Machine Graphics and Vision

Aims & Scope

Machine GRAPHICS & VISION provides a scientific exchange forum and an authoritative source of information in the field of, in general, pictorial information exchange between computers and their environment, including applications of visual and graphical computer systems.

Until June 2013 the Journal has been maintained by the Institute of Computer Science of the Polish Academy of Sciences.

See the previous version of the web pages of the journal.

The journal concentrates on theoretical and computational models underlying computer generated, analysed, or otherwise processed imagery, in particular: • image processing • scene analysis, modeling, and understanding • machine vision • pattern matching and pattern recognition • image synthesis, including three-dimensional imaging and solid modeling • computer-aided graphic arts and animation • mathematical approaches to image processing, analysis, and synthesis • computational geometry • image models and transforms • visualization and graphical data presentation • diagrammatic knowledge representation and reasoning • monocular and stereo vision • modeling of human visual perception • innovative uses of various graphic and vision devices and systems.

The journal publishes: -original research papers- tutorial papers- state-of-the-art surveys- reports on work in progress- research direction proposals

View Aims & Scope

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Metrics & Ranking

Journal Rank

Year	Value
2024	26944

Journal Citation Indicator

Year	Value
2024	20

SJR (SCImago Journal Rank)

Year	Value
2024	0.124

Quartile

Year	Value
2024	Q4

h-index

Year	Value
2024	14

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A Curvature-Tensor-Based Perceptual Quality Metric for 3D Triangular Meshes

  Citation: 18

  Authors: Fakhri, Jean-Marc, Kai

• Data augmentation techniques for transfer learning improvement in drill wear classification using convolutional neural network

  Citation: 15

  Authors: Jarosław, Joanna, Izabella, Jarosław, Albina, Michał, Arkadiusz, Jakub, Bartosz, Grzegorz

• Classifiers ensemble of transfer learning for improved drill wear classification using convolutional neural network

  Citation: 13

  Authors: Jarosław, Joanna, Izabella, Jarosław, Albina, Michał, Arkadiusz, Jakub, Bartosz, Grzegorz

• Identifying selected diseases of leaves using deep learning and transfer learning models

  Citation: 11

  Authors: Afsana, Sayeda Fatema Tuj, Muhammad, Riddho Ridwanul, Omar, Taskeed, Md Sawkat

• An attention-based deep network for plant disease classification

  Citation: 10

  Authors: Asish, Debotosh, Ondrej

• Offline handwritten pre-segmented character recognition of Gurmukhi script

  Citation: 8

  Authors: Munish, Manish, Simpel, Rajendra

• Lung and colon cancer detection from CT images using Deep Learning

  Citation: 7

  Authors: Joseph D., Akinkunle A., Olajumoke O., Taiwo O., Emmanuel J.

• Extraction of image parking spaces in intelligent video surveillance systems

  Citation: 7

  Authors: Rykhard, Sergey, Tatiana, Pavel

• Hough transform for lines with slope defined by a pair of co-primes

  Citation: 6

  Authors: Leszek, Arkadiusz

• A Survey of Passive 3D Reconstruction Methods on the Basis of More than One Image

  Citation: 6

  Authors: Mariusz, Przemyslaw

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