{"title":"工业质量检测中的多传感器摄像机","authors":"R. Massen","doi":"10.1109/AT.1995.535971","DOIUrl":null,"url":null,"abstract":"Industrial surface inspection can greatly be improved if not just greylevel intensity, but also colour and local height can be imaged at once. The multi-sensorial camera produces for every pixel not a scalar attribute, but a complete feature vector with many, preferably uncorrelated components. A typical example is the \"3D&Colour\" line scan camera which generates a feature vector (Intensity, Hue, Saturation, Z=height) for every pixel at resolutions of typically 2048 pixels along the line of scan and with scanning frequencies up to several kHz. The processing of such a vectorial image starts with a LUT-based, trainable pixel classifier who transforms the vectorial image into a stack of binary class label images. This significant data reduction results in only little information loss and leads to further processing based on well-established image region processing techniques.","PeriodicalId":268081,"journal":{"name":"AT'95: Advanced Technologies Intelligent Vision","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multisensorial cameras in industrial quality inspection\",\"authors\":\"R. Massen\",\"doi\":\"10.1109/AT.1995.535971\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Industrial surface inspection can greatly be improved if not just greylevel intensity, but also colour and local height can be imaged at once. The multi-sensorial camera produces for every pixel not a scalar attribute, but a complete feature vector with many, preferably uncorrelated components. A typical example is the \\\"3D&Colour\\\" line scan camera which generates a feature vector (Intensity, Hue, Saturation, Z=height) for every pixel at resolutions of typically 2048 pixels along the line of scan and with scanning frequencies up to several kHz. The processing of such a vectorial image starts with a LUT-based, trainable pixel classifier who transforms the vectorial image into a stack of binary class label images. This significant data reduction results in only little information loss and leads to further processing based on well-established image region processing techniques.\",\"PeriodicalId\":268081,\"journal\":{\"name\":\"AT'95: Advanced Technologies Intelligent Vision\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1995-10-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AT'95: Advanced Technologies Intelligent Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AT.1995.535971\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AT'95: Advanced Technologies Intelligent Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AT.1995.535971","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multisensorial cameras in industrial quality inspection
Industrial surface inspection can greatly be improved if not just greylevel intensity, but also colour and local height can be imaged at once. The multi-sensorial camera produces for every pixel not a scalar attribute, but a complete feature vector with many, preferably uncorrelated components. A typical example is the "3D&Colour" line scan camera which generates a feature vector (Intensity, Hue, Saturation, Z=height) for every pixel at resolutions of typically 2048 pixels along the line of scan and with scanning frequencies up to several kHz. The processing of such a vectorial image starts with a LUT-based, trainable pixel classifier who transforms the vectorial image into a stack of binary class label images. This significant data reduction results in only little information loss and leads to further processing based on well-established image region processing techniques.