE Juliet
    International Journal of Emerging Trends in Engineering Research 7 (11), 1-5 , 2019
    2019
    Citations: 4
  • Joint Energy Predication and Gathering Data in Wireless Rechargeable Sensor Network.
    I Vallirathi, SE Juliet
    Computer Systems Science & Engineering 44 (3) , 2023
    2023
    Citations: 3
  • Performance evaluation of frequency transform based block classification of compound image segmentation techniques
    EJ Selwyn, DJ Florinabel
    Journal of The Institution of Engineers (India): Series B 99 (2), 157-165 , 2018
    2018
    Citations: 3
  • Security in Smart Healthcare System: A Comprehensive Survey
    PS P. Jeyadurga, S EbenezerJuliet, Joshua Selwyn
    International Journals of Advanced Research in Computer Science and … , 2017
    2017
    Citations: 3
  • Multiechelon gabor orientation driven morphological inpainting based recovery of digitized paintings
    DJ Florinabel, SE Juliet, V Sadasivam
    The Institutions of Engineers (India), Journal-CP 90, 18-25 , 2009
    2009
    Citations: 3
  • Deep Learning based Multi-face Recognition System for Automatic Attendance Registering in Classrooms
    AP Chawla, SE Juliet, A Suman, A Khan, G Manikandan
    6th International Conference on Deep Learning, Artificial Intelligence and … , 2025
    2025
    Citations: 2
  • A deep learning model based glaucoma detection using retinal images
    J Ruby Elizabeth, D Kesavaraja, SE Juliet
    Journal of Intelligent & Fuzzy Systems 48 (6), 823-834 , 2025
    2025
    Citations: 2
  • Prediction of Cardio Vascular Disease from Retinal Fundus Images Using Machine Learning
    MSDSE Juliet
    Springer- Proceedings of International Conference on Recent Trends in … , 2023
    2023
    Citations: 2
  • Building Footprint Semantic Segmentation using Bi-Channel Bi-Spatial (B 2 -CS)
    C Jenifer Grace Giftlin, S Jenicka, S Ebenezer Juliet
    Journal of the Indian Society of Remote Sensing 50 (10), 1841-1854 , 2022
    2022
    Citations: 2
  • Survey on Recharging Methods of Sensor Nodes in Wireless Rechargeable Sensor Networks
    IVDSE Juliet
    International Journal of Science and Research (IJSR) 8 (12), 1636-1639 , 2019
    2019
    Citations: 2
  • Multiorientation-based multistructure morphological inpainting
    DJ Florinabel, SE Juliet, V Sadasivam
    International Journal of Image and Graphics 11 (02), 177-193 , 2011
    2011
    Citations: 2
  • Simplified DCT based segmentation with efficient coding of computer screen images
    SE Juliet, DJ Florinabel, V Sadasivam
    Proceedings of the First International Conference on Internet Multimedia … , 2009
    2009
    Citations: 2
  • Wound Image Analysis System for Patients with Diabetes using two Level Segmentation
    PA Mustoora, AR Fathima, SU Easwari, SE Juliet
    International Journal of Emerging Technologies in Engineering Research … , 2016
    2016
    Citations: 1