Richard W. Conners

72 papers receiving 2.2k citations

Hit Papers

A Theoretical Comparison of Texture Algorithms19802026199520101980200400600

Peers

Richard W. Conners
Comparison fields: 5 of 143
  • Computer Vision and Pattern Recognition 913
  • Electrical and Electronic Engineering 605
  • Industrial and Manufacturing Engineering 459
  • Control and Systems Engineering 429
  • Media Technology 311
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Countries citing papers authored by Richard W. Conners

Since Specialization
Citations

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

Fields of papers citing papers by Richard W. Conners

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard W. Conners. 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 Richard W. Conners. The network helps show where Richard W. Conners may publish in the future.

Co-authorship network of co-authors of Richard W. Conners

This figure shows the co-authorship network connecting the top 25 collaborators of Richard W. Conners. A scholar is included among the top collaborators of Richard W. Conners 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 Richard W. Conners. Richard W. Conners 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
#WorkIndexed citations
1
Source of ENF in Battery-Powered Digital Recordings
22
2
Tampering Detection of Digital Recordings Using Electric Network Frequency and Phase Angle
10
3 47
4
Practical issues in frequency disturbance recorder design for wide-area monitoring
13
5 5
6 2
7 4
8 1
9
Using MORRPH in an Industrial Machine Vision System
12
10 36
11 2
12
A Machine Vision System for Automatically Grading Hardwood Lumber - (Industrial Metrology)
7
13
Lumber Scanning System for Surface Defect Detection
1
14 27
15
3-D Signal Processing in A Computer Vision System
5
16
A Multisensor Machine Vision System for Hardwood Defect Detection
4
17
Automated computer grading of hardwood lumber
31
18
ALPS- A potential new automated lumber processing system
19
19 143
20 84

About Richard W. Conners

Richard W. Conners is a scholar working on Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition and Environmental Engineering, having authored 80 papers that have together received 2.4k indexed citations. Recurring topics across this work include Industrial Vision Systems and Defect Detection (40 papers), Remote Sensing and LiDAR Applications (22 papers) and Image and Object Detection Techniques (17 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (459 citations), Computer Vision and Pattern Recognition (913 citations) and Media Technology (311 citations). Richard W. Conners has collaborated with scholars based in United States, China and South Africa. Frequent co-authors include Charles A. Harlow, Mohan M. Trivedi, Yilu Liu, Philip A. Araman, Charles W. McMillin, Penn Markham, Zhiyong Yuan, Virgilio Centeno, A.G. Phadke and Changqing Xu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Power Systems and IEEE Transactions on Smart Grid.

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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