Whence the fuzziness? Morphological e ects in interacting ...
Image Thresholding berdasarkan Index of fuzziness dan ...
Transcript of Image Thresholding berdasarkan Index of fuzziness dan ...
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IMAGE THRESHOLDING BERDASARKAN INDEX OF FUZZINESS
DAN FUZZY SIMILARITY MEASURE
GULPI QORIK OKTAGALU PRATAMASUNU
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Latar Belakang
Segmentasi citra itu penting Aplikasi pemrosesan citra.
Metode segmentasi citra Sederhana
mudah diimplementasikan
thresholding
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Latar Belakang Threshold dipilih untuk mengklasifikasi
pixels berdasarkan nilainya dari histogram
Threshold yang paling optimal memisahkan objek dan background dengan tepat.
Akurasi segmentasi proses penentuan threshold -> histogram citra.
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Latar Belakang
Histogram citra ideal lembah yang memisahkan
dua puncak
memudahkan proses thresholding
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Latar Belakang
Histogram yang tidak ideal lembah tidak jelas, multi
modal
Karena multi-object, derau, pencahayaan tidak seragam, ambiguitas gray level
thresholding sulit
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Latar Belakang
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Meminimalkan measure of fuzziness dengan fungsi kriteria
Menggunakan similaritas antar gray level berdasarkan fuzzy measure
Huang & Wang
1995
Tobias & Seara
2002
Global measure, hasil tidak selalu optimal
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Latar Belakang
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Meminimalkan measure of fuzziness dengan fungsi kriteria
Menggunakan similaritas antar gray level berdasarkan fuzzy measure
Huang & Wang
1995
Tobias & Seara
2002
Global measure, hasil tidak selalu optimal
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Latar Belakang
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Meminimalkan measure of fuzziness dengan fungsi kriteria
Menggunakan similaritas antar gray level berdasarkan fuzzy measure
Fungsi minimal piksel untuk menentukan seed subsets
Huang & Wang
1995
Tobias & Seara
2002
Lopes, dkk
2010
Penentuan manual, menambah subjektivitas hasil
Global measure, hasil tidak selalu optimal
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Latar Belakang
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Meminimalkan measure of fuzziness dengan fungsi kriteria
Menggunakan similaritas antar gray level berdasarkan fuzzy measure
Fungsi minimal piksel untuk menentukan fuzzy region
Huang & Wang
1995
Tobias & Seara
2002
Lopes, dkk
2010
Penentuan manual, menambah subjektivitas hasil
Global measure, hasil tidak selalu optimal
ππππππ₯β§π πππ(ππ πππ)= π1 π(π₯π)
127(255)
π=0(128)
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Latar Belakang
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Meminimalkan measure of fuzziness dengan fungsi kriteria
Menggunakan similaritas antar gray level berdasarkan fuzzy measure
Fungsi minimal piksel untuk menentukan fuzzy region
Huang & Wang
1995
Tobias & Seara
2002
Lopes, dkk
2010
Penentuan manual, menambah subjektivitas hasil
30 citra belum mewakili seluruh jenis citra
Global measure, hasil tidak selalu optimal
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Latar Belakang
Oleh karena itu,
Dibutuhkan metode image thresholding berdasarkan pengukuran similaritas antar gray
level berbasis fuzzy dengan penentuan fuzzy region otomatis untuk menghindari terjadinya
lokal minimum.
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Tujuan Penelitian
Mengusulkan metode image thresholding
berdasarkan index of fuzziness untuk menentukan
fuzzy region dengan fuzzy similarity measure sebagai penentu nilai threshold yang dihitung dari
fuzzy region.
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Rumusan Masalah
Bagaimana cara menentukan fuzzy region dari suatu citra berdasarkan index of fuzziness?
Bagaimana cara menentukan threshold yang optimal pada fuzzy region berdasarkan fuzzy similarity
measure?
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Batasan Masalah
Citra yang akan diuji adalah citra abu-abu yang terdiri dari berbagai objek real scene.
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Kontribusi Penelitian
Metode image thresholding dengan penentuan fuzzy region yang efektif dan otomatis berdasarkan index of fuzziness
dengan penentuan threshold berdasarkan fuzzy similarity measure.
