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en:processing:градиент [2012/06/21 11:27] Анна Протопопова created |
en:processing:градиент [2012/06/21 11:27] Анна Протопопова |
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| {{:processing:lena_1.jpg|}} {{:processing:lena_2.jpg|}} | {{:processing:lena_1.jpg|}} {{:processing:lena_2.jpg|}} | ||
| - | In general case directional derivative along //X// axis (or //Y// axis) for discrete function can be expressed as {{:processing:grad_1.jpg|}} (or {{:processing:grad_2.jpg|}}), where {{:processing:grad_3.jpg|}} refers to the resultant of discrete functions {{:processing:grad_4.jpg|}} and {{:processing:grad_5.jpg|}} and differentiable function {{:processing:grad_6.jpg|}} (image). There are various calculation algorithms for derivative of two-dimensional discrete function. FemtoScan uses three algorithms: | + | In general case directional derivative along //X// axis (or //Y// axis) for discrete function can be expressed as {{:processing:grad_1.jpg|}} (or {{:processing:grad_2.jpg|}}), where {{:processing:grad_3.jpg|}} refers to the resultant of discrete functions {{:processing:grad_4.jpg|}} and {{:processing:grad_5.jpg|}} and differentiable function {{:processing:grad_6.jpg|}} (image). There are various calculation algorithms for derivative of two-dimensional discrete function. Femtoscan uses three algorithms: |
| * Difference algorithm. In this algorithm the //X// (or //Y//) derivative at a point is determined as the difference between values in neighboring points in line (or in column). | * Difference algorithm. In this algorithm the //X// (or //Y//) derivative at a point is determined as the difference between values in neighboring points in line (or in column). | ||
| * [[http://en.wikipedia.org/wiki/Prewitt_operator|Prewitt algorithm]]. This algorithm was created by Dr. Judith Prewitt for the most efficient edge detecting in medical images. The derivative at a point is one third from sum of differences between values in neighboring points in the same line, one line higher and one line lower (or in three columns – column with point where derivative is calculated, column to the left and column to the right). | * [[http://en.wikipedia.org/wiki/Prewitt_operator|Prewitt algorithm]]. This algorithm was created by Dr. Judith Prewitt for the most efficient edge detecting in medical images. The derivative at a point is one third from sum of differences between values in neighboring points in the same line, one line higher and one line lower (or in three columns – column with point where derivative is calculated, column to the left and column to the right). | ||