Linearity of Feature Extraction Techniques for Medical Images by using Scale Invariant Feature Transform

In Machine Learning, Pattern Recognition and in the field of image processing, Feature Extraction starts from an initial set of the measured data. Builds derived values are intended to be informative and non redundant, facilating the subsequent learning and in some cases leading to the better human interpretations. Feature Extraction is a dimensionally reduction process, where an initial set of raw variables has been reduced to more manageable groups. Many data analysis software packages provide for feature extraction and for dimension reduction. Determining a subset of the initial features is also known as feature extraction. Common Numerical programming environments are MATLAB, SciLab, NumPy, etc. 

by Ramar S | Keerthiswaran V | Karthik Raj S S ""Linearity of Feature Extraction Techniques for Medical Images by using Scale Invariant Feature Transform""

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020,

URL: https://www.ijtsrd.com/papers/ijtsrd30358.pdf

Paper Url :https://www.ijtsrd.com/engineering/bio-mechanicaland-biomedical-engineering/30358/linearity-of-feature-extraction-techniques-for-medical-images-by-using-scale-invariant-feature-transform/ramar-s

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