Christian Klukas

Computer Vision and Pattern Recognition · 2006–2017

A researcher in the Science corpus: 6 papers; trusts 30 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 66 citations into it; #6,546 of 21,575 by trust here, #12,172 by citations
• Plant Science — 58 citations into it; #864 of 8,232 by trust here, #3,980 by citations
• Molecular Biology — 28 citations into it
• Ecology — 17 citations into it
• Biophysics — 9 citations into it
• Genetics — 8 citations into it
• Media Technology — 7 citations into it
• Environmental Engineering — 6 citations into it; #949 of 5,479 by trust here, #2,527 by citations

Whom Christian Klukas 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. Teuvo Kohonen
6. Jean-Michel Pape
7. Amos Bairoch
8. Tomokazu Konishi
9. Xi Yin
10. Susumu Goto

Who trusts Christian Klukas overall

1. Massimo Minervini
2. Mario Valerio Giuffrida
3. Jean-Michel Pape
4. Hanno Scharr
5. Sotirios A. Tsaftaris
6. Jordan R. Ubbens
7. Andreas Fischbach
8. Bernardino Romera-Paredes
9. Weijun Luo
10. Ian Stavness

Papers from Christian Klukas's perspective

Ordered by how strongly the researchers Christian Klukas trusts endorse each paper. Same corpus, a different order for every researcher.

1. International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge
2. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics
3. Submitted to Ieee Transactions on Pattern Analysis and Machine Intelligence Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
4. 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.
5. A Computational Approach to Edge Detection
6. Random Walks for Image Segmentation
7. SPICY: towards automated phenotyping of large pepper plants in the greenhouse.
8. Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects1[C][W]
9. Annotated Image Datasets of Rosette Plants
10. An Exemplar-Based CRF for Multi-instance Object Segmentation

Their papers

• Leaf segmentation in plant phenotyping: a collation study (2015) — 281 citations
• VANTED: A system for advanced data analysis and visualization in the context of biological networks (2006) — 550 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
• Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014) — 359 citations