Local Variation as a Statistical Hypothesis Test

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Cite
Baltaxe, Michael, et al. “Local Variation As a Statistical Hypothesis Test”. International Journal of Computer Vision, vol. 117, no. 2, 2015, pp. 131-4, https://doi.org/10.1007/s11263-015-0855-4.
Baltaxe, M., Meer, P., & Lindenbaum, M. (2015). Local Variation as a Statistical Hypothesis Test. International Journal of Computer Vision, 117(2), 131-141. https://doi.org/10.1007/s11263-015-0855-4
Baltaxe, Michael, Peter Meer, and Michael Lindenbaum. “Local Variation As a Statistical Hypothesis Test”. International Journal of Computer Vision 117, no. 2 (2015): 131-41. https://doi.org/10.1007/s11263-015-0855-4.
Baltaxe M, Meer P, Lindenbaum M. Local Variation as a Statistical Hypothesis Test. International Journal of Computer Vision. 2015;117(2):131-4.
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Mathematics
Instruments and machines
Electronic computers
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  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 47 2015
10.1109/TPAMI.2014.2377715 2015
10.1109/TPAMI.2012.231 2013
10.1109/TPAMI.2012.28 2012
10.1109/TPAMI.2012.120 2012
Citations
Title Journal Journal Categories Citations Publication Date
Assessing Hierarchies by Their Consistent Segmentations

 Journal of Mathematical Imaging and Vision
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 2024
Graph based over-segmentation methods for 3D point clouds Computer Vision and Image Understanding
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 19 2018
Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science2
Technology: Mechanical engineering and machinery2
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics2
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software1
Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 2 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Graph based over-segmentation methods for 3D point clouds and was published in 2018. The most recent citation comes from a 2024 study titled Assessing Hierarchies by Their Consistent Segmentations. This article reached its peak citation in 2024, with 1 citations. It has been cited in 2 different journals. Among related journals, the Journal of Mathematical Imaging and Vision cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year