Fuzzy for Image ProcessingPenyusun:Tri Nurwati

(Dari berbagai sumber)

Fuzzy Image Processing • Fuzzy image processing is the collection

of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved.(From: Tizhoosh, Fuzzy Image Processing, Springer, 1997)

Struktur pengolahan citra dengan fuzzy

Proses pembuatan fuzzy pada pengolahan citra

Tidak seperti penggunakan logika fuzzy di suatu plant, untuk pengolahan citra pembuatan fuzzy melalui proses:

• coding of image data (fuzzification)• the middle step (modification of

membership values • decoding of the results

(defuzzification)

Proses pembuatan fuzzy pada pengolahan citra

• Setelah data citra ditransformasikan dari level gray ke dalam membership function (fuzzification), dalam proses ini dibutuhkan ketelitian dalam pengelompokan dan penentuan nilai membership input dan output

Kelebihan pengolahan citra dengan menggunakan logika

fuzzy• Teknik logika fuzzy sangat

mumpuni dalam pemrosesan/pengolahan dan representatif pengetahuan (rule)

• Teknik logika Fuzzy dapat mengatur keambiguan (mirip) dan hal-hal yang relatif

Kelebihan pengolahan citra dengan menggunakan logika

fuzzy• Teori set fuzzy mempunyai

kelebihan dapat mempresentasikan dan memproses pengetahuan pengguna dalam bentuk aturan “it-then”

Contoh:colour = {yellow, orange, red, violet, blue}

Contoh:warna gray: gelap, gray, dan terang

Aplikasi :• Histogram-based gray-level

fuzzification (or briefly histogram fuzzification)contoh: Perbaikan ketajaman warna image (seperti gambar panda di atas)

• Local fuzzification (contoh: deteksi tepi)

• Feature fuzzification (Scene analysis, object recognition)

Perbaikan Image dengan Fuzzy

• many researchers have applied the fuzzy set theory to develop new techniques for contrast improvement

Langkah-langkah1.1. Contrast Improvement with INT-

OperatorLangkah: a.menentukan fungsi membership

b.Mengubah nilai membership

c.Membuat skala warna gray

1.2. Contrast Improvement using Fuzzy Expected Value (Craig and Schneider 1992)

1. Step: Calculate the image histogram

2. Step: Determine the fuzzy expected value (FEV)

3. Step: Calculate the distance of gray-levels from FEV

4. Step: Generate new gray-levels

1.3. Contrast Improvement with Fuzzy Histogram

Hyperbolization (Tizhoosh 1995/1997) 1. Step: Setting the shape of membership

function (regrading to the actual image)

2. Step: Setting the value of fuzzifier Beta (a linguistic hedge)

3. Step: Calculation of membership values

4. Step: Modification of the membership values by linguistic hedge

5. Step: Generation of new gray-levels

1.4. Contrast Improvement based on Fuzzy If-Then Ruels

(Tizhoosh 1997) 1. Step: Setting the parameter of inference system

(input features, membership functions,..)2. Step: Fuzzification of the actual pixel

(memberships to the dark, gray and bright sets of pixels)

.

1.4. Contrast Improvement based on Fuzzy If-Then Ruels

(Tizhoosh 1997)3. Step: Inference (e.g. if dark then

darker, if gray then gray, if bright then brighter)

4. Step: Defuzzification of the inference result by the use of three singletons

1.5. Locally Adaptive Contrast Enhancement (Tizhoosh et al.

1997)

• In many cases, the global fuzzy techniques fail to deliver satisfactory results. Therefore, a locally adaptive implementation is necessary to achieve better results. See some examples and a comparison with calssical approach.

Deteksi Tepi• Perbaiki dengan rumus di bawah

Deteksi Tepi

Contoh Hasil Deteksi Tepi

Segmentasi Image dengan Fuzzy

Segmentasi Image dengan Fuzzy

Contoh segmentasi