Titlebook: Recognizing Planar Objects Using Invariant Image Features; Thomas H. Reiss Book 1993 Springer-Verlag Berlin Heidelberg 1993 3D.Affine Inva

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subjects were divided into 3 groups, giving 10 subjects per perspective. The analysis results show that (1) there is no significant difference among the three perspectives in terms of defect detection rate and number of defects found per hour, (2) there is no significant difference in the defect co

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subjects were divided into 3 groups, giving 10 subjects per perspective. The analysis results show that (1) there is no significant difference among the three perspectives in terms of defect detection rate and number of defects found per hour, (2) there is no significant difference in the defect co

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subjects were divided into 3 groups, giving 10 subjects per perspective. The analysis results show that (1) there is no significant difference among the three perspectives in terms of defect detection rate and number of defects found per hour, (2) there is no significant difference in the defect co

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Invariance to affine transformations,erformance — for example, the correlation invariants classified one of the digits better than the moment invariants..Particular emphasis was placed on the invariants‘ performance with coarsely sampled images; the moments perform well, and are more robust than the Fourier descriptors based on Arbter‘

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0302-9743 ous experiments investigating the stabilityof newand old invariants are discussed. The main conclusionis that momentinvariants are very effective, both forparti978-3-540-56713-4978-3-540-47634-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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r, designer, tester). The reading is made according to a special scenario, specific for each perspective. The basic assumption behind PBR is that the perspectives find different defects and a combination of several perspectives detects more defects compared to the same amount of reading with a singl