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Prior to the 19th century most patella fractures were treated non-surgically with extension splinting, frequently resulting in poorly joined fragments of bone and long-term pain and disability. [6] Incomplete understanding of the importance of the patella led to the trend of removing the whole patella, also resulting in pain, disability and ...
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 fracture pattern of the condyles is variable and all types of fractures can occur. This is a high energy injury with a complex mechanism that includes varus and valgus forces. Up to 33% of these fractures may be open, often with extensive soft tissue injuries and risk of compartment syndrome. Represents 20% of all tibial plateau fractures.
The Müller AO Classification of fractures is a system for classifying bone fractures initially published in 1987 [1] by the AO Foundation as a method of categorizing injuries according to therognosis of the patient's anatomical and functional outcome. "AO" is an initialism for the German "Arbeitsgemeinschaft für Osteosynthesefragen", the ...
Bipartite patella is a condition where the patella, or kneecap, is composed of two separate bones. Instead of fusing together as normally occurs in early childhood, the bones of the patella remain separated. [1] The condition occurs in approximately 1–2% of the population [2] [3] and is no more likely to occur in males than females.
Pattern Recognition is a single blind peer-reviewed academic journal published by Elsevier Science. It was first published in 1968 by Pergamon Press . The founding editor-in-chief was Robert Ledley , who was succeeded from 2009 until 2016 by Ching Suen of Concordia University .
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.
This is an important technique for all types of time series analysis, especially for seasonal adjustment. [2] It seeks to construct, from an observed time series, a number of component series (that could be used to reconstruct the original by additions or multiplications) where each of these has a certain characteristic or type of behavior.