Thresholding for Change Detection.
作者： Paul L. Rosin
DOI:
关键词: Mathematics 、 Pattern recognition 、 Signal 、 Image differencing 、 Artificial intelligence 、 Change detection 、 Euler number 、 Poisson distribution 、 Intensity (heat transfer) 、 Thresholding 、 Noise (signal processing)
摘要: Abstract Image differencing is used for many applications involving change detection. Although it usually followed by a thresholding operation to isolate regions of there are few methods available in the literature specific (and appropriate for) We describe four different selecting thresholds that work on very principles. Either noise or signal modeled, and model covers either spatial intensity distribution characteristics. The as follows: (1) Normal distribution, (2) intensities tested making local comparisons two image frames (i.e., difference map not used), (3) properties modeled Poisson (4) stable number (or Euler number).
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