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Feature (computer vision) - Wikipedia
In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in the image such as points, edges or objects. Features may also be the result of a general neighborhood operation or feature detection applied to the image. Other examples of fe
Feature engineering - Wikipedia
This article needs to be updated. Please help update this article to reflect recent events or newly available information. (February 2024) Feature engineering, a preprocessing step in supervised machine learning and statistical modeling,[1] transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant informat
Histogram - Wikipedia
For the histogram used in digital image processing, see Image histogram and Color histogram. A histogram is a visual representation of the distribution of quantitative data. The term was first introduced by Karl Pearson.[1] To construct a histogram, the first step is to "bin" (or "bucket") the range of values— divide the entire range of values into a series of intervals—and then count how many val
Scale-invariant feature transform - Wikipedia
The detection and description of local image features can help in object recognition. The SIFT features are local and based on the appearance of the object at particular interest points, and are invariant to image scale and rotation. They are also robust to changes in illumination, noise, and minor changes in viewpoint. In addition to these properties, they are highly distinctive, relatively easy
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Pattern recognition - Wikipedia
