Takashi Naito

806 total citations
35 papers, 557 citations indexed

About

Takashi Naito is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Automotive Engineering. According to data from OpenAlex, Takashi Naito has authored 35 papers receiving a total of 557 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Computer Vision and Pattern Recognition, 12 papers in Aerospace Engineering and 11 papers in Automotive Engineering. Recurrent topics in Takashi Naito's work include Video Surveillance and Tracking Methods (18 papers), Advanced Image and Video Retrieval Techniques (11 papers) and Autonomous Vehicle Technology and Safety (11 papers). Takashi Naito is often cited by papers focused on Video Surveillance and Tracking Methods (18 papers), Advanced Image and Video Retrieval Techniques (11 papers) and Autonomous Vehicle Technology and Safety (11 papers). Takashi Naito collaborates with scholars based in Japan, Switzerland and United States. Takashi Naito's co-authors include Kiyosumi Kidono, Yoshiko Kojima, Akihiro Watanabe, Yoshiki Ninomiya, Jun Miura, Junichi MEGURO, Chunzhao Guo, Koichiro Yamaguchi, Takashi Machida and Hui Cao and has published in prestigious journals such as IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles and Machine Vision and Applications.

In The Last Decade

Takashi Naito

34 papers receiving 526 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Takashi Naito Japan 13 358 225 120 119 59 35 557
Alireza Asvadi Portugal 10 380 1.1× 229 1.0× 184 1.5× 215 1.8× 57 1.0× 18 631
Kiyosumi Kidono Japan 13 310 0.9× 181 0.8× 81 0.7× 156 1.3× 108 1.8× 33 527
Tao Wu China 15 484 1.4× 302 1.3× 207 1.7× 148 1.2× 65 1.1× 79 784
Yifeng Shi China 10 388 1.1× 177 0.8× 75 0.6× 127 1.1× 64 1.1× 18 575
Pengpeng Sun China 9 192 0.5× 200 0.9× 97 0.8× 98 0.8× 53 0.9× 15 460
Naoki Suganuma Japan 12 265 0.7× 279 1.2× 124 1.0× 204 1.7× 88 1.5× 72 561
Luca Caltagirone Sweden 6 312 0.9× 238 1.1× 173 1.4× 117 1.0× 18 0.3× 8 501
Luís Garrote Portugal 10 295 0.8× 139 0.6× 75 0.6× 145 1.2× 68 1.2× 38 503
Chunzhao Guo Japan 13 380 1.1× 343 1.5× 125 1.0× 125 1.1× 58 1.0× 43 611
Clemens Rabe Germany 11 519 1.4× 261 1.2× 67 0.6× 156 1.3× 53 0.9× 15 701

