Phil Sallee

912 total citations
14 papers, 347 citations indexed

About

Phil Sallee is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Phil Sallee has authored 14 papers receiving a total of 347 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 2 papers in Computational Mechanics. Recurrent topics in Phil Sallee's work include Image and Signal Denoising Methods (4 papers), Sparse and Compressive Sensing Techniques (2 papers) and Seismic Imaging and Inversion Techniques (2 papers). Phil Sallee is often cited by papers focused on Image and Signal Denoising Methods (4 papers), Sparse and Compressive Sensing Techniques (2 papers) and Seismic Imaging and Inversion Techniques (2 papers). Phil Sallee collaborates with scholars based in United States and Switzerland. Phil Sallee's co-authors include Bruno A. Olshausen, Michael S. Lewicki, Matthew Farrens, Ranga Raju Vatsavai, E Bright, Anil Cheriyadat, Budhendra Bhaduri, Michael Ingram, Michael Hegarty and John B. Greer and has published in prestigious journals such as International Journal of Image and Graphics, DigitalCommons - CalPoly (California State Polytechnic University) and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.

In The Last Decade

Phil Sallee

14 papers receiving 327 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Phil Sallee United States 8 247 53 52 45 40 14 347
Diego Valsesia Italy 12 319 1.3× 66 1.2× 107 2.1× 134 3.0× 39 1.0× 52 470
Éric Debreuve France 9 191 0.8× 42 0.8× 15 0.3× 24 0.5× 41 1.0× 26 297
In Kyu Park South Korea 11 276 1.1× 23 0.4× 52 1.0× 24 0.5× 29 0.7× 29 330
Chengyi Xiong China 10 258 1.0× 27 0.5× 52 1.0× 67 1.5× 120 3.0× 48 367
Shouhong Wan China 11 152 0.6× 60 1.1× 93 1.8× 39 0.9× 30 0.8× 58 360
Qinghao Hu China 7 208 0.8× 128 2.4× 31 0.6× 12 0.3× 19 0.5× 14 320
Mohamed S. El-Mahallawy Egypt 9 103 0.4× 81 1.5× 22 0.4× 21 0.5× 24 0.6× 32 365
Minoru Maruyama Japan 9 231 0.9× 47 0.9× 55 1.1× 11 0.2× 41 1.0× 44 303

Countries citing papers authored by Phil Sallee

Since Specialization
Citations

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

Fields of papers citing papers by Phil Sallee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Phil Sallee

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

All Works

14 of 14 papers shown
1.
Sallee, Phil, et al.. (2019). Comparing the Effects of Annotation Type on Machine Learning Detection Performance. 855–861. 15 indexed citations
2.
Hegarty, Michael, et al.. (2019). High-Performance Deep Learning Classification for Radio Signals. 1026–1029. 6 indexed citations
3.
Euliss, Gary W., John B. Greer, Glenn R. Easley, et al.. (2014). Experimental study of super-resolution using a compressive sensing architecture. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 9109. 91090F–91090F. 3 indexed citations
4.
Sallee, Phil, et al.. (2010). Training and feature-reduction techniques for human identification using anthropometry. 5404. 1–8. 13 indexed citations
5.
6.
Ingram, Michael, et al.. (2008). Current challenges in automating visual perception. 290. 1–8. 3 indexed citations
7.
Cheriyadat, Anil, et al.. (2008). Overhead image statistics. 1–8. 14 indexed citations
8.
Camenisch, Jan, Christian Collberg, Neil F. Johnson, & Phil Sallee. (2006). Proceedings of the 8th international conference on Information hiding. 1 indexed citations
9.
Sallee, Phil. (2005). MODEL-BASED METHODS FOR STEGANOGRAPHY AND STEGANALYSIS. International Journal of Image and Graphics. 5(1). 167–189. 116 indexed citations
10.
Sallee, Phil & Bruno A. Olshausen. (2004). Image denoising using learned overcomplete representations. 2. III–381. 1 indexed citations
11.
Sallee, Phil, et al.. (2002). Branch transition rate: a new metric for improved branch classification analysis. DigitalCommons - CalPoly (California State Polytechnic University). 241–250. 25 indexed citations
12.
Sallee, Phil & Bruno A. Olshausen. (2002). Learning Sparse Multiscale Image Representations. 15. 1351–1358. 51 indexed citations
13.
Olshausen, Bruno A., Phil Sallee, & Michael S. Lewicki. (2000). Learning Sparse Image Codes using a Wavelet Pyramid Architecture. 13. 887–893. 43 indexed citations
14.
Olshausen, Bruno A., Phil Sallee, & Michael S. Lewicki. (2000). Learning sparse wavelet codes for natural images. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4119. 200–200. 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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