Loading…

A GUI based application for Low Intensity Object Classification & Count using SVM Approach

There is a requirement of processing low intensity images from EO (Electro Optical), IR (Infra-Red) and ISAR (Inverse Synthetic Aperture Radar) sensors on airborne platforms to detect and classify targets(ships, vessels, other objects) against a library of images in real time with a degree of confid...

Full description

Saved in:
Bibliographic Details
Main Authors: Gupta, Vishal, Marriwala, Nikhil, Gupta, Monish
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 302
container_issue
container_start_page 299
container_title
container_volume
creator Gupta, Vishal
Marriwala, Nikhil
Gupta, Monish
description There is a requirement of processing low intensity images from EO (Electro Optical), IR (Infra-Red) and ISAR (Inverse Synthetic Aperture Radar) sensors on airborne platforms to detect and classify targets(ships, vessels, other objects) against a library of images in real time with a degree of confidence. Pre-processing of the test input images improves the accuracy of detection and classification. The proposed method verifies and validates the trained software against the pre-processed images. The objective of the proposed approach is to train the machine learning technique in order to estimate the efficiency and accuracy of the classified and detected output from the Deep leaning models. In our work, we have compared the results in terms of accuracy and time with the previous researchers work and we have summarized about our method gives better accuracy.
doi_str_mv 10.1109/ISPCC53510.2021.9609470
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9609470</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9609470</ieee_id><sourcerecordid>9609470</sourcerecordid><originalsourceid>FETCH-LOGICAL-i484-759646a6ab318b7fd615c00c6d822ebb49a55e53ee97f91d4892a6a6d3e757c83</originalsourceid><addsrcrecordid>eNo1kEtLw0AYRUdBsNb-AhfOyl3qvL55LEPQGohUaHXhpswkE50Sk5BJKf33Bqyry4XD4XIRuqdkSSkxj_nmLcuAw9QZYXRpJDFCkQt0Q6UEwQCEukQzJgVPtKRwjRYx7gkhnBGuAGboM8Wr9xw7G32Fbd83obRj6FpcdwMuuiPO29G3MYwnvHZ7X444a2yMof7nHnDWHdoRH2Jov_Dm4xWnfT90tvy-RVe1baJfnHOOts9P2-wlKdarPEuLJAgtEgVGCmmldZxqp-pqmlkSUspKM-adE8YCeODeG1UbWglt2ETLinsFqtR8ju7-tMF7v-uH8GOH0-78BP8FIu1SZA</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>A GUI based application for Low Intensity Object Classification &amp; Count using SVM Approach</title><source>IEEE Xplore All Conference Series</source><creator>Gupta, Vishal ; Marriwala, Nikhil ; Gupta, Monish</creator><creatorcontrib>Gupta, Vishal ; Marriwala, Nikhil ; Gupta, Monish</creatorcontrib><description>There is a requirement of processing low intensity images from EO (Electro Optical), IR (Infra-Red) and ISAR (Inverse Synthetic Aperture Radar) sensors on airborne platforms to detect and classify targets(ships, vessels, other objects) against a library of images in real time with a degree of confidence. Pre-processing of the test input images improves the accuracy of detection and classification. The proposed method verifies and validates the trained software against the pre-processed images. The objective of the proposed approach is to train the machine learning technique in order to estimate the efficiency and accuracy of the classified and detected output from the Deep leaning models. In our work, we have compared the results in terms of accuracy and time with the previous researchers work and we have summarized about our method gives better accuracy.</description><identifier>EISSN: 2643-8615</identifier><identifier>EISBN: 1665425547</identifier><identifier>EISBN: 9781665425544</identifier><identifier>DOI: 10.1109/ISPCC53510.2021.9609470</identifier><language>eng</language><publisher>IEEE</publisher><subject>CNN etc ; Computer vision ; Libraries ; Machine learning ; Object detection ; Optical imaging ; ships detections ; Signal processing ; Signal processing algorithms ; Support vector machines ; SVM</subject><ispartof>2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), 2021, p.299-302</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9609470$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9609470$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Gupta, Vishal</creatorcontrib><creatorcontrib>Marriwala, Nikhil</creatorcontrib><creatorcontrib>Gupta, Monish</creatorcontrib><title>A GUI based application for Low Intensity Object Classification &amp; Count using SVM Approach</title><title>2021 6th International Conference on Signal Processing, Computing and Control (ISPCC)</title><addtitle>ISPCC</addtitle><description>There is a requirement of processing low intensity images from EO (Electro Optical), IR (Infra-Red) and ISAR (Inverse Synthetic Aperture Radar) sensors on airborne platforms to detect and classify targets(ships, vessels, other objects) against a library of images in real time with a degree of confidence. Pre-processing of the test input images improves the accuracy of detection and classification. The proposed method verifies and validates the trained software against the pre-processed images. The objective of the proposed approach is to train the machine learning technique in order to estimate the efficiency and accuracy of the classified and detected output from the Deep leaning models. In our work, we have compared the results in terms of accuracy and time with the previous researchers work and we have summarized about our method gives better accuracy.