Fuzzy for Image Processing

Presentasi berjudul: "Fuzzy for Image Processing"— Transcript presentasi:

1 Fuzzy for Image Processing
fuzzy logic Fuzzy for Image Processing Penyusun: Tri Nurwati (Dari berbagai sumber)

2 Fuzzy Image Processing
fuzzy logic 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)

3 Struktur pengolahan citra dengan fuzzy
fuzzy logic Struktur pengolahan citra dengan fuzzy

4 Proses pembuatan fuzzy pada pengolahan citra
fuzzy logic 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)

5 Proses pembuatan fuzzy pada pengolahan citra
fuzzy logic 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

6 fuzzy logic

7 Kelebihan pengolahan citra dengan menggunakan logika fuzzy
fuzzy logic 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

8 Kelebihan pengolahan citra dengan menggunakan logika fuzzy
fuzzy logic Kelebihan pengolahan citra dengan menggunakan logika fuzzy Teori set fuzzy mempunyai kelebihan dapat mempresentasikan dan memproses pengetahuan pengguna dalam bentuk aturan “it-then”

9 fuzzy logic

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

11 Contoh: warna gray: gelap, gray, dan terang
fuzzy logic Contoh: warna gray: gelap, gray, dan terang

12 fuzzy logic 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)

13 Perbaikan Image dengan Fuzzy
fuzzy logic Perbaikan Image dengan Fuzzy many researchers have applied the fuzzy set theory to develop new techniques for contrast improvement

14 Langkah-langkah fuzzy logic
1.1. Contrast Improvement with INT- Operator Langkah: a.menentukan fungsi membership b.Mengubah nilai membership c.Membuat skala warna gray

15 fuzzy logic 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

16 fuzzy logic 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

17 1.4. Contrast Improvement based on Fuzzy If-Then Ruels (Tizhoosh 1997)
fuzzy logic 1.4. Contrast Improvement based on Fuzzy If-Then Ruels (Tizhoosh 1997) Step: Setting the parameter of inference system (input features, membership functions,..) Step: Fuzzification of the actual pixel (memberships to the dark, gray and bright sets of pixels) .

18 1.4. Contrast Improvement based on Fuzzy If-Then Ruels (Tizhoosh 1997)
fuzzy logic 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

19 1.5. Locally Adaptive Contrast Enhancement (Tizhoosh et al. 1997)
fuzzy logic 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.

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21 fuzzy logic

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24 fuzzy logic Deteksi Tepi Perbaiki dengan rumus di bawah

25 fuzzy logic Deteksi Tepi

26 Contoh Hasil Deteksi Tepi
fuzzy logic Contoh Hasil Deteksi Tepi

27 Segmentasi Image dengan Fuzzy
fuzzy logic Segmentasi Image dengan Fuzzy

28 Segmentasi Image dengan Fuzzy
fuzzy logic Segmentasi Image dengan Fuzzy

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30 fuzzy logic Contoh segmentasi