Dalcimar Casanova

1.5k citations
53 papers · 909 · h-index 13

Impact in

Papers in

Dalcimar Casanova

47 papers receiving 888 citations

Peers

Dalcimar Casanova
Comparison fields: 5 of 126
  • Computer Vision and Pattern Recognition 360
  • Media Technology 91
  • Analytical Chemistry 61
  • Plant Science 213
  • Artificial Intelligence 157
Replace En Fan with:
En Fan China
André Ricardo Backes Brazil
Meng Zhou China
Marco Mora Chile
Amr Badr Egypt
Zhilu Wu China
Hong Chen China
Ashish Kumar Tripathi India
Fardin Akhlaghian Tab Iran
Dalcimar Casanova relative to En Fan China En Fan's profile →
Citations per field
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Citations per year

Countries citing papers authored by Dalcimar Casanova

Since Specialization
Citations

This map shows the geographic impact of Dalcimar Casanova's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dalcimar Casanova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dalcimar Casanova more than expected).

Fields of papers citing papers by Dalcimar Casanova

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dalcimar Casanova. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dalcimar Casanova. The network helps show where Dalcimar Casanova may publish in the future.

Co-authors

The 25 scholars most cited alongside Dalcimar Casanova, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dalcimar Casanova Line = papers co-authored together Dalcimar Casanova links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014163
2 2011121
3 2009101
4 200990
5 200888
6 201282
7 201035
8 201824
9 202122
10 202317
11 201616
12 202314
13 201912
14 202211
15
IFSC/USP at ImageCLEF 2011: Plant identication task
20119
16 20019
17 20198
18 20138
19
Automatic counting of stomata in epidermis microscopic images
20147
20 20187

About Dalcimar Casanova

Dalcimar Casanova is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Plant Science, Industrial and Manufacturing Engineering and Signal Processing, having authored 53 papers that have together received 909 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (11 papers), Smart Agriculture and AI (6 papers), Image and Object Detection Techniques (5 papers), Industrial Vision Systems and Defect Detection (5 papers), Neural Networks and Applications (5 papers), Biometric Identification and Security (4 papers), Greenhouse Technology and Climate Control (4 papers) and Animal Behavior and Welfare Studies (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (360 citations), Media Technology (91 citations), Analytical Chemistry (61 citations), Plant Science (213 citations) and Artificial Intelligence (157 citations). Dalcimar Casanova has collaborated with scholars based in Brazil, Ireland and United Arab Emirates. Frequent co-authors include Odemir Martinez Bruno, André Ricardo Backes, Jarbas Joaci de Mesquita Sá, Gonzalo Travieso, Luciano da Fontoura Costa, Diego R. Amancio, César H. Comin, Marcelo Teixeira, João B. Florindo and Rosana Marta Kolb. Their work appears in journals such as Information Sciences, IEEE Access, International Journal of Production Research, Applied Soft Computing and Pattern Recognition.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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