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Metode yang diusulkan
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Looping titik ππͺ untuk setiap gray level
Hitung Index of Fuzziness
(πΈπ»)
Inisialisasi fuzzy region berdasarkan posisi πΈπ» terbesar
Hitung ππ© dan ππΎ
Histogram Citra
Hitung Fuzzy Similarity Measure
Thresholding Citra
Inisialisasi Fuzzy Region
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Pencarian Fuzzy Region (Looping) β’ Looping nilai π£β¨
β’ dari (ππππ+1) hingga (πΏ β 1) β’ ππππ = gray level
minimal
β’ πΏ = gray level maximal
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ππͺ
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Untuk setiap πβ¨ (Looping) β’ Update fungsi keanggotaan πβ§ dan ππ, dimana g
adalah gray level
πβ§ π = π(π; 0, π£β¨/2, π£β¨)
ππ π = π π; π£β¨ , (π£β¨+πΏ)/2, πΏ
β’ π π₯; π, π, π =
0, π₯β€π
2π₯βπ
πβπ
2, πβ€π₯β€π
1β 2π₯βπ
πβπ
2, πβ€π₯β€π
1, π₯β₯π
π π₯; π, π, π = 1 β π π₯; π, π, π .
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ππ© ππΎ ππͺ
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Untuk setiap πβ¨ (Looping) β’ Hitung Index of Fuzziness
πΎπ = πΎβ§ β πΎπ
πΎβ§ =2
π π π Γ πβ§ π
πΏ
π=0
πΎπ =2
π π π Γ ππ π
πΏ
π=0
β’ Dimana
β’ N=jumlah pixels dalam citra
β’ h(g)=intensitas hist pada g gray level
β’ L= gray level maksimal
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πΎπ πΎπ πΎπ πΎπ πΎπ
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Pembagian Fuzzy Region β’ π£β¨=argmax(πΎπ)
β’ Hitung nilai π£β§ dan π£π
π£β§ = π β π ππ£ππ=0
π ππ£ππ=0
π£π = π β π ππΏβ1π=π£π+1
π ππΏβ1π=π£π+1
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ππͺ ππͺ ππ© ππͺ ππΎ
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Inisialisasi Fuzzy Region β’ Inisialisasi awal fuzzy subset πΆβ§
dan πΆπ, π₯π, dan π₯π berdasarkan π£π dan π£π€
ππ = ππ© + π ππ = ππΎ β π
π = *ππ, ππ+π, β¦ , ππ+
ππ: πΏ = *ππ, ππ, β¦ , πππ+ πͺπ© = (ππ, ππͺπ© ππ )
ππ: πΏ = *πππ , πππ+π, β¦ ,ππ³+
πͺπΎ = (ππ, ππͺπΎ ππ )
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ππΎ ππ©
ππ ππ
Fuzzy region (F) πͺπ© πͺπΎ
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Fuzzy Similarity Measure β’ Inisialisasi fungsi keanggotaan
tiap fuzzy set untuk perhitungan Fuzzy Similarity Measure, dimana g adalah gray level
β’ πβ§ π = π π; π£β§ , π£β¨ , π£π
β’ ππ π = π π; π£β§ , π£β¨ , π£π
22 Fuzzy region πͺπ© πͺπΎ
ππͺ
ππ© ππΎ
ππͺ ππ© ππͺ ππΎ
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Fuzzy Similarity Measure β’ Looping j = (π₯π: π₯π)
β’ Hitung Fuzzy Similarity Measure π ππ terhadap πΆβ§
π πΆβ§ βͺ ππ , π π πΉ
β’ Hitung Fuzzy Similarity Measure π ππ terhadap πΆπ
π πΆπ βͺ ππ , π π πΉ
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π πΆβ§ βͺ ππ π πΆπ βͺ ππ
Fuzzy region (F)
πͺπ© πͺπΎ ππ β π
ππ ππ
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Fuzzy Similarity Measure π
π(π¨) = (π§ β π(π΄)) ππ§=1
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πβ¦(π§)ππ§=1
π΄(π¨) = πβ¦ π§ Γ ππ¨ π Γ π§ Γ |(ππ¨ π β ππ¨β π |
π
π§=1
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Thresholding β’ Hitung
ππ‘ππ‘ππ = π πΆπ βͺ ππ β Ξ± β π πΆβ§ βͺ ππ ,
dimana
πΌ =π πΆππ πΆβ§
T = arg max ππ‘ππ‘ππ
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T
Fuzzy region πͺπ© πͺπΎ
π πΆβ§ βͺ ππ
π πΆπ βͺ ππ
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Pembentukan Histogram
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Index of fuzziness
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Inisialisasi Fuzzy Region
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Fuzzy Similarity Measure
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Thresholding
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Uji Coba
Data Uji Sintetis
Data Uji Natural
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Data Uji Sintetis
Data sintetis yang dibuat manual oleh peneliti Multi objek
Tingkat derau bervariasi
Tipe derau gaussian
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Data Uji Natural
