Song Mao

29 papers receiving 485 citations

Peers

Song Mao
Comparison fields: 5 of 58
  • Computer Vision and Pattern Recognition 254
  • Computer Networks and Communications 168
  • Electrical and Electronic Engineering 132
  • Artificial Intelligence 97
  • Information Systems 38
Replace Aggeliki Sgora with:
Aggeliki Sgora Greece
Rein Vesilo Australia
Yuwen Pan China
Ing-Yi Chen Taiwan
Lida Abdi Iran
Amir Hossein Jahangir Iran
Yuh-Rau Wang Taiwan
P. Balasubramanie India
Ismo Kärkkäinen Finland
P. Chenna Reddy India
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Citations per field
00.5×3.7×
Aggeliki Sgora · 1×
Citations per year

Countries citing papers authored by Song Mao

Since Specialization
Citations

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

Fields of papers citing papers by Song Mao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Song Mao

This figure shows the co-authorship network connecting the top 25 collaborators of Song Mao. A scholar is included among the top collaborators of Song Mao 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 Song Mao. Song Mao 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 2
2 2
3 13
4 2
5 21
6 77
7 70
8 16
9 1
10
PSET: A Page Segmentation Evaluation Toolkit
2
11 8
12
AUTOMATED METADATA EXTRACTION TO PRESERVE THE DIGITAL CONTENTS OF BIOMEDICAL COLLECTIONS
2
13 21
14 123
15 11
16 25
17
Stochastic Language Models for Automatic Acquisition of Lexicons from Printed Bilingual Dictionaries
3
18 64
19 3
20
A Methodology for Empirical Performance Evaluation of Page Segmentation Algorithms
7

About Song Mao

Song Mao is a scholar working on Computer Vision and Pattern Recognition, Statistics and Probability and Statistics, Probability and Uncertainty, having authored 30 papers that have together received 528 indexed citations. Recurring topics across this work include Handwritten Text Recognition Techniques (14 papers), Image Retrieval and Classification Techniques (7 papers) and Image Processing and 3D Reconstruction (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (254 citations), Computer Networks and Communications (168 citations) and Statistics and Probability (30 citations). Song Mao has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Tapas Kanungo, Azriel Rosenfeld, Chenyi Zhao, Chenglin Zhao, Yabin Ye, Zheng Zhou, Jong Woo Kim, Yimin Shi, Grid Thoma and George R. Thoma. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Communications of the ACM and IEEE Transactions on Image Processing.

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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