MULTIMEDIA & COMPUTER VISION

1. YAYASAN PERGURUAN TINGGI KOMPUTER (YPTK) PADANG MULTIMEDIA & COMPUTER VISION UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG 1 PROSES DIP 2 STAGES IN DIP 3 PENGOLAHAN CITRA UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

2. [Digital Images Processing] UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

3. [Digital Images Processing] BAGAIMANA MERAWAT/MENJAGA HATI Kecuali orang-orang yang menghadap Allah dengan hati yang bersih (QS: 26. Asy Syu'araa : 89) BUKA BERSIHKAN SEHATKAN LEMBUTKAN TAJAMKAN 1 2 Yaitu orang-orang yang beriman dan hati mereka manjadi tenteram dengan mengingat Allah. Ingatlah, hanya dengan mengingati Allah-lah hati menjadi tenteram. (QS : 13. Ar Ra'd:28) 3 4 5 UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

4. High Level Process Mid Level Process Low Level Process Input: Attributes Output: Understanding Examples: Scene understanding, autonomous navigation Input: Image Output: Attributes Examples: Object recognition, segmentation Input: ImageOutput: Image Examples: Noise removal, image sharpening [Digital Images Processing] Process Level of DIP • The continuum from image processing to computer vision can be broken up into low-, mid- and high-level processes In this course we will stop here UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

5. [Digital Images Processing] Examples: Image Enhancement • One of the most common uses of DIP techniques: improve quality, remove noise etc UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

6. Key Stages in DIP [Digital Images Processing] Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

7. [Digital Images Processing] Image Acquisition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

8. [Digital Images Processing] Image Enhancement Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

9. [Digital Images Processing] Image Restoration Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

10. [Digital Images Processing] Morphological Processing Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

11. [Digital Images Processing] Image Segmentation Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

12. [Digital Images Processing] Object Recognition Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

13. [Digital Images Processing] Representation & Description Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

14. [Digital Images Processing] Image Compression Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

15. [Digital Images Processing] Representation & Description Image Restoration Morphological Processing Image Enhancement Segmentation Image Acquisition Object Recognition Representation & Description Problem Domain Colour Image Processing Image Compression UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

16. The goal of computer vision [Digital Images Processing] • To bridge the gap between pixels and “meaning” What we see What a computer sees UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

17. [Digital Images Processing] Vision as measurement device Goesele et al. Pollefeys et al. UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

18. Human Eye Ciliary Muscle Sclera Ear side (Temporal) Iris Vitreous Humor Fovea Pupil Lens Retina Optic Nerve Cornea Nose side (Nasal) Aqueous Humor Choroid Suspensory ligament UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

19. Human Visual System Detection Processing Exposure Control Image formation • Retina • Rods • Cones • Brain • Cornea • Lens • Iris/pupil • Photoreceptor • sensitivity UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

20. Retina • Retina is the photosensitive “detector” for the eye. • Two types of receptors in the retina: rods for low light level, and cones for color. • Located at the center of the retina, fovea contains a greater concentration of cones. • Signals from the receptors leave through the optic nerve tothe brain. Retina Fovea Optic Nerve UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

21. Plexiform Layer • The retina is made of three layers: • Plexiform layer is a network of nerves which carry the signals from the photo receptors. • Photo receptors. • Choroid provides nourishment to the receptors, as well as absorb any light that didn’t get absorbed by the photo receptors, like a antihalation backing in film. Fovea Photo receptors Light Plexiform Layer Choroid Optic Nerve UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

22. Rods and Cones Synaptic endings Cell nucleus Inner segments Outer segments Rod Cone • Highly sensitive to low light level or scotopic conditions. • Black and white. • Dispersed in the periphery of the retina. • Sensitive to high light level or photopic conditions. • Three types of cones responsible for color vision. • Concentrated in the fovea. UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

23. Elements of Visual Perception • Cones • 6 – 7 million in each eye • Photopic or bright-light vision • Highly sensitive to color • Rods • 75 – 150 million • Not involved in color vision • Sensitive to low level of illumination (scotopic or dim-light vision) • An object appears brightly colored in daylight will be seen colorless in moonlight (why) UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

24. 80º 80 º 60 º 60 º 40 º 40 º 20 º 20 º 0 º Photoreceptors Distribution Visual Axis Temporal Nasal • Cones are concentrated in the fovea. • Rods predominate the periphery. • There is a blind spot where there are no photoreceptors, at the point where the nerves exit the eye (optic nerve). Blind spot 160 140 Rods 120 Number of receptors per mm2 100 80 60 40 Cones 20 40 º 60 º 20 º 40 º 20 º 0 º 60 º 80 º UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

25. Retina Cones Light • The retina is made of network of nerve cells. • The network works together to reduce the amount of information in a process called lateral inhibition. Rods Bipolar cells To optic nerve Amicrine cells Ganglion cells Horizontal cells UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

26. UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

27. Vision as a source of semantic information [Digital Images Processing] UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

28. [Digital Images Processing] Object categorization sky building flag face banner wall street lamp bus bus UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

29. Scene and context categorization [Digital Images Processing] • outdoor • city • traffic • … UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

30. [Digital Images Processing] Qualitative spatial information slanted non-rigid moving object vertical rigid moving object rigid moving object horizontal UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc slide credit: Fei-Fei, Fergus & Torralba

31. Personal photo albums Movies, news, sports Medical and scientific images Surveillance and security [Digital Images Processing] Why study Digital Image Processing? • Vision is useful: Images and video are everywhere! UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc

32. 0 0 0 6 1 0 9 0 23 1 0 14 0 4 1 1 A= 00 N=100 U=101 “”=010 I=1100 S=1101 D=0110 G=11100 J=11101 L=11110 M=11111 P=01110 R=01111 0 5 1 1 3 8 0 0 2 1 1 4 0 1 2 1 0 1 23 X 8 BIT = 184 2 1

33. MetodeKompresiHuffman [Digital Images Processing] 0 0 0 I = 0000-0000 A=0000-0110 N=0000-0010 “”=0000-0011 K=0000-1000 M=0000-1001 T=0000-1010 F=0000-1011 J=0001-0110 O=0001-0111 R=0001-1100 S=0001-1101 U=0001-1111 10 0 1 5 1 0 0 25 4 1 0 0 8 1 0 1 4 1 15 2 1 1 0 7 0 TentukanKalimat JumlahkanKemunculanHuruf Urutkanberdasarkankemunculan ASC JmlkankemunculanberdasarkanNilaiTerkecil BerikanNilaiBinerberdasarkanNilaiterkecil TentukannilaiBinernya 2 1 1 4 0 1 2 1 UNIVERSITAS PUTRA INDONESIA “YPTK” PADANG Dr. Ir. Sumijan, M.Sc