NIST Technical NoteU.S. Department of Commerce, National Institute of Standards and Technology, 1989 |
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... Template Matching B8.1.3 . Parametric Edge Modeling .. B8.2 . Region Extraction .............. B8.2.1 . Intensity and Color ......... B8.2.2 . Texture B8.3 . Optical Flow ...... B8.4 . Evaluation ............. 32 .... 34 34 34 .... 37 ...
... Template Matching B8.1.3 . Parametric Edge Modeling .. B8.2 . Region Extraction .............. B8.2.1 . Intensity and Color ......... B8.2.2 . Texture B8.3 . Optical Flow ...... B8.4 . Evaluation ............. 32 .... 34 34 34 .... 37 ...
Page 34
... template matching , or parametric model fitting . These boundary features take the form of either edges or corners . Corner detection will be dis- cussed in the Level 2 Perception Processing document . The following sections provide ...
... template matching , or parametric model fitting . These boundary features take the form of either edges or corners . Corner detection will be dis- cussed in the Level 2 Perception Processing document . The following sections provide ...
Page 37
... Template Matching In template matching , an edge pattern is centered on each pixel in an image , and the closeness of their correspondence is measured . Since these templates often represent sec- ond differences of step edges , the ...
... Template Matching In template matching , an edge pattern is centered on each pixel in an image , and the closeness of their correspondence is measured . Since these templates often represent sec- ond differences of step edges , the ...
Page 38
... templates with larg- er spans offer the advantage of being less sensitive to noise . However , larger templates have ... template - based method , used by Frei and Chen [ FREI77 ] , chooses orthogonal 3x3 masks as a basis for expansion ...
... templates with larg- er spans offer the advantage of being less sensitive to noise . However , larger templates have ... template - based method , used by Frei and Chen [ FREI77 ] , chooses orthogonal 3x3 masks as a basis for expansion ...
Page 39
... template and those in the image window . Define the mean , a , and the variance , σ , in the template as : + k Σ Pim απ n l = -k_m = -k [ 10 ] σ ( p ) = Σ ( PLM n 1 = -k m = -k 1 Σ Σ im - α ) 2 · ( where p1 is a pixel in the template at ...
... template and those in the image window . Define the mean , a , and the variance , σ , in the template as : + k Σ Pim απ n l = -k_m = -k [ 10 ] σ ( p ) = Σ ( PLM n 1 = -k m = -k 1 Σ Σ im - α ) 2 · ( where p1 is a pixel in the template at ...
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algorithm parameters Appendix Architecture Artificial Intelligence averaging binary blurred camera class of algorithms command number Execution command number Planning Comparator Module Computer Computer Vision Corner Detection described edge detection Engineering enhancement Execution command number Execution module Figure frequency functions Gaithersburg global data system GONZA77 grey level hierarchy High-Pass Filtering histogram IEEE Image Processing input Institute of Standards intensity Job Assignment module Level 1 Job Level 1 Sensory low-pass Low-Pass Filtering manipulator mask measure methods model global memory module receives neighborhood NIST number Execution status object optical flow output performed pixels Planner module Precision requirements Priority preprocessing technique priority level assigned queue requirements Precision requirements Robotics ROSEN82 segmentation selected algorithm sensory processing module sensory processing system SP WM TD spatial integrator specific Standards and Technology step edge surface patch Tactile Task Decomposition interface task decomposition module Telerobot texture threshold value tion update Visual Perception world model global
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