Seiichi Mita

2.4k total citations
124 papers, 1.7k citations indexed

About

Seiichi Mita is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering and Aerospace Engineering. According to data from OpenAlex, Seiichi Mita has authored 124 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Computer Vision and Pattern Recognition, 44 papers in Automotive Engineering and 27 papers in Aerospace Engineering. Recurrent topics in Seiichi Mita's work include Autonomous Vehicle Technology and Safety (44 papers), Video Surveillance and Tracking Methods (36 papers) and Advanced Neural Network Applications (22 papers). Seiichi Mita is often cited by papers focused on Autonomous Vehicle Technology and Safety (44 papers), Video Surveillance and Tracking Methods (36 papers) and Advanced Neural Network Applications (22 papers). Seiichi Mita collaborates with scholars based in Japan, United States and Switzerland. Seiichi Mita's co-authors include Vijay John, David McAllester, Quoc Huy, Chunzhao Guo, Keisuke Yoneda, Hossein Tehrani, Akihiro Takeuchi, Zheng Liu, Bin Qi and Zheng Liu and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Intelligent Transportation Systems and Japanese Journal of Applied Physics.

In The Last Decade

Seiichi Mita

118 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Seiichi Mita Japan	21	1.1k	668	416	226	206	124	1.7k
Pietro Cerri Italy	21	881 0.8×	703 1.1×	297 0.7×	174 0.8×	257 1.2×	32	1.4k
Denis F. Wolf Brazil	20	811 0.8×	610 0.9×	434 1.0×	306 1.4×	182 0.9×	101	1.6k
Xinyu Zhang China	25	1.0k 1.0×	370 0.6×	431 1.0×	160 0.7×	226 1.1×	120	1.9k
A. Fascioli Italy	21	1.6k 1.5×	1.1k 1.7×	341 0.8×	279 1.2×	215 1.0×	40	2.0k
Jennifer Dolson United States	8	883 0.8×	467 0.7×	270 0.6×	186 0.8×	219 1.1×	12	1.5k
Martin Lauer Germany	22	938 0.9×	583 0.9×	635 1.5×	278 1.2×	276 1.3×	96	2.2k
Dirk Langer United States	11	674 0.6×	569 0.9×	365 0.9×	279 1.2×	223 1.1×	23	1.4k
Cristiano Premebida Portugal	21	1.0k 0.9×	487 0.7×	424 1.0×	109 0.5×	150 0.7×	62	1.6k
Oliver Pink Germany	8	680 0.6×	670 1.0×	458 1.1×	248 1.1×	240 1.2×	10	1.5k
Jesse Levinson United States	9	1.0k 0.9×	742 1.1×	799 1.9×	219 1.0×	333 1.6×	9	1.9k

Countries citing papers authored by Seiichi Mita

Since Specialization

Citations

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

Fields of papers citing papers by Seiichi Mita

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Seiichi Mita

This figure shows the co-authorship network connecting the top 25 collaborators of Seiichi Mita. A scholar is included among the top collaborators of Seiichi Mita 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 Seiichi Mita. Seiichi Mita 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

Shimizu, Sota, et al.. (2019). Generation Method of Disparity Map for Driving Support by Wide-Angle Fovea Sensor. Transactions of the Society of Automotive Engineers of Japan. 50(4). 1 indexed citations

John, Vijay, et al.. (2018). Estimation of Steering Angle and Collision Avoidance for Automated Driving Using Deep Mixture of Experts. IEEE Transactions on Intelligent Vehicles. 3(4). 571–584. 10 indexed citations

John, Vijay, et al.. (2017). Gabor Filter and Gershgorin Disk-based Convolutional Filter Constraining for Image Classification. International Journal of Machine Learning and Computing. 7(4). 55–60. 3 indexed citations

Yoneda, Keisuke, et al.. (2015). Urban Road Localization by using Multiple Layer Matching and High Resolution 3D Point Cloud. Journal of the Japan Society for Precision Engineering. 81(11). 1017–1026. 2 indexed citations

Zhao, Lihua, Ryutaro Ichise, Seiichi Mita, & Yutaka Sasaki. (2015). Core Ontologies for Safe Autonomous Driving.. International Semantic Web Conference. 15 indexed citations

Huy, Quoc, Seiichi Mita, & Keisuke Yoneda. (2014). Practical Global and Local Path Planning Algorithm for Autonomous Vehicles Parking. Journal of the Japan Society for Precision Engineering. 80(3). 308–315. 2 indexed citations

Xie, Qiwei, et al.. (2014). Image Fusion Based on the \({\Delta ^{ - 1}} - T{V_0}\) Energy Function. Entropy. 16(11). 6099–6115. 2 indexed citations

Guo, Chunzhao, et al.. (2014). Drivable Road Boundary Detection for Intelligent Vehicles Based on Stereovision with Plane-induced Homography. ACTA AUTOMATICA SINICA. 39(4). 371–380. 1 indexed citations

Okuda, Hiroyuki, Yuichi Tazaki, Tatsuya Suzuki, et al.. (2013). Cooperative Automatic Parking System Based on Consensus Control with Mutual Exclusion Mechanism. Transactions of the Society of Instrument and Control Engineers. 49(11). 986–993. 1 indexed citations

10.

Hayashi, Rina, et al.. (2013). Path Planning Based on Support Vector Machine for Autonomous Vehicle Part 2. 1 indexed citations

11.

Mita, Seiichi, et al.. (2011). Reduction of Bit Errors Due to Intertrack Interference Using LLRs of Neighboring Tracks. IEEE Transactions on Magnetics. 47(10). 3316–3319. 3 indexed citations

12.

Mita, Seiichi. (2009). Reduction of bit error rate due to inter-track interference by iterative use of its adaptive estimation and log likelihood ratios of neighboring tracks. IEICE Technical Report; IEICE Tech. Rep.. 109. 35–42. 2 indexed citations

13.

Guo, Chunzhao & Seiichi Mita. (2009). Drivable road region detection based on homography estimation with road appearance and driving state models. e81 d. 204–209. 11 indexed citations

14.

Yoshida, Eiji & Seiichi Mita. (2007). Some properties of Quaternion Neural Network. IEICE Technical Report; IEICE Tech. Rep.. 107(157). 29–34. 1 indexed citations

15.

Mita, Seiichi & Hajime Matsui. (2003). Current and Future Schemes for Securing Data Reliability in HDD. 103(181). 7–12. 1 indexed citations

16.

Mita, Seiichi, et al.. (1999). Modified EEPRML with 16/17 (3;11) MTR Code and Cyclic Redundancy Check Code for High Density Magnetic Recording Channels. IEICE Transactions on Electronics. 82(12). 2201–2208. 2 indexed citations

17.

Kobayashi, Naoya, et al.. (1998). PRML Detection by List-Viterbi Algorithm. 81(7). 654–663. 1 indexed citations

18.

Mita, Seiichi. (1992). Recent Developments of Signal Processing in Digital Magnetic Storage. Journal of the Magnetics Society of Japan. 16(S_1_PMRS_92). S1_131–140. 2 indexed citations

19.

Mita, Seiichi, et al.. (1985). Digital Video Recording Techniques Using 1/2" Metal Particle Tape. 9(21). 13–18. 1 indexed citations

20.

Mita, Seiichi, et al.. (1981). . The Journal of the Institute of Television Engineers of Japan. 35(7). 563–569. 2 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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