Pattern Recognition and Image Analysis Pleiades Publishing Journal Impact Factor

Pattern Recognition and Image Analysis - Pleiades Publishing | 2024 Impact Factor:0.5 | Cite Score:1.6 | Q3

Pattern Recognition and Image Analysis Journal With Cite Score

Cite Score and Journal Rank of Pattern Recognition and Image Analysis

About: The Pattern Recognition and Image Analysis Journal is a peer-reviewed academic journal dedicated to the latest research and developments in pattern recognition and image analysis. It publishes high-quality articles, including original research papers, reviews, and technical notes, that focus on algorithms, techniques, and applications in pattern recognition and image analysis. The journal covers a wide range of topics, from theoretical aspects to practical applications, aimed at advancing the state of the art in these fields.
Objective:
The primary objective of the Pattern Recognition and Image Analysis Journal is to advance the field of pattern recognition and image analysis by disseminating innovative research and developments. The journal aims to provide a platform for researchers and practitioners to share their findings on new methodologies, algorithms, and applications. It seeks to foster progress in understanding and solving complex problems related to pattern recognition and image analysis, ultimately contributing to both theoretical advancements and practical implementations.
Interdisciplinary Approach:
The Pattern Recognition and Image Analysis Journal adopts an interdisciplinary approach by integrating research from various fields related to pattern recognition and image analysis. This includes areas such as computer vision, machine learning, artificial intelligence, statistical analysis, and signal processing. The journal encourages contributions that combine insights from these diverse disciplines to address complex challenges and develop novel solutions in pattern recognition and image analysis.
Impact:
The journal has a significant impact on both the academic and practical aspects of pattern recognition and image analysis. Pattern Recognition and Image Analysis Journal is widely cited by researchers, practitioners, and industry professionals who are working on advanced topics and applications in these fields. The research published in the journal influences the development of new techniques, tools, and applications, contributing to advancements in technology and the broader field of computer science.
Significance:
The Pattern Recognition and Image Analysis Journal plays a crucial role in advancing the understanding and application of pattern recognition and image analysis technologies. By providing a forum for high-quality research and development, the journal supports the growth of knowledge in these areas. Its emphasis on both theoretical and practical contributions makes it a valuable resource for researchers, practitioners, and educators who are working to push the boundaries of pattern recognition and image analysis technology and its applications in various domains.

Journal Home: Journal Homepage

Editor-in-Chief: Igor A. Sokolov

Scope: The Pattern Recognition and Image Analysis Journal focuses on research and advancements in the fields of pattern recognition and image analysis. The journal publishes high-quality articles that explore both theoretical and practical aspects of these fields. Its scope includes, but is not limited to, the following areas:
Pattern Recognition: Research on techniques and algorithms for identifying patterns and regularities in data, including classification, clustering, and feature extraction.
Image Analysis: Studies on methods for analyzing and interpreting images, including image segmentation, object detection, and image enhancement.
Computer Vision: Exploration of techniques for enabling computers to interpret and understand visual information from the world, including visual perception, 3D reconstruction, and scene understanding.
Machine Learning for Pattern Recognition: Application of machine learning algorithms and techniques to pattern recognition tasks, including supervised, unsupervised, and reinforcement learning approaches.
Deep Learning for Image Analysis: Research on deep learning methods and their application to image analysis, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and transfer learning.
Feature Extraction and Representation: Studies on methods for extracting and representing features from images and patterns, including dimensionality reduction, feature selection, and representation learning.
Image Processing: Exploration of techniques for processing images to improve their quality or extract useful information, including filtering, noise reduction, and image restoration.
Pattern Recognition in Medical Imaging: Research on applying pattern recognition techniques to medical imaging data for diagnosis, segmentation, and treatment planning.
Facial Recognition: Studies on techniques for recognizing and analyzing human faces in images, including face detection, feature extraction, and face matching.
Object Detection and Tracking: Research on methods for detecting and tracking objects within images or video sequences, including real-time applications and challenges.
Document Analysis and Recognition: Exploration of techniques for analyzing and recognizing text and structures in documents, including optical character recognition (OCR) and handwriting recognition.
Multimodal Pattern Recognition: Research on integrating information from multiple modalities (e.g., text, audio, video) for improved pattern recognition and analysis.
Pattern Recognition in Remote Sensing: Studies on applying pattern recognition techniques to remote sensing data for applications such as land cover classification, object detection, and environmental monitoring.
Security and Privacy in Pattern Recognition: Exploration of methods for ensuring security and privacy in pattern recognition systems, including encryption, secure data transmission, and privacy-preserving algorithms.