Morphological processing

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Transcript presentasi:

Morphological processing Dalam hal ini pemrosesan morfologi citra digital berarti mempelajari topologi atau struktur objek-objek dalam citra. Pemrosesan morfologi berkenaan dengan operasi - operasi tertentu yang ditujukan terhadap objek sehingga bentuk objek dapat dikenali.

Dulu kita memandang sebuah citra sebagai suatu fungsi intensitas terhadap posisi (x,y) Dengan pendekatan morfologi, kita memandang suatu citra sebagai himpunan Pemrosesan citra secara morfologi biasanya dilakukan terhadap citra biner (hanya terdiri dari 0 dan 1), walaupun tidak menutup kemungkinan dilakukan terhadap citra dengan skala keabuan 0-255 Untuk sementara yang akan kita pelajari adalah pemrosesan morfologi terhadap citra biner

Structuring elements and neighbourhoods A structuring element is a rectangular array of pixels containing the values either 1 or 0. This is located at the true centre pixel when both dimensions are odd (e.g. in 3x3 or 5x5 structuring elements).

If we visualize the centre pixel of the structuring element being placed directly above the pixel under consideration in the image, then the neighbourhood of that pixel is determined by those pixels which lie under neath those pixels having value 1 in the structuring element. This is illustrated inFigure 8.3

Dilasi Dilasi merupakan proses penggabungan titik-titik latar (0) menjadi bagian dari objek (1), berdasarkan structuring element S yang digunakan. Cara dilasi adalah: Untuk setiap titik pada A, lakukan hal berikut: letakkan titik poros S pada titik A tersebut beri angka 1 untuk semua titik (x,y) yang terkena / tertimpa oleh struktur S pada posisi tersebut

Contoh dilasi S = {(0,0),(0,1),(1,0)} = {poros,(+0,+1),(+1,+0)} Posisi poros ( (x,y) ∈ A ) Sxy (0,0) {(0,0),(1,0),(0,1)} (0,1) {(0,1),(1,1),(0,2)} (0,2) {(0,2),(1,2),(0,3)} ..... ...... (2,2) {(2,2),(2,3),(3,2)} Capture proses pada saat posisi poros S ada di (2,2)

Erosi Erosi merupakan proses penghapusan titik-titik objek (1) menjadi bagian dari latar (0), berdasarkan structuring element S yang digunakan. Cara erosi adalah: - Untuk setiap titik pada A, lakukan hal berikut: - letakkan titik poros S pada titik A tersebut - jika ada bagian dari S yang berada di luar A, maka titik poros dihapus / dijadikan latar.

Contoh erosi Kode S = {(0,0),(0,1),(1,0)} = {poros,(+0,+1),(+1,+0)} A E S = {(0,0),(0,1),(1,0)} = {poros,(+0,+1),(+1,+0)} A = {(0,0),(0,1),(0,2), (1,0),(1,1),(1,2), (2,0),(2,1),(2,2)} D Posisi poros ( (x,y) ∈ A ) Sxy Kode (0,0) {(0,0),(1,0),(0,1)} 1 (0,1) {(0,1),(1,1),(0,2)} (0,2) {(0,2),(1,2),(0,3)} ..... ...... (2,2) {(2,2),(2,3),(3,2)} Capture proses pada saat posisi poros S ada di (2,2). Titik (2,2) akan dihapus karena ada bagian dari S yang berada di luar A

Opening Opening adalah proses erosi yang diikuti dengan dilasi. Efek yang dihasilkan adalah menghilangnya objek-objek kecil dan kurus, memecah objek pada titik-titik yang kurus, dan secara umum men-smooth-kan batas dari objek besar tanpa mengubah area objek secara signifikan Rumusnya adalah:

Contoh Opening A ⊗ S ( ) ⊕

Contoh Opening

Closing Closing adalah proses dilasi yang diikuti dengan erosi. Efek yang dihasilkan adalah mengisi lubang kecil pada objek, menggabungkan objek-objek yang berdekatan, dan secara umum men-smooth-kan batas dari objek besar tanpa mengubah area objek secara signifikan Rumusnya adalah:

Contoh Closing A ⊕ S ( ) ⊗

Dilation, erosion and structuring elements within Matlab

Effects and uses of erosion and dilation It is apparent that erosion reduces the size of a binary object, whereas dilation increases it.

