Jean-Michel Pape

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 63 citations into it; #6,547 of 21,575 by trust here, #12,172 by citations
• Plant Science — 43 citations into it; #865 of 8,232 by trust here, #4,987 by citations
• Ecology — 16 citations into it
• Biophysics — 7 citations into it
• Media Technology — 7 citations into it
• Molecular Biology — 7 citations into it
• Environmental Engineering — 6 citations into it; #950 of 5,479 by trust here, #2,527 by citations

Whom Jean-Michel Pape trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Massimo Minervini
2. Hanno Scharr
3. Igor Kononenko
4. Tin Kam Ho
5. Xi Yin
6. Sotirios A. Tsaftaris
7. Mark A. Hall
8. Mohammed M. Abdelsamea
9. Neeraj Kumar
10. Christian Klukas

Who trusts Jean-Michel Pape overall

1. Massimo Minervini
2. Mario Valerio Giuffrida
3. Hanno Scharr
4. Jordan R. Ubbens
5. Sotirios A. Tsaftaris
6. Andreas Fischbach
7. Christian Klukas
8. Ian Stavness
9. Bernardino Romera-Paredes
10. Mengye Ren

Papers from Jean-Michel Pape's perspective

Ordered by how strongly the researchers Jean-Michel Pape trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Distinctive Image Features from Scale-Invariant Keypoints
3. International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
6. Submitted to Ieee Transactions on Pattern Analysis and Machine Intelligence Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
7. A Computational Approach to Edge Detection
8. Dynamics of seedling growth acclimation towards altered light conditions can be quantified via GROWSCREEN: a setup and procedure designed for rapid optical phenotyping of different plant species.
9. ImageNet Large Scale Visual Recognition Challenge
10. ImageNet: A large-scale hierarchical image database

Their papers

• Leaf segmentation in plant phenotyping: a collation study (2015) — 281 citations
• segmentation in plant phenotyping: a collation study. Machine Vision and Applications (2017)
• Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images (2015) — 55 citations
• 3-D Histogram-Based Segmentation and Leaf Detection for Rosette Plants (2014) — 97 citations