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The journal publishes papers in the general area of pattern recognition, including applications in the areas of image processing, computer vision, handwriting recognition, biometrics and biomedical signal processing. The journal awards the Pattern Recognition Society Medal to the best paper published in the journal each year.
The journal covers research in computer vision and image understanding, pattern analysis and recognition, machine intelligence, machine learning, search techniques, document and handwriting analysis, medical image analysis, video and image sequence analysis, content-based retrieval of image and video, and face and gesture recognition.
The International Conference on Pattern Recognition Applications and Methods (ICPRAM) is held annually since 2012. From the beginning it is held in conjunction with two other conferences: ICAART - International Conference on Agents and Artificial Intelligence and ICORES - International Conference on Operations Research and Enterprise Systems.
[9] [10] The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. [11] [12] Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.
The conference generally has less than 30% acceptance rates for all papers and less than 5% for oral presentations. [3] [4] [5] It is managed by a rotating group of volunteers who are chosen in a public election at the Pattern Analysis and Machine Intelligence-Technical Community (PAMI-TC) meeting four years before the meeting. [6]
In psychology and cognitive neuroscience, pattern recognition is a cognitive process that matches information from a stimulus with information retrieved from memory. [1]Pattern recognition occurs when information from the environment is received and entered into short-term memory, causing automatic activation of a specific content of long-term memory.
For example, large datasets based on data extracted from news reports can be built to facilitate social networks analysis or counter-intelligence. In effect, the text mining software may act in a capacity similar to an intelligence analyst or research librarian, albeit with a more limited scope of analysis.
A very common type of prior knowledge in pattern recognition is the invariance of the class (or the output of the classifier) to a transformation of the input pattern. This type of knowledge is referred to as transformation-invariance. The mostly used transformations used in image recognition are: translation; rotation; skewing; scaling.