Morphological opening and closing

Opening Opening adalah proses erosi yang diikuti dengan dilasi. Efek yang dihasilkan adalah menghilangnya objek-objek kecil dan kurus, memecah objek pada titik-titik yang kurus, dan secara umum men-smooth-kan batas dari objek besar tanpa mengubah area objek secara signifikan Rumusnya adalah:

Contoh Opening A ⊗ S ( ) ⊕

Contoh Opening

Closing Closing adalah proses dilasi yang diikuti dengan erosi. Efek yang dihasilkan adalah mengisi lubang kecil pada objek, menggabungkan objek-objek yang berdekatan, dan secara umum men-smooth-kan batas dari objek besar tanpa mengubah area objek secara signifikan Rumusnya adalah:

Contoh Closing A ⊕ S ( ) ⊗

Contoh Closing

The rolling-ball analogy The bestway to illustrate the difference between morphological opening and closing is to use the ‘rolling-ball’ analogy. Let us suppose for a moment that the structuring element B is a flat (i.e. 2-D) rolling ball and the object A being opened corresponds to a simple binary object in the shape of a hexagon. Imagine the ball rolling around freely within A but constrained so as to always stay inside its boundary.

We can also use the rolling-ball analogy to define the closing of A by B. The analogy is the same, except that this time we roll structuring element B all the way around the outer boundary of object A. The resulting contour defines the boundary of the closed object A ● B.

Boundary extraction We can define the boundary of an object by first eroding the object with a suitable small structuring element and then subtracting the result from the original image. Thus, for a binary image A and a structuring element B, the boundary AP is defined as

Extracting connected components Earlier in this chapter we discussed the 4-connection and 8-connection of pixels. The set of all pixels which are connected to a given pixel is called the connected component of that pixel. Extracting connected components is a very common operation, particularly in automated inspection applications. Extracting connected components can be achieved with the Matlab Image Processing Toolbox function bwlabel.

Hit-or-Miss transform Suatu structuring element S dapat direpresentasikan dalam bentuk (S1,S2) dimana S1 adalah kumpulan titik-titik objek (hitam) dan S2 adalah kumpulan titik-titik latar (putih)

Hit-or-miss transform Contoh: S1= {b,e,h} S2={a,d,g,c,f,i} a b c d e f g h i Hit-and-misss transform A*S adalah kumpulan titik-titik dimana S1 menemukan match di A dan pada saat yang bersamaan S2 juga menemukan match di luar A.

Contoh hit-or-miss transform A*S Yang match dipertahankan Yang tidak match dihapus S

hasil

Lakukan posisioning untuk croping

hasil

Skeletonization

The skeleton of a binary object is a representation of the basic form of that object which has been reduced down to its minimal level (i.e. a ‘bare bones’ representation). The skeleton is a useful representation of the object morphology, as it provides both topological information and numerical metrics which can be used for comparison and categorization. The topology is essentially encapsulated in the number of nodes (where branches meet) and number of end points and the metric information is provided by the lengths of the branches and the angles between them.

Opening by reconstruction

In contrast to morphological opening, opening by reconstruction is a morphological transformation which enables the objects which survive an initial erosion to be exactly restored to their original shape. The method is conceptually simple and requires two images which are called the marker and the mask. The mask is the original binary image. The marker image is used as the starting point of the procedure and is, in many cases, the image obtained after an initial erosion of the original image.

Grey-scale erosion and dilation with flat structuring elements

Flat structuring elements have height values which are all zero and are thus specified entirely by their neighbourhood. When a flat structuring element is assumed, grey-scale erosion and dilation are equivalent to local minimum and maximum filters respectively.

Grey-scale opening and closing Grey-scale opening and closing are defined in exactly the same way as for binary images and their effect on images is also complementary. Grey-scale opening (erosion followed by dilation) tends to suppress small bright regions in the image whilst leaving the rest of the image relatively unchanged, whereas closing (dilation followed by erosion) tends to suppress small dark regions.

The top-hat transformation The top-hat transformation is defined as the difference between the image and the image after opening with structuring element b, namely I - I © b.

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