Peter W. Pachowicz

600 total citations
29 papers, 61 citations indexed

About

Peter W. Pachowicz is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Peter W. Pachowicz has authored 29 papers receiving a total of 61 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 5 papers in Control and Systems Engineering. Recurrent topics in Peter W. Pachowicz's work include Neural Networks and Applications (10 papers), Image Retrieval and Classification Techniques (8 papers) and Advanced Image and Video Retrieval Techniques (5 papers). Peter W. Pachowicz is often cited by papers focused on Neural Networks and Applications (10 papers), Image Retrieval and Classification Techniques (8 papers) and Advanced Image and Video Retrieval Techniques (5 papers). Peter W. Pachowicz collaborates with scholars based in United States and South Korea. Peter W. Pachowicz's co-authors include Sung Wook Baik, Jerzy Bala, Harry Wechsler, Ryszard S. Michalski, Lee W. Wagenhals, Alexander H. Levis, Jan M. Żytkow, Brian R. Hirshman, Janusz Wnek and Kenneth De Jong and has published in prestigious journals such as Pattern Recognition Letters, Image and Vision Computing and IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans.

In The Last Decade

Peter W. Pachowicz

19 papers receiving 50 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peter W. Pachowicz United States	5	37	28	7	7	6	29	61
Marco Fornoni Switzerland	6	54 1.5×	34 1.2×	8 1.1×	16 2.3×	4 0.7×	9	71
Elias Grinias France	2	43 1.2×	16 0.6×	10 1.4×	7 1.0×	1 0.2×	3	67
Mozhdeh Gheini United States	2	22 0.6×	31 1.1×	5 0.7×	2 0.3×	5 0.8×	5	74
Bedirhan Uzun Türkiye	4	38 1.0×	16 0.6×	3 0.4×	13 1.9×	6 1.0×	7	58
Quinn Jones United States	3	47 1.3×	51 1.8×	3 0.4×	3 0.4×	2 0.3×	4	80
Sukmin Yun South Korea	3	35 0.9×	44 1.6×	4 0.6×	3 0.4×	4 0.7×	5	67
Weilai Xiang China	4	26 0.7×	14 0.5×	4 0.6×	11 1.6×	9 1.5×	6	49
Enyu Zhou United States	4	57 1.5×	15 0.5×	7 1.0×	14 2.0×	2 0.3×	4	76
C.G. Yang Hong Kong	4	50 1.4×	13 0.5×	6 0.9×	9 1.3×	1 0.2×	5	67
Mohammad Pezeshki Canada	2	17 0.5×	25 0.9×	5 0.7×	2 0.3×	2 0.3×	3	52

Countries citing papers authored by Peter W. Pachowicz

Since Specialization

Citations

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

Fields of papers citing papers by Peter W. Pachowicz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peter W. Pachowicz

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

All Works

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20 of 20 papers shown

Pachowicz, Peter W., et al.. (2007). Building and analyzing timed influence net models with internet-enabled pythia. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6564. 65640C–65640C. 2 indexed citations

Pachowicz, Peter W., et al.. (2004). Competitive reinforcement learning in continuous control tasks. 3. 1909–1914. 5 indexed citations

Pachowicz, Peter W., et al.. (2004). Statistically significant performance results of a mine detector and fusion algorithm from an x-band high-resolution SAR. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5415. 1084–1084.

Pachowicz, Peter W., et al.. (2004). <title>AdaptSAPS utility for adaptive ATR development and assessment</title>. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 5427. 376–384. 1 indexed citations

Baik, Sung Wook & Peter W. Pachowicz. (2003). Adaptive object recognition based on the radial basis function paradigm. 5. 3138–3142.

Pachowicz, Peter W., et al.. (2003). Iterative rule simplification for noise tolerant inductive learning. 452–453. 1 indexed citations

Pachowicz, Peter W.. (2003). Low-level numerical characteristics and inductive learning methodology in texture recognition. 91–98.

Bala, Jerzy & Peter W. Pachowicz. (2002). Application of symbolic machine learning to the recognition of texture concepts. i. 224–230.

Baik, Sung Wook & Peter W. Pachowicz. (2002). Online model modification for adaptive texture recognition in image sequences. IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans. 32(6). 625–639. 5 indexed citations

10.

Pachowicz, Peter W., et al.. (2001). Development of a robust algorithm for detection of mine targets in image data from electro-optic and acoustic sensors. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 4394. 943–943. 2 indexed citations

11.

Baik, Sung Wook & Peter W. Pachowicz. (1999). Model modification methodology for adaptive object recognition in a sequence of images. 4 indexed citations

12.

Pachowicz, Peter W. & Jerzy Bala. (1994). A Noise-Tolerant Approach to Symbolic Learning from Sensory Data. Journal of Intelligent & Fuzzy Systems. 2(4). 347–361. 2 indexed citations

13.

Pachowicz, Peter W.. (1994). Semi-autonomous evolution of object models for adaptive object recognition. IEEE Transactions on Systems Man and Cybernetics. 24(8). 1191–1207. 11 indexed citations

14.

Michalski, Ryszard S., Jerzy Bala, & Peter W. Pachowicz. (1993). GMU Research on Learning in Vision: Initial Results. George Mason University. 1 indexed citations

15.

Bala, Jerzy, Eric Bloedorn, Kenneth De Jong, et al.. (1992). A Brief Review of AQ Learning Programs and Their Application to the MONKS Problems. George Mason University. 1 indexed citations

16.

Pachowicz, Peter W. & Jerzy Bala. (1991). Improving recognition effectiveness of noisy texture concepts. International Conference on Machine Learning. 625–629.

17.

Żytkow, Jan M. & Peter W. Pachowicz. (1990). Fusion Of Vision And Touch For Spatio-Temporal Reasoning In Learning Manipulation Tasks. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 1198. 404–404. 2 indexed citations

18.

Pachowicz, Peter W.. (1989). Image processing by software parallel computation. Image and Vision Computing. 7(2). 122–128.

19.

Pachowicz, Peter W.. (1986). Time effects comparison for software computation of images. Pattern Recognition Letters. 4(1). 45–49. 2 indexed citations

20.

Pachowicz, Peter W.. (1984). Image processing and analysis of the grain constitution in minerals. Image and Vision Computing. 2(4). 204–209. 3 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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