Tuomas Eerola

892 total citations
35 papers, 363 citations indexed

About

Tuomas Eerola is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology and Water Science and Technology. According to data from OpenAlex, Tuomas Eerola has authored 35 papers receiving a total of 363 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 10 papers in Molecular Biology and 6 papers in Water Science and Technology. Recurrent topics in Tuomas Eerola's work include Identification and Quantification in Food (10 papers), Image and Object Detection Techniques (8 papers) and Water Quality Monitoring Technologies (5 papers). Tuomas Eerola is often cited by papers focused on Identification and Quantification in Food (10 papers), Image and Object Detection Techniques (8 papers) and Water Quality Monitoring Technologies (5 papers). Tuomas Eerola collaborates with scholars based in Finland, Czechia and United States. Tuomas Eerola's co-authors include Heikki Kälviäinen, Lasse Lensu, Heikki Haario, Petri Toiviainen, Geoff Luck, Pavel Zemčík, Jarmo Ilonen, Arto Kaarna, Roman Juránek and Markéta Dubská and has published in prestigious journals such as IEEE Transactions on Image Processing, Sensors and Chemical Engineering Science.

In The Last Decade

Tuomas Eerola

31 papers receiving 328 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Tuomas Eerola Finland	11	137	56	53	48	44	35	363
Matías Nitsche Argentina	9	191 1.4×	31 0.6×	26 0.5×	31 0.6×	64 1.5×	19	475
Jesús Ruiz-Santaquiteria Spain	12	126 0.9×	55 1.0×	35 0.7×	71 1.5×	11 0.3×	23	384
José-Luis Lisani Spain	15	596 4.4×	34 0.6×	83 1.6×	46 1.0×	42 1.0×	46	830
Abdesslam Benzinou France	10	89 0.6×	55 1.0×	69 1.3×	26 0.5×	34 0.8×	29	303
Yolanda González-Cid Spain	12	206 1.5×	22 0.4×	43 0.8×	35 0.7×	47 1.1×	42	553
Sile Wang China	8	104 0.8×	26 0.5×	11 0.2×	23 0.5×	65 1.5×	20	345
Antoine Beyeler Switzerland	12	283 2.1×	13 0.2×	8 0.2×	34 0.7×	48 1.1×	20	620
Pejman Rasti France	14	174 1.3×	11 0.2×	9 0.2×	81 1.7×	50 1.1×	40	478
Zhou Rong China	14	143 1.0×	11 0.2×	27 0.5×	67 1.4×	142 3.2×	58	873

Countries citing papers authored by Tuomas Eerola

Since Specialization

Citations

This map shows the geographic impact of Tuomas Eerola'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 Tuomas Eerola with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tuomas Eerola more than expected).

Fields of papers citing papers by Tuomas Eerola

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tuomas Eerola. 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 Tuomas Eerola. The network helps show where Tuomas Eerola may publish in the future.

Co-authorship network of co-authors of Tuomas Eerola

This figure shows the co-authorship network connecting the top 25 collaborators of Tuomas Eerola. A scholar is included among the top collaborators of Tuomas Eerola based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tuomas Eerola. Tuomas Eerola is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Reich, Barbara, et al.. (2025). Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks. Pattern Recognition Letters. 193. 79–85.

Eerola, Tuomas, et al.. (2025). Re‐identification of patterned animals by multi‐image feature aggregation and geometric similarity. IET Computer Vision. 19(1).

Eerola, Tuomas, Lumi Haraguchi, Lasse Lensu, et al.. (2024). Survey of automatic plankton image recognition: challenges, existing solutions and future perspectives. Artificial Intelligence Review. 57(5). 13 indexed citations

Eerola, Tuomas, et al.. (2024). Species-Agnostic Patterned Animal Re-identification by Aggregating Deep Local Features. International Journal of Computer Vision. 132(9). 4003–4018. 7 indexed citations

Eerola, Tuomas, Lumi Haraguchi, Silke Van den Wyngaert, et al.. (2023). Toward phytoplankton parasite detection using autoencoders. Machine Vision and Applications. 34(6). 5 indexed citations

Eerola, Tuomas, et al.. (2022). Matching individual Ladoga ringed seals across short-term image sequences. Mammalian Biology. 102(3). 957–972. 7 indexed citations

Eerola, Tuomas, Lasse Lensu, Sanna Suikkanen, et al.. (2020). Towards operational phytoplankton recognition with automated high-throughput imaging and compact convolutional neural networks. 4 indexed citations

Eerola, Tuomas, et al.. (2020). Siamese Network Based Pelage Pattern Matching for Ringed Seal Re-identification. LUTPub (LUT University). 25–34. 27 indexed citations

Eerola, Tuomas, Lasse Lensu, Heikki Kälviäinen, et al.. (2020). Modelling internal knot distribution using external log features. Computers and Electronics in Agriculture. 179. 105795–105795. 6 indexed citations

10.

Eerola, Tuomas, et al.. (2018). Fish Detection from Low Visibility Underwater Videos. 1971–1976. 16 indexed citations

11.

Tamminen, Jussi, et al.. (2017). Determination of single droplet sizes, velocities and concentrations with image analysis for reactive extraction of copper. Chemical Engineering Science. 167. 54–65. 10 indexed citations

12.

Eerola, Tuomas, et al.. (2017). Automatic individual identification of Saimaa ringed seals. IET Computer Vision. 12(2). 146–152. 14 indexed citations

13.

Vuoskoski, Jonna K. & Tuomas Eerola. (2016). DOMAIN-SPECIFIC OR NOT? THE APPLICABILITY OF DIFFERENT EMOTION MODELS IN THE ASSESSMENT OF MUSIC-INDUCED EMOTIONS. 2 indexed citations

14.

Matas, Jiřı́, et al.. (2016). Detection of bubbles as concentric circular arrangements. Machine Vision and Applications. 27(3). 387–396. 14 indexed citations

15.

Eerola, Tuomas, et al.. (2015). Online measurement of the bubble size distribution in medium-consistency oxygen delignification. 68(2). 159. 2 indexed citations

16.

Eerola, Tuomas, et al.. (2011). Semisynthetic Ground Truth for Dirt Particle Counting and Classification Methods. Machine Vision and Applications. 215–218. 3 indexed citations

17.

Eerola, Tuomas, Joni‐Kristian Kämäräinen, Lasse Lensu, et al.. (2008). Finding best measurable quantities for predicting human visual quality experience. Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics. 733–738. 5 indexed citations

18.

Eerola, Tuomas, Joni‐Kristian Kämäräinen, Lasse Lensu, et al.. (2008). Is there hope for predicting human visual quality experience?. Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics. 42. 725–732. 6 indexed citations

19.

Eerola, Tuomas, Geoff Luck, & Petri Toiviainen. (2006). An investigation of preschoolers’ corporeal synchronization with music. 26 indexed citations

20.

Toiviainen, Petri & Tuomas Eerola. (2002). A computational Model of Melodic Similarity Based on Multiple Representations and Self-Organizing Maps. 4 indexed citations

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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