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Bahan UTS Citra

Bahan UTS Citra. Nopember 2011. Sampling & Kuantisasi. Sampling adalah : Transformasi citra kontinue ke citra digital disebut digitasi Banyaknya pixel ( blok ) untuk mendefinisikan suatu gambar

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Bahan UTS Citra

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  1. Bahan UTS Citra Nopember 2011

  2. Sampling & Kuantisasi • Sampling adalah : • Transformasicitrakontinuekecitra digital disebutdigitasi • Banyaknya pixel (blok) untukmendefinisikansuatugambar • Contoh : Digitasicitradenganjumlahbaris 256 danjumlahkolom 256 - resolusispasial 256 x 256. • Kuantisasiadalah : • Halus / kasarnyapembagiantingkatkecemerlangan/intensitas • Transformasi data analog yang bersifatkontinuekedaerahintensitasdiskrit • Banyaknyaderajatnilaipadasetiap pixel (menunjukkanjumlah bit padagambar digital) • Contoh : • Citra denganintensitaspikselberkisarantara 0 dan 255 - resolusikecemerlangancitraadalah 256 • Citra b/w jumlah bit = 2, citra grayscale dengan 8 bit, true color dengan 24 bit

  3. Ilustrasi Sampling dan Kuantisasi Hasil Sampling dan Kuantisasi Citra Citra Kontinyu

  4. Ilustrasi Sampling dan Kuantisasi

  5. Kuantisasi Gray • Rumuskuantisasi gray level X=int(x*(2n -1) / 255) • Perbedaankuantisasi gray level 32 dankuantisasi gray level 64 adalah : • Berapanilaikuantisasi 32, dan 64 dari gray level 100 ? • kuantisasi gray level 32: nilai gray level 0-255 akandikuantisasikenilai gray 0-31 (25-1) • X=int(100*31/255)= 12 • kuantisasi gray level 64 : nilai gray level 0-255 akandikuantisasikenilai gray 0-63 (26-1) • X=int(100*63/255)=25

  6. The Histogram of a Grayscale Image • Let I be a 1-band (grayscale) image. • I(r,c) is an 8-bit integer between 0 and 255. • Histogram, hI, of I: • a 256-element array, hI • hI (g), for g = 1, 2, 3, …, 256, is an integer • hI (g) = number of pixels in I that have value g-1.

  7. black marks pixels with intensity g lower RHC: numberof pixels with intensity g 16-level (4-bit) image The Histogram of a Grayscale Image

  8. The Histogram of a Grayscale Image Black marks pixels with intensity g Plot of histogram: number of pixels with intensity g

  9. The Histogram of a Grayscale Image Black marks pixels with intensity g Plot of histogram: number of pixels with intensity g

  10. Brightness & Kontras Dark image : gambar dengan histogram warna terpusat pada nilai gray level rendah Bright image : gambar dengan histogram warna terpusat pada nilai gray level tinggi Low contrass image : gambar dengan histogram warna sempit dan terpusat pada nilai gray level tengah High contrass image : gambar dengan histogram warna Pada range gray scale dan distribusi piksel mendekati uniform, dan tidak banyak intensitas piksel dg jumlah yg sangat berbeda dg intensitas piksel lainnya

  11. 255 127 0 0 127 255 transform mapping Point Processes: Increase Brightness

  12. 255 127 0 0 127 255 Point Processes: Decrease Brightness 255-g transform mapping

  13. 255 127 0 0 127 255 Point Processes: Increase Contrast transform mapping

  14. 255 127 0 0 127 255 Point Processes: Decrease Contrast transform mapping

  15. 255 127 0 0 127 255 Point Processes: Contrast Stretch MJ mJ mI MI transform mapping

  16. Histogram Equalisasi Citra awal: 5 2 7 7 3 2 6 6 3 4 6 6 3 4 9 6 Citra akhir: 5 1 8 8 3 1 7 7 3 4 7 7 3 4 9 7

  17. Luminosity before after Point Processes: Histogram Equalization

  18. Histogram EQ The CDF (cummulative distribution) is the LUT for remapping. pdf CDF

  19. Histogram EQ The CDF (cummulative distribution) is the LUT for remapping. pdf LUT

  20. Histogram EQ The CDF (cummulative distribution) is the LUT for remapping. pdf LUT

  21. Histogram EQ

  22. Point Processes: Histogram Equalization Task: remap image I with min = mI and max = MI so that its histogram is as close to constant as possible and has min = mJ and max = MJ . be the cumulative (probability) distribution function of I. Then J has, as closely as possible, the correct histogram if Using intensity extrema

  23. Filtering Domain Spatial • Konvolusi : Diketahui citra asal, filter mask H Dapatkan citra hasil konvolusi • Penjelasan Low Pass Filter dan High Pass Filter

  24. Transformasi Gray Scale • Tiga fungsi trasformasi gray scale : linier, power law, dan log

  25. Transformasi Gray Scale • Log Trasformasi • Transformasi log, akan membuat • input gray level menuju ke • intensitas yg lebih tinggi • (warna putih), contoh r>1/2L-1 • nilai s = > 3/4L • Nilai gray level input (dark area) • pada range yg sempit menjadi gray • level output (bright area) • Digunakan pada saat daerah dark image • akan diperluas menjadi daerah bright image

  26. Transformasi Gray Scale • Power Law Trasformasi • Kurva power law bernilai < 1 akan membuat • Range citra input gelap yg sempit menjadi • range output citra terang yang lebar • Kurva power law bernilai > 1 akan membuat • Range citra input terang yg lebar menjadi • range output citra gelap yang sempit

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