Assessing Hierarchies by Their Consistent Segmentations

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Abstract
Cite
Gutman, Zeev, et al. “Assessing Hierarchies by Their Consistent Segmentations”. Journal of Mathematical Imaging and Vision, 2024, https://doi.org/10.1007/s10851-024-01176-z.
Gutman, Z., Vij, R., Najman, L., & Lindenbaum, M. (2024). Assessing Hierarchies by Their Consistent Segmentations. Journal of Mathematical Imaging and Vision. https://doi.org/10.1007/s10851-024-01176-z
Gutman, Zeev, Ritvik Vij, Laurent Najman, and Michael Lindenbaum. “Assessing Hierarchies by Their Consistent Segmentations”. Journal of Mathematical Imaging and Vision, 2024. https://doi.org/10.1007/s10851-024-01176-z.
1.
Gutman Z, Vij R, Najman L, Lindenbaum M. Assessing Hierarchies by Their Consistent Segmentations. Journal of Mathematical Imaging and Vision. 2024;.
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Refrences
Title Journal Journal Categories Citations Publication Date
10.1109/TPAMI.2017.2700300 IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 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
  • Technology: Engineering (General). Civil engineering (General)
 2017
10.1109/TPAMI.2016.2554550 IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 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
  • Technology: Engineering (General). Civil engineering (General)
 2017
10.1109/TIP.2017.2779604 IEEE Transactions on Image Processing
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 2017
Local Variation as a Statistical Hypothesis Test International Journal of Computer Vision
  • 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)
 2 2016
10.1109/TPAMI.2015.2481406 2016
Refrences Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science16
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics14
Technology: Engineering (General). Civil engineering (General)14
Technology: Mechanical engineering and machinery12
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks9
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software2
Science: Mathematics1
Science: Physics1
Medicine: Medicine (General): Medical technology1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 16 is the most frequently represented among the references in this article. It primarily includes studies from IEEE Transactions on Pattern Analysis and Machine Intelligence and IEEE Transactions on Image Processing. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year