L. Pitas

433 citations
8 papers · 312 · h-index 6

Impact in

    • Advanced Steganography and Watermarking Techniques
    • Chaos-based Image/Signal Encryption
    • Digital Media Forensic Detection
    • Face and Expression Recognition
    • Advanced Data Compression Techniques
    • Image Retrieval and Classification Techniques
    • Image and Signal Denoising Methods
    • Image and Video Stabilization

Papers in

    • Advanced Steganography and Watermarking Techniques 3
    • Chaos-based Image/Signal Encryption 3
    • Digital Media Forensic Detection 3
    • Face and Expression Recognition 1
    • Face recognition and analysis 1
    • Blind Source Separation Techniques 2

L. Pitas

8 papers receiving 288 citations

Peers

L. Pitas
Comparison fields: 5 of 39
  • Computer Vision and Pattern Recognition 289
  • Signal Processing 34
  • Media Technology 26
  • Computer Graphics and Computer-Aided Design 9
  • Computational Mathematics 1
Replace Vijay Kumar Sharma with:
Vijay Kumar Sharma India
Michiharu Niimi Japan
Amit Phadikar India
M. Yasuhara Japan
Shiaw-Shian Yu Taiwan
Lie Gu United States
Ronaldo Messina France
Shigeyuki Sakazawa Japan
Gangyi Jiang China
Narima Zermi Algeria
L. Pitas relative to Vijay Kumar Sharma India Vijay Kumar Sharma's profile →
Citations per field
00.5×1.5×2.3×
Vijay Kumar Sharma · 1×
Citations per year

Countries citing papers authored by L. Pitas

Since Specialization
Citations

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

Fields of papers citing papers by L. Pitas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 10 scholars most cited alongside L. Pitas, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with L. Pitas Line = papers co-authored together L. Pitas links everyone, so they are left out of the graph.

All Works

About L. Pitas

L. Pitas is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics, Mathematical Physics and Media Technology, having authored 8 papers that have together received 312 indexed citations. Recurring topics across this work include Advanced Steganography and Watermarking Techniques (3 papers), Chaos-based Image/Signal Encryption (3 papers), Digital Media Forensic Detection (3 papers), 3D Shape Modeling and Analysis (2 papers), Blind Source Separation Techniques (2 papers), Face and Expression Recognition (1 paper), Face recognition and analysis (1 paper) and Anatomy and Medical Technology (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (289 citations), Signal Processing (34 citations), Media Technology (26 citations), Computer Graphics and Computer-Aided Design (9 citations) and Computational Mathematics (1 citation). L. Pitas has collaborated with scholars based in Greece and United Kingdom. Frequent co-authors include V. Solachidis, Ioan Buciu, A. Nikolaidis, Anastasios Tefas, Constantine Kotropoulos, Nikos Nikolaidis, Sofia Tsekeridou, Adrian G. Borş, Zuzana Černeková and Marios A. Gavrielides. Their work appears in journals such as 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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