Affine Normalized Invariant functionals using Independent Component Analysis
The paper presents a hybrid technique for affine invariant feature extraction with the view of object recognition based on parameterized contour. The presented technique first normalizes an input image by removing affine distortions using independent component analysis which also reduces the effect...
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| creator | Asad Ali Gilani, A.M. Memon, N.A. |
| description | The paper presents a hybrid technique for affine invariant feature extraction with the view of object recognition based on parameterized contour. The presented technique first normalizes an input image by removing affine distortions using independent component analysis which also reduces the effect of noise introduced during contour parameterization. Then two invariant functionals are constructed, one using the normalized object contour and the other using the dyadic wavelet transform. Experimental results conducted using three different standard datasets confirm the validity of the proposed approach. Beside this the error rates obtained in terms of invariant stability are significantly lower when compared to other wavelet based techniques and the proposed invariants exhibit higher feature disparity than the method of Fourier descriptors. |
| doi_str_mv | 10.1109/INMIC.2006.358143 |
| format | Conference Proceeding |
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The presented technique first normalizes an input image by removing affine distortions using independent component analysis which also reduces the effect of noise introduced during contour parameterization. Then two invariant functionals are constructed, one using the normalized object contour and the other using the dyadic wavelet transform. Experimental results conducted using three different standard datasets confirm the validity of the proposed approach. Beside this the error rates obtained in terms of invariant stability are significantly lower when compared to other wavelet based techniques and the proposed invariants exhibit higher feature disparity than the method of Fourier descriptors.</description><identifier>ISBN: 142440794X</identifier><identifier>ISBN: 9781424407941</identifier><identifier>EISBN: 1424407958</identifier><identifier>EISBN: 9781424407958</identifier><identifier>DOI: 10.1109/INMIC.2006.358143</identifier><language>eng</language><publisher>IEEE</publisher><subject>Affine invariants ; Character recognition ; Computer science ; Dyadic Wavelet Transform ; Error analysis ; Feature extraction ; Geometric Transformations ; Independent component analysis ; Noise reduction ; Object recognition ; Paper technology ; Pattern recognition ; Shearing ; Wavelet transforms</subject><ispartof>2006 IEEE International Multitopic Conference, 2006, p.94-99</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/4196386$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27924,54919</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4196386$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Asad Ali</creatorcontrib><creatorcontrib>Gilani, A.M.</creatorcontrib><creatorcontrib>Memon, N.A.</creatorcontrib><title>Affine Normalized Invariant functionals using Independent Component Analysis</title><title>2006 IEEE International Multitopic Conference</title><addtitle>INMIC</addtitle><description>The paper presents a hybrid technique for affine invariant feature extraction with the view of object recognition based on parameterized contour. The presented technique first normalizes an input image by removing affine distortions using independent component analysis which also reduces the effect of noise introduced during contour parameterization. Then two invariant functionals are constructed, one using the normalized object contour and the other using the dyadic wavelet transform. Experimental results conducted using three different standard datasets confirm the validity of the proposed approach. Beside this the error rates obtained in terms of invariant stability are significantly lower when compared to other wavelet based techniques and the proposed invariants exhibit higher feature disparity than the method of Fourier descriptors.</description><subject>Affine invariants</subject><subject>Character recognition</subject><subject>Computer science</subject><subject>Dyadic Wavelet Transform</subject><subject>Error analysis</subject><subject>Feature extraction</subject><subject>Geometric Transformations</subject><subject>Independent component analysis</subject><subject>Noise reduction</subject><subject>Object recognition</subject><subject>Paper technology</subject><subject>Pattern recognition</subject><subject>Shearing</subject><subject>Wavelet transforms</subject><isbn>142440794X</isbn><isbn>9781424407941</isbn><isbn>1424407958</isbn><isbn>9781424407958</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFjMtKxDAYhSMiqOM8gLjpC7Tm_ifLUrwU6riZAXdD0qYSmaal6Qjj0xtRcHMunI-D0C3BBSFY39ebl7oqKMayYEIRzs7QNeGUcwxaqPP_wt8u0TrGD4wxAcm05leoKfveB5dtxnkwB__luqwOn2b2JixZfwzt4sdgDjE7Rh_e09a5ySVJazUO0xh-UpmIU_TxBl30iXXrP1-h3ePDtnrOm9enuiqb3BMQS25w23EqgChh21Y6C0JpEAKMo7JNiCXWYODWUoDeMqaUVkCV4oxaIyRbobvfX--c20-zH8x82nOiJVOSfQPIHU5f</recordid><startdate>200612</startdate><enddate>200612</enddate><creator>Asad Ali</creator><creator>Gilani, A.M.</creator><creator>Memon, N.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200612</creationdate><title>Affine Normalized Invariant functionals using Independent Component Analysis</title><author>Asad Ali ; Gilani, A.M. ; Memon, N.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-a0cd4257185bcc6eb75897557ae26c175b1ba074bb277fb3388987288432ba563</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Affine invariants</topic><topic>Character recognition</topic><topic>Computer science</topic><topic>Dyadic Wavelet Transform</topic><topic>Error analysis</topic><topic>Feature extraction</topic><topic>Geometric Transformations</topic><topic>Independent component analysis</topic><topic>Noise reduction</topic><topic>Object recognition</topic><topic>Paper technology</topic><topic>Pattern recognition</topic><topic>Shearing</topic><topic>Wavelet transforms</topic><toplevel>online_resources</toplevel><creatorcontrib>Asad Ali</creatorcontrib><creatorcontrib>Gilani, A.M.</creatorcontrib><creatorcontrib>Memon, N.A.</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>Asad Ali</au><au>Gilani, A.M.</au><au>Memon, N.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Affine Normalized Invariant functionals using Independent Component Analysis</atitle><btitle>2006 IEEE International Multitopic Conference</btitle><stitle>INMIC</stitle><date>2006-12</date><risdate>2006</risdate><spage>94</spage><epage>99</epage><pages>94-99</pages><isbn>142440794X</isbn><isbn>9781424407941</isbn><eisbn>1424407958</eisbn><eisbn>9781424407958</eisbn><abstract>The paper presents a hybrid technique for affine invariant feature extraction with the view of object recognition based on parameterized contour. The presented technique first normalizes an input image by removing affine distortions using independent component analysis which also reduces the effect of noise introduced during contour parameterization. Then two invariant functionals are constructed, one using the normalized object contour and the other using the dyadic wavelet transform. Experimental results conducted using three different standard datasets confirm the validity of the proposed approach. Beside this the error rates obtained in terms of invariant stability are significantly lower when compared to other wavelet based techniques and the proposed invariants exhibit higher feature disparity than the method of Fourier descriptors.</abstract><pub>IEEE</pub><doi>10.1109/INMIC.2006.358143</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2006 IEEE International Multitopic Conference, 2006, p.94-99 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Affine invariants Character recognition Computer science Dyadic Wavelet Transform Error analysis Feature extraction Geometric Transformations Independent component analysis Noise reduction Object recognition Paper technology Pattern recognition Shearing Wavelet transforms |
| title | Affine Normalized Invariant functionals using Independent Component Analysis |
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