Seminar hasil penelitian ekstraksi fitur bentuk dan venasi citra daun dengan pemodelan fourier dan b-spline



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SEMINAR HASIL PENELITIAN EKSTRAKSI FITUR BENTUK DAN VENASI CITRA DAUN DENGAN PEMODELAN FOURIER DAN B-SPLINE

Rahmadhani M

G64050646

Pembimbing:

Yeni Herdiyeni, S.Si., M.Kom.

Ir. Iwan Hilwan, MS.

Latar Belakang



Latar Belakang

  • Kemampuan mengidentifikasi daun menjadi kebutuhan besar bagi taksonomis (Hickey et al. 1999)

  • Karakter turunan bentuk dan venasi efektif sebagai pembeda (Rasnovi 2001)

  • isu jenis informasi kuantitatif



Contoh Karakter Daun



Penelitian Terkait



Tujuan



Ruang Lingkup

Fokus:

citra daun tunggal dari beberapa famili dikotiledon berhabitus pohon buah di kampus IPB Dramaga

Metodologi Penelitian



Data Penelitian

  • Citra hasil pemotretan sebelas jenis daun di IPB Darmaga

  • 1100 citra daun tunggal

  • Format JPG, 200 x 150 piksel



Data Penelitian (2)



Ekstraksi Fitur Bentuk



Pengukuran Tingkat Kemiripan



Ekstraksi Venasi



Pencarian Struktur



Evaluasi

Menggunakan sistem:

evaluasi recall-precision hasil temu kembali
  • Evaluasi Ekstraksi Venasi

Evaluasi visual:

penilaian terhadap hasil ekstraksi venasi skala 0-2 (sangat tidak sesuai s.d sangat sesuai)

Evaluasi Hasil Temu Kembali





Hasil Ekstraksi Bentuk



Hasil Ekstraksi Bentuk



Recall-Precision



Hasil Ekstraksi Venasi



Tabulasi Total Nilai Kesesuaian



Why too low accuracy?



Kesimpulan

  • Ekstraksi bentuk daun dengan pemodelan Fourier berhasil diimplementasikan

  • Fourier lebih efektif sebagai penciri bentuk daun daripada HT

  • Ekstraksi venasi pada citra daun dengan pemodelan b-spline berhasil diimplementasikan

Plus: automasi inisialisasi parameter pencarian awal

Saran

  • ekstraksi bentuk dengan pendekatan kurva yang lain seperti pemodelanBezier

  • ekstraksi venasi pada citra daun yang lebih baik

  • Penelitian ukuran kesamaan antara satu set b-spline dengan satu set b-spline lainnya

menjadikan venasi dengan pemodelan b-spline sebagai penciri, bersama dengan penciri bentuk, untuk pengenalan jenis daun secara automatis.



Tinjauan Pustaka



Fourier Descriptors

  • Fourier descriptors describe the shape in terms of its spatial frequency content.

  • Begin by representing the boundary of the shape as a periodic function (in one of a few possible ways).

  • Expand this in a Fourier series; obtain a set of coefficients that capture shape information.



Fourier Descriptors (cont.)

  • imagining that the image plane is complex



Fourier Descriptors (cont.)

  • • The truncated series is

  • The period is made equal to 2π by choosing the speed at which we circumnavigate the shape properly



Fourier Descriptors (cont.)

  • The k-th Fourier transform coefficient is calculated as

  • Shape feature must be invariant to rotation, translation, and dilatation effect. For satisfying these, some adjustments on Fourier descriptors should be done



Fourier Descriptors (cont.)



B-Spline

  • Polynomial curves

  • Ck-1 continuity

  • Cubic B-spline:

C2 continuity

B-Spline (cont.)

Knots
  • • A sequence of scalar values t1, …, t2k with ti≠tj if i≠j, and ti< tj for i

  • • If ti chosen at uniform interval (such as 1,2,3, …), than it is a uniform knot sequence



B-Spline (cont.)

Control points
  • We can define a unique k degree polynomial F(t) with blossom f, such that

Vi = f(ti+1,ti+2, …,ti+k)
  • The sequence of vi for i [0,k] are the control points of a B-spline

  • Evaluation of a point on a curve with f(t,t,t)

  • Remark: no control points will lie on the curve!



B-Spline (cont.)

  • Given a sequence of knots, t1,…t2k,

  • For each interval [ti, ti+1], there’s a kth degree parametric curve F(t) defined with corresponding B-spline control points vi-k,vi-k+1, … , vi

  • If f() is the k-parameter blossom associated to the curve, then



B-Spline (cont.)

  • The control point are defined by vi =f(tj+1, …, tj+k), j=i-k, i-k, …, I

  • The k-th degree Bézier curve corresponding to this curve has the control points:

pj =f(ti, ti, …, ti,ti+1,ti+1,…, ti+1),

where j = 0, 1, …, k
  • The evaluation of the point on the curve at

t ∈[ti ,ti+1] is given by F(t) = f(t,t,…,t)

B-Splines Blending Functions



B-Splines Blending Functions Cox-deBoors Recurrence



Standard hough transform

  • Line equation: y=mx+c presenting in parameter space (c,m)

  • But this is wrong for vertical lines.

  • So polar co-ordinates are used as parameter space, i.e. (r,Θ) instead of (c,m).



Standard hough transform (cont.)



Standard hough transform (cont.)

  • Advantages of Parameterization

  • How to find intersection of the parametric curves

    • Use of accumulator arrays
    • concept of ‘Voting’
    • To reduce the computational load use Gradient information


SHT Algorithm

  • Quantize the Hough Transform space: identify the maximum and minimum values of r and q

  • Generate an accumulator array A(r, q); set all values to zero

  • For all edge points (xi, yi) in the image

  • For all cells in A(r, q)

    • Search for the maximum value of A(r, q)
    • Calculate the equation of the line
  • To reduce the effect of noise more than one element (elements in a neighborhood) in the accumulator array are increased



SHT Algorithm(cont.)




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