</description><subject>CNN etc</subject><subject>Computer vision</subject><subject>Libraries</subject><subject>Machine learning</subject><subject>Object detection</subject><subject>Optical imaging</subject><subject>ships detections</subject><subject>Signal processing</subject><subject>Signal processing algorithms</subject><subject>Support vector machines</subject><subject>SVM</subject><issn>2643-8615</issn><isbn>1665425547</isbn><isbn>9781665425544</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kEtLw0AYRUdBsNb-AhfOyl3qvL55LEPQGohUaHXhpswkE50Sk5BJKf33Bqyry4XD4XIRuqdkSSkxj_nmLcuAw9QZYXRpJDFCkQt0Q6UEwQCEukQzJgVPtKRwjRYx7gkhnBGuAGboM8Wr9xw7G32Fbd83obRj6FpcdwMuuiPO29G3MYwnvHZ7X444a2yMof7nHnDWHdoRH2Jov_Dm4xWnfT90tvy-RVe1baJfnHOOts9P2-wlKdarPEuLJAgtEgVGCmmldZxqp-pqmlkSUspKM-adE8YCeODeG1UbWglt2ETLinsFqtR8ju7-tMF7v-uH8GOH0-78BP8FIu1SZA</recordid><startdate>20211007</startdate><enddate>20211007</enddate><creator>Gupta, Vishal</creator><creator>Marriwala, Nikhil</creator><creator>Gupta, Monish</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20211007</creationdate><title>A GUI based application for Low Intensity Object Classification &amp; Count using SVM Approach</title><author>Gupta, Vishal ; Marriwala, Nikhil ; Gupta, Monish</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i484-759646a6ab318b7fd615c00c6d822ebb49a55e53ee97f91d4892a6a6d3e757c83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2021</creationdate><topic>CNN etc</topic><topic>Computer vision</topic><topic>Libraries</topic><topic>Machine learning</topic><topic>Object detection</topic><topic>Optical imaging</topic><topic>ships detections</topic><topic>Signal processing</topic><topic>Signal processing algorithms</topic><topic>Support vector machines</topic><topic>SVM</topic><toplevel>online_resources</toplevel><creatorcontrib>Gupta, Vishal</creatorcontrib><creatorcontrib>Marriwala, Nikhil</creatorcontrib><creatorcontrib>Gupta, Monish</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gupta, Vishal</au><au>Marriwala, Nikhil</au><au>Gupta, Monish</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A GUI based application for Low Intensity Object Classification &amp; Count using SVM Approach</atitle><btitle>2021 6th International Conference on Signal Processing, Computing and Control (ISPCC)</btitle><stitle>ISPCC</stitle><date>2021-10-07</date><risdate>2021</risdate><spage>299</spage><epage>302</epage><pages>299-302</pages><eissn>2643-8615</eissn><eisbn>1665425547</eisbn><eisbn>9781665425544</eisbn><abstract>There is a requirement of processing low intensity images from EO (Electro Optical), IR (Infra-Red) and ISAR (Inverse Synthetic Aperture Radar) sensors on airborne platforms to detect and classify targets(ships, vessels, other objects) against a library of images in real time with a degree of confidence. Pre-processing of the test input images improves the accuracy of detection and classification. The proposed method verifies and validates the trained software against the pre-processed images. The objective of the proposed approach is to train the machine learning technique in order to estimate the efficiency and accuracy of the classified and detected output from the Deep leaning models. In our work, we have compared the results in terms of accuracy and time with the previous researchers work and we have summarized about our method gives better accuracy.</abstract><pub>IEEE</pub><doi>10.1109/ISPCC53510.2021.9609470</doi><tpages>4</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2643-8615
ispartof 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC), 2021, p.299-302
issn 2643-8615
language eng
recordid cdi_ieee_primary_9609470
source IEEE Xplore All Conference Series
subjects CNN etc
Computer vision
Libraries
Machine learning
Object detection
Optical imaging
ships detections
Signal processing
Signal processing algorithms
Support vector machines
SVM
title A GUI based application for Low Intensity Object Classification & Count using SVM Approach
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T18%3A14%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=A%20GUI%20based%20application%20for%20Low%20Intensity%20Object%20Classification%20&%20Count%20using%20SVM%20Approach&rft.btitle=2021%206th%20International%20Conference%20on%20Signal%20Processing,%20Computing%20and%20Control%20(ISPCC)&rft.au=Gupta,%20Vishal&rft.date=2021-10-07&rft.spage=299&rft.epage=302&rft.pages=299-302&rft.eissn=2643-8615&rft_id=info:doi/10.1109/ISPCC53510.2021.9609470&rft.eisbn=1665425547&rft.eisbn_list=9781665425544&rft_dat=%3Cieee_CHZPO%3E9609470%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i484-759646a6ab318b7fd615c00c6d822ebb49a55e53ee97f91d4892a6a6d3e757c83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9609470&rfr_iscdi=true