Pixel-based data fusion for a better object detection in automotive applications
The proposed technique addresses a fusion method of two imaging sensors on pixel-level. The fused image will provide a scene representation which is robust against illumination changes and different weather conditions. Thus, the combination of the advantages of each camera will extend the capabiliti...
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| creator | Thomanek, J Lietz, H Wanielik, G |
| description | The proposed technique addresses a fusion method of two imaging sensors on pixel-level. The fused image will provide a scene representation which is robust against illumination changes and different weather conditions. Thus, the combination of the advantages of each camera will extend the capabilities for many computer vision applications, such as video surveillance and automatic object recognition. The presented pixel-based fusion technique is examined on the images of two sensors, a far-infrared (FIR) light camera and a visible light camera which are built-in a vehicle. The sensor images are first decomposed using the Dyadic Wavelet Transform. The transformed data are combined in the wavelet domain controlled by a "goal-oriented" fusion rule. Finally, the fused wavelet representation image will be processed by a pedestrian detection system. |
| doi_str_mv | 10.1109/ICICISYS.2010.5658327 |
| format | Conference Proceeding |
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Finally, the fused wavelet representation image will be processed by a pedestrian detection system.</description><subject>Bioinformatics</subject><subject>Classification algorithms</subject><subject>Feature extraction</subject><subject>image registration</subject><subject>Image resolution</subject><subject>pedestrian recognition</subject><subject>Pixel</subject><subject>pixel-based data fusion</subject><subject>Support vector machine classification</subject><subject>Visualization</subject><subject>wavelet transform</subject><isbn>9781424465828</isbn><isbn>1424465826</isbn><isbn>1424465842</isbn><isbn>1424465850</isbn><isbn>9781424465842</isbn><isbn>9781424465859</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1kN1KAzEQhSMiqHWfQIS8wNb8bpJLWdQWChbaG6_K7GYCKdvuspuKvr0p1pmLj3POMBeHkCfO5pwz97ys824-N3PBsqUrbaUwV-SeK6FUVkpck8IZ-6-FvSXFNO1Znnytrbkj63X8xq5sYEJPPSSg4TTF_khDP1KgDaaEI-2bPbaJekwZ5zQeKZxSf-hT_EIKw9DFFs7J9EBuAnQTFhfOyPbtdVsvytXH-7J-WZXRsVRi1TrbIEqvpcOKV0aCRa2M4FYpyY0VDBtvQAvuZRDKKCZCJRxI40AFOSOPf28jIu6GMR5g_NldOpC_blZQvw</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Thomanek, J</creator><creator>Lietz, H</creator><creator>Wanielik, G</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Pixel-based data fusion for a better object detection in automotive applications</title><author>Thomanek, J ; Lietz, H ; Wanielik, G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-e6c98bee3d539e61673a8e54721844317820ebd7a521d3f247402f629a379a4f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Bioinformatics</topic><topic>Classification algorithms</topic><topic>Feature extraction</topic><topic>image registration</topic><topic>Image resolution</topic><topic>pedestrian recognition</topic><topic>Pixel</topic><topic>pixel-based data fusion</topic><topic>Support vector machine classification</topic><topic>Visualization</topic><topic>wavelet transform</topic><toplevel>online_resources</toplevel><creatorcontrib>Thomanek, J</creatorcontrib><creatorcontrib>Lietz, H</creatorcontrib><creatorcontrib>Wanielik, G</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>Thomanek, J</au><au>Lietz, H</au><au>Wanielik, G</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Pixel-based data fusion for a better object detection in automotive applications</atitle><btitle>2010 IEEE International Conference on Intelligent Computing and Intelligent Systems</btitle><stitle>ICICISYS</stitle><date>2010-10</date><risdate>2010</risdate><volume>2</volume><spage>385</spage><epage>390</epage><pages>385-390</pages><isbn>9781424465828</isbn><isbn>1424465826</isbn><eisbn>1424465842</eisbn><eisbn>1424465850</eisbn><eisbn>9781424465842</eisbn><eisbn>9781424465859</eisbn><abstract>The proposed technique addresses a fusion method of two imaging sensors on pixel-level. 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| ispartof | 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems, 2010, Vol.2, p.385-390 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Bioinformatics Classification algorithms Feature extraction image registration Image resolution pedestrian recognition Pixel pixel-based data fusion Support vector machine classification Visualization wavelet transform |
| title | Pixel-based data fusion for a better object detection in automotive applications |
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