75 citra dari dataset Wiezmann dan Tizhoosh Untuk memudahkan
komparasi
Ground truth dengan Gold Standard
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Evaluasi
Misclassification Error (ME) Membandingkan performa thresholding metode yang diusulkan pada objek dunia nyata (Data uji standar, Tizhoosh)
ππΈ = 1 βπ΅π β© π΅π + |πΉπ β© πΉπ|
|π΅π| + |πΉπ|
π΅π dan πΉπ adalah background dan objek dari citra ground truth, π΅π dan πΉπ adalah background dan objek dari citra tersegmentasi
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Hasil Uji Citra Sintetis
Citra Metode Otsu Metode yang diusulkan
π(0, 0) 0,00 0,00
π(0.01, 0.01) 0,11 0,01
π(0.05, 0.05) 7,24 4,41
π(0.1, 0.1) 17,03 11,51
π(0.15, 0.15) 23,54 15,09
Rata-rata 9,58 6,21
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Metode Otsu Metode yang diusulkan
Citra asal g(0.01, 0.01)
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Metode Otsu Metode yang diusulkan
Citra asal g(0.1, 0.1)
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Hasil Uji Citra Natural
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Citra Sample
Metode Thresholding
Metode Otsu
(%)
Metode yang diusulkan
(%)
Sample 1 10,07 2,43
Sample 2 32,00 1,00
Sample 3 26,38 1,56
Sample 4 40,95 2,11
Sample 5 46,22 1,84
Sample 6 39,54 30,63
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Metode Otsu Metode Lopes Metode yang diusulkan
Citra asli Ground truth
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Hasil Uji dataset Tizhoosh
Citra Sample
Metode Thresholding
Metode Otsu
(%) Metode yang
diusulkan (%) Metode Lopes 1
(%) Metode Lopes 2
(%)
Blocks 94.36 99.34 98.87 99.34
Gearwheel 98.23 99.48 95.59 95.59
Potatoes 98.28 99.81 96.98 96.98
Rice 95.73 92.75 82.06 95.91
Shadow 89.66 92.96 93.26 93.26
Stones 96.86 98.14 97.05 97.05
Zimba 97.61 99.51 96.55 98.86
Rata-rata 95.82 97.43 94.34 96.71
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Hasil Uji Citra Natural
Nilai rata-rata ME dari 75 citra natural
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Metode Thresholding Misclassification Error (%)
Metode Otsu 10,29
Metode yang diusulkan 6,85
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Index of fuzziness terbesar sebagai penentu fuzzy region
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Perbandingan FSM dengan Variance dan IoF
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Index of fuzziness
Variance Fuzzy similarity
measure
Posisi Threshold
Thresholded Image
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Kelemahan Metode
β’ Hasil yang kurang baik terjadi pada citra dengan histogram unimodal
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Metode Otsu Metode yang diusulkan
Citra asal
Ground truth
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Citra asal
Ground truth Metode Otsu Metode yang diusulkan
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Kesimpulan Image thresholding berdasarkan index of fuzziness dan fuzzy similarity measure berhasil digunakan untuk melakukan thresholding terhadap
citra dengan berbagai macam jenis histogram dengan performa berdasarkan nilai ME 6,85%
Posisi fuzzy region yang efektif terletak pada daerah di sekitar gray level dengan nilai Index of fuzziness terbesar
Fuzzy similarity measure lebih efektif digunakan untuk menentukan threshold yang optimal karena memperhitungkan intensitas gray
level dan fungsi keanggotaan fuzzy
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Saran
Perlu adanya penelitian lebih lanjut tentang pengembangan fuzzy similarity measure yang memperhitungkan jarak threshold dari pusat
fuzzy region.
Perlu dilakukan penelitian lebih lanjut mengenai penentuan fuzzy region berdasarkan pada teori fuzzy sets type II.
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Terima Kasih
GULPI QORIK OKTAGALU PRATAMASUNU
5113201021
Dosen Pembimbing
Dr. Agus Zainal Arifin, S.Kom, M.Kom
Anny Yuniarti, S.Kom, M.Comp.Sc
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