John W. V. Miller
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence top 10%
- Water Science and Technology top 10%
- Media Technology top 5%
- Materials Chemistry
- Co-authors
- Padhraic SmythR.M. GoodmanFrederick M. WaltzCharles M. HigginsM. ShridharJoe P. WindhamJames B. FarisonKai Wang
- Topics
- Medical Image Segmentation Techniques (9 papers)Industrial Vision Systems and Defect Detection (7 papers)CCD and CMOS Imaging Sensors (6 papers)
- Journals
- IEEE Transactions on Information TheoryIEEE Transactions on Image ProcessingSoil Science Society of America Journal
- Partner nations
- United StatesUnited KingdomGermany
In The Last Decade
John W. V. Miller
37 papers receiving 454 citations
Peers
Comparison fields: 5 of 104
- Computer Vision and Pattern Recognition 124
- Artificial Intelligence 113
- Water Science and Technology 112
- Media Technology 67
- Materials Chemistry 46
Countries citing papers authored by John W. V. Miller
This map shows the geographic impact of John W. V. Miller'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 John W. V. Miller with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John W. V. Miller more than expected).
Fields of papers citing papers by John W. V. Miller
This network shows the impact of papers produced by John W. V. Miller. 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 John W. V. Miller. The network helps show where John W. V. Miller may publish in the future.
Co-authorship network of co-authors of John W. V. Miller
This figure shows the co-authorship network connecting the top 25 collaborators of John W. V. Miller. A scholar is included among the top collaborators of John W. V. Miller 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 John W. V. Miller. John W. V. Miller is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | 6 | |
| 5 | 2 | |
| 6 | 5 | |
| 7 | 3 | |
| 8 | Machine Vision Systems for Inspection and Metrology VII | 6 |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 16 | |
| 13 | 46 | |
| 14 | 15 | |
| 15 | 0 | |
| 16 | 1 | |
| 17 | 52 | |
| 18 | 1 | |
| 19 | An Information Theoretic Approach to Rule-Based Connectionist Expert Systems | 11 |
| 20 | 6 |
About John W. V. Miller
John W. V. Miller is a scholar working on Media Technology, Computer Vision and Pattern Recognition and Industrial and Manufacturing Engineering, having authored 42 papers that have together received 498 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (9 papers), Industrial Vision Systems and Defect Detection (7 papers) and CCD and CMOS Imaging Sensors (6 papers). The work is most often cited by research in Water Science and Technology (112 citations), Media Technology (67 citations) and Computer Vision and Pattern Recognition (124 citations). John W. V. Miller has collaborated with scholars based in United States, United Kingdom and Germany. Frequent co-authors include Padhraic Smyth, R.M. Goodman, Frederick M. Waltz, Charles M. Higgins, M. Shridhar, Joe P. Windham, James B. Farison, Kai Wang, Serge Belongie and F. Alleva. Their work appears in journals such as IEEE Transactions on Information Theory, IEEE Transactions on Image Processing and Soil Science Society of America Journal.
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.