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    Computer Vision: Dr. Sanjay Jain Associate Professor, CSA

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    sanjayjainitm
    This document discusses computer vision research conducted by Dr. Sanjay Jain and his group. It provides an overview of their work in developing approaches to efficiently analyze heterogeneous data like text, images and video. It summarizes completed research projects involving tampering detection in video and fog detection/removal from images. It also outlines current work on heart disease prediction and face recognition for attendance monitoring. Finally, it proposes future work on developing an intelligent system for fog detection and removal from vehicle camera images at night to help prevent accidents, as well as an IoT-based smart agriculture system using machine learning to optimize crop watering.

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    Computer Vision: Dr. Sanjay Jain Associate Professor, CSA

    Uploaded by

    sanjayjainitm
    73 views8 pages
    This document discusses computer vision research conducted by Dr. Sanjay Jain and his group. It provides an overview of their work in developing approaches to efficiently analyze heterogeneous data like text, images and video. It summarizes completed research projects involving tampering detection in video and fog detection/removal from images. It also outlines current work on heart disease prediction and face recognition for attendance monitoring. Finally, it proposes future work on developing an intelligent system for fog detection and removal from vehicle camera images at night to help prevent accidents, as well as an IoT-based smart agriculture system using machine learning to optimize crop watering.

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    Computer Vision

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    This document discusses computer vision research conducted by Dr. Sanjay Jain and his group. It provides an overview of their work in developing approaches to efficiently analyze heterogeneous data like text, images and video. It summarizes completed research projects involving tampering detection in video and fog detection/removal from images. It also outlines current work on heart disease prediction and face recognition for attendance monitoring. Finally, it proposes future work on developing an intelligent system for fog detection and removal from vehicle camera images at night to help prevent accidents, as well as an IoT-based smart agriculture system using machine learning to optimize crop watering.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    73 views8 pages

    Computer Vision: Dr. Sanjay Jain Associate Professor, CSA

    Uploaded by

    sanjayjainitm
    This document discusses computer vision research conducted by Dr. Sanjay Jain and his group. It provides an overview of their work in developing approaches to efficiently analyze heterogeneous data like text, images and video. It summarizes completed research projects involving tampering detection in video and fog detection/removal from images. It also outlines current work on heart disease prediction and face recognition for attendance monitoring. Finally, it proposes future work on developing an intelligent system for fog detection and removal from vehicle camera images at night to help prevent accidents, as well as an IoT-based smart agriculture system using machine learning to optimize crop watering.

    Copyright:

    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 8

    Computer Vision

    Dr. Sanjay Jain


    Associate Professor, CSA
    Computer Vision
    • With the advancement of technology, user’s world-
    wide generate and collect vast amounts of
    heterogeneous data(Text, Images, Video etc).
    • Research target of the computer vision group is to
    develop novel approaches for exploiting
    heterogeneous contents efficiently. This includes
    intelligent search and browsing functionality, as well
    as techniques for extracting higher-level semantic
    information directly from the visual content.
    Computer Vision-Work Done
    1. Tampering Detection in Digital Video using Temporal
    Fingerprints in Variable Size GOP’s.
    (Phd. Research work done by Ms. Vaishali Joshi)
    Publications: 04 Papers
    2. Synchronous Fog detection and removal from
    images.
    (Phd. Research work done by Mr. Ashok Shrivastava)
    Publications: 04 Papers
    Computer Vision-Work Done
    1. Enhanced Hybrid Median Filtration Technique For
    Medical Images
    (M.Tech Research work done by Ms. Jyotsna Singh)
    Publications: 02 Papers
    2. A Robust Method To Recognize Palm Vein Using Sift
    And SVM Classifier
    (M.Tech Research work done by Ms. Richa Singh)
    Publications: 02 Papers
    Computer Vision- Present ongoing Work
    1. Heart Disease Prediction using Hybrid Machine
    Learning Techniques
    (Research Scholar-Mr. Amir Khan(M.Tech-CSE)
    2. Face Recognition Based Video Analysis for
    attendance monitoring
    (Research Scholar-Mr. Namarshi Palit(B.Tech-CSE)
    Computer Vision- Proposed Work
    Intelligent scheme for fog detection and removal from images captured by
    the onboard camera in vehicle during low vision
    During day light we can easily recognize the presence of fog with our base
    eyes, but at night it becomes highly difficult to identify the fog, which may
    lead to accidents. Therefore, it is necessary to find out the presence of fog
    in the night time and also indicate the range of fog, i.e., the type of the fog
    you are facing. Based on this indication the drives could adjust his speed of
    the vehicle.
    But identifying the fog, and indicating the type of fog is not enough to reduce
    the problem caused by fog. Defogging of images captured by the onboard
    camera could help to reduce the risk factor in true manner, so that the
    driver can view the road more clearly and this could actually help to
    prevent the accident and reduce the death rate.
    Requirements: CNN expert, Digital camera to capture the real video data
    Note: Project Submitted for CORE Research Grant of SERB.
    Computer Vision- Proposed Research Work
    IoT based Smart Agriculture using Machine Learning
    Smart irrigation system will predicts the water requirement for a crop, using
    IOT and machine learning algorithms. Moisture, temperature and humidity
    are the three most essential parameters to determine the quantity of water
    required in any agriculture field.
    This system will comprises of temperature, humidity and moisture IOT
    sensors, which will deploy in an agricultural field, The data will send
    through a microprocessor, IoT device using cloud platform. An efficient
    machine learning algorithm will be applied on the data sensed from the
    field to predict the water requirement of the crop.
    Requirements: Expert of Agriculture, IOT and computer science, IOT sensors
    Thank You

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