Countries citing papers authored by Takashi Naito

Since Specialization
Citations

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

Fields of papers citing papers by Takashi Naito

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takashi Naito

This figure shows the co-authorship network connecting the top 25 collaborators of Takashi Naito. A scholar is included among the top collaborators of Takashi Naito 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 Takashi Naito. Takashi Naito is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Guo, Chunzhao, Kiyosumi Kidono, Junichi MEGURO, et al.. (2016). A Low-Cost Solution for Automatic Lane-Level Map Generation Using Conventional In-Car Sensors. IEEE Transactions on Intelligent Transportation Systems. 17(8). 2355–2366. 48 indexed citations
2.
Guo, Chunzhao, Junichi MEGURO, Yoshiko Kojima, & Takashi Naito. (2014). Automatic lane-level map generation for advanced driver assistance systems using low-cost sensors. 3975–3982. 25 indexed citations
3.
Kidono, Kiyosumi, et al.. (2013). Sparse FIND: A Novel Low Computational Cost Feature for Object Classification. Journal of the Japan Society for Precision Engineering. 79(11). 1063–1068. 3 indexed citations
4.
Ishida, Hiroyuki, Junichi MEGURO, Yoshiko Kojima, & Takashi Naito. (2013). 3D Road Boundary Detection Using Conformal Geometric Algebra. 5(0). 176–182. 3 indexed citations
5.
Kidono, Kiyosumi, Takashi Naito, & Jun Miura. (2012). Reliable pedestrian recognition combining high-definition LIDAR and vision data. 9. 1783–1788. 17 indexed citations
6.
Ishida, Hiroyuki, Kiyosumi Kidono, Yoshiko Kojima, & Takashi Naito. (2012). Road marking recognition for map generation using sparse tensor voting. 1132–1135. 5 indexed citations
7.
Noda, Masafumi, Tomokazu Takahashi, Daisuke Deguchi, et al.. (2011). Detection of Road Markings Recorded in In-Vehicle Camera Images by Using Position-Dependent Classifiers. IEEJ Transactions on Industry Applications. 131(4). 466–474. 2 indexed citations
8.
Naito, Takashi, et al.. (2011). Human Skin Detection by Visible and Near-Infrared Imaging. Machine Vision and Applications. 503–507. 22 indexed citations
9.
Kidono, Kiyosumi, Akihiro Watanabe, Takashi Naito, & Jun Miura. (2011). Pedestrian Recognition Using High-definition LIDAR. Journal of the Robotics Society of Japan. 29(10). 963–970. 3 indexed citations
10.
Noda, Masafumi, Tomokazu Takahashi, Daisuke Deguchi, et al.. (2011). Road image update using in-vehicle camera images and aerial image. 460–465. 6 indexed citations
11.
Deguchi, Daisuke, et al.. (2010). Improvement of Automatic Calibration of an In-vehicle Gaze Tracking System Using Positional Relation between Gaze Targets. IEICE Technical Report; IEICE Tech. Rep.. 109(470). 199–204. 1 indexed citations
12.
Noda, Masafumi, Tomokazu Takahashi, Ichiro Ide, et al.. (2009). Recognition of Road Markings from In-Vehicle Camera Images by a Generative Learning Method. Machine Vision and Applications. 514–517. 14 indexed citations
13.
Noda, Masafumi, Tomokazu Takahashi, Daisuke Deguchi, et al.. (2009). Ego-localization by Matching In-vehicle Camera Images with Aerial Image. IEICE Technical Report; IEICE Tech. Rep.. 109(306). 177–182. 2 indexed citations
14.
Yamaguchi, Koichiro, et al.. (2009). Road scene labeling using SfM module and 3D bag of textons. 4. 657–664. 5 indexed citations
15.
Yamaguchi, Koichiro, et al.. (2009). Texture Segmentation of Road Environment Scene Using SfM Module and HLAC Features. 1. 220–230. 1 indexed citations
16.
Deguchi, Daisuke, et al.. (2009). Automatic calibration of an in-vehicle gaze tracking system using driver's typical gaze behavior. 998–1003. 9 indexed citations
17.
Naito, Takashi, et al.. (2008). A Hierarchical Threat Assessment Architecture for Driver Assistance Systems in Urban Areas. 2 indexed citations
18.
Cao, Hui, Takashi Naito, & Yoshiki Ninomiya. (2008). Approximate RBF Kernel SVM and Its Applications in Pedestrian Classification. INRIA a CCSD electronic archive server. 24 indexed citations
19.
Yamada, Keiichi, Takashi Naito, & Shin Yamamoto. (1999). Image Information Media for ITS. Applications of Wide Dynamic Range Camera to Image Sensing for ITS.. The Journal of The Institute of Image Information and Television Engineers. 53(5). 746–748.
20.
Naito, Takashi, Toshihiko Tsukada, Keiichi Yamada, Kazuhiro Kozuka, & Shin Yamamoto. (1998). Image Acquisition and Recognition of License Plates of Passing Vehicles under Varied Illummination Condition. IEEJ Transactions on Sensors and Micromachines. 118(6). 312–318. 1 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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