Saysal Grnt leme Teknikleri Do Dr Mehmet Serdar

Sayısal Görüntü İşleme Teknikleri Doç. Dr. Mehmet Serdar Güzel Slides are mainly adapted from the following course page: http: //www. comp. dit. ie/bmacnamee and https: //www. harrisgeospatial. com/docs/Detect. Edges. html

Lecturer Instructor: Assoc. Prof Dr. Mehmet S Güzel Office hours: Tuesday, 1: 30 -2: 30 pm Open door policy – don’t hesitate to stop by! Watch the course website Assignments, lab tutorials, lecture notes • slid e 2

Image Enhancement (Spatial Filtering 2

Sharpening Spatial Filters Sharpening spatial filters seek out to highlight satisfactory detail Eliminate blurring from images Highlight edges Sharpening filters are based on spatial differentiation

1 st Derivative The formula for the 1 st derivative of a function is as follows: It’s just the difference between subsequent values and measures the rate of change of the function. In image processing basically the differences between subsequent pixels

Sobel Mask

What are edges We can also say that sudden changes of discontinuities in an image are called as edges. Significant transitions in an image are called as edges. Types of edges Generally edges are of three types: Horizontal edges Vertical Edges Diagonal Edges Why detect edges

Linear filters or smoothing filters: Prewitt Operator Sobel Operator Robinson Compass Masks Krisch Compass Masks Laplacian Operator. Above mentioned all the filters are Prewitt operator is used for detecting edges horizontally and vertically. Sobel Operator: is very similar to Prewitt operator. It is also a derivate mask and is used for edge detection. It also calculates edges in both horizontal and vertical direction.

1 st Derivative based examples

1 st Derivative based examples

2 nd Derivative The formula for the 2 nd derivative of a function is given as Considers the values both before and after the current value

2 nd Derivative Laplacian Filtering

Comprasion between 1 nd Derivative && 2 nd Derivative