Applied Computer Vision and Pattern Recognition: 2nd Volume
Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Computer vision tasks include methods for acquiring digital images (through image sensors), image processing, and image analysis to reach an understanding of digital images. In general, it deals with the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information that a computer can interpret. For interpretation, computer vision is closely related to pattern recognition.
Indeed, pattern recognition is the process of recognizing patterns by using machine learning algorithms. Pattern recognition can be defined as the identification and classification of meaningful patterns of data based on the extraction and comparison of characteristic properties or features of the data. Pattern recognition is a very important area of research and application, underpinning developments in related fields, such as computer vision, image processing, text and document analysis, and neural networks. It is closely related to machine learning and finds applications in rapidly emerging areas, such as biometrics, bioinformatics, multimedia data analysis, and, more recently, data science. Nowdays, a data-driven approach (such as deep learning) is popular to achieve the goal of pattern recognition and classification in many applications.
This Topic, on Applied Computer Vision and Pattern Recognition, invites papers on theoretical and applied issues, including, but not limited to, the following areas:
- Statistical, structural, and syntactic pattern recognition;
- Neural networks, machine learning, and deep learning;
- Computer vision, robot vision, and machine vision;
- Multimedia systems and multimedia content;
- Biosignal processing, speech processing, image processing, and video processing;
- Data mining, information retrieval, big data, and business intelligence.
This Topic will present the results of research describing recent advances in both the computer vision and pattern recognition fields.
Prof. Dr. Antonio Fernández-Caballero
Prof. Dr. Byung-Gyu Kim
- pattern recognition
- neural networks, machine learning
- deep learning, artificial intelligence
- computer vision
- data mining
- signal processing
- image processing
|Journal Name||Impact Factor||CiteScore||Launched Year||First Decision (median)||APC|
|2.7||4.5||2011||15.8 Days||CHF 2300|
|2.9||4.7||2012||15.8 Days||CHF 2200|
Machine Learning and Knowledge Extractionmake
|3.9||8.5||2019||19.2 Days||CHF 1400|
Journal of Imagingjimaging
|3.2||4.4||2015||21.9 Days||CHF 1600|
|3.9||6.8||2001||16.4 Days||CHF 2600|
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