Computer Vision Penginderaan Visual untuk berbagai
keperluan
Dr. Mohammad Iqbal @ 2016
Disampaikan pada seminar nasional “Perkembangan Computer
Vision dan Multimedia" yang dilaksanakan oleh Himpunan
Mahasiswa Teknik Informatika Universitas Nasional pada hari
Rabu, 20 Januari 2016, di Aula Universitas Nasional Blok I lantai 4
Gunadarma University S3, S2, S1 and Proffessional Program
Faculties
1. Computer Science and Information Technology
2. Industrial Technology
3. Economic
4. Civil Engineering and Plan
5. Psikology
6. Literature
Research Organizations
Research Organization University and for every Faculty
Special Science Group Discussion
Lecturer Group Research: Foshema & Scimed
Pusat Studi :
Mikroelektronika dan Pengolahan citra – imaging system dan smart sensor
Robotika dan Multimedia Sistem Multimedia dan Robotik – Implementasi robotic vision dan data set collection
Informatika Kedokteran – Implementasi vision di bidang kedokteran dan kesehatan
Interaksi Manusia dan Teknologi – Evaluasi Interaksi mesin dengan manusia
3
Penelitian Computer Vision di Gunadarma
Menu Seminar kita hari ini…
Penggunaan Vision Hari Ini
Computer Vision Anatomy
Penglihatan (Vision) itu Tidak Sederhana
Apakah Computer Vision?
Kesimpulan
Diskusi
Mengapa perlu belajar tentang Computer vision?
Jutaan citra di capture setiap waktu
Ada jutaan aplikasi yang bisa dibuat berdasarkan CV
Menu Seminar kita hari ini…
Apakah Computer Vision?
• Defenisi Komputer Grafik ? (transformasi 3D->2D) • Defenisi Komputer grafik ? (Modeling vs. Rendering) • Jadi Defenisi Komputer vision (2D->3D) • Defenisi Computer Vision :
• Irisan antara Computer Vision dan Computer Graphics • Menurut para ahli • Permodelan berbasiskan Citra (Image-Based Modeling)
• Disiplin ilmu yang terkait • Kecerdasan Buatan • Dasar Matematika yang dibutuhkan • Kaitan ilmu modern terkini untuk Computer Vision • Lingkup Kurikulum Computer Vision di Universitas
Apakah Computer Vision?
• Kebalikan dari Komputer Grafik
• Pemahaman komputer terhadap Citra (Image Understanding) secara AI, atau menganalisis perilaku (behavior) / pola Citra
• Sensor untuk robotika
• Emulasi Komputer dari penglihatan manusia
Computer vision
World model
Computer graphics
World model
Modeling Create model Apply material ke model Tempatkan model di scene Tempatkan light di scene Tempatkan camera
Defenisi Komputer grafik ? (Modeling vs. Rendering)
Directional Light Ambient Light
Point Light
Spot Light
Penggabungan pencahayaan oleh Patrick Doran (2009)
Rendering
Ambil “citra” dengan camera
Dua-duanya dapat selesai dengan commercial software: Autodesk MayaTM ,3D Studio MaxTM, BlenderTM, etc.
9 ILMU LANJUT : Grafik Komputer
Defenisi Computer Vision : Irisan antara Computer Vision
dan Computer Graphics
modeling - shape - light - motion - optics - images IP
animation
rendering
user-interfaces
surface design
Computer Graphics
shape estimation
motion estimation
recognition
2D modeling
modeling - shape - light - motion - optics - images IP
Computer Vision
Defenisi Computer Vision [Trucco&Verri’98]
Trucco and Verri: computing properties of the 3D world from one or more digital images
Sockman and Shapiro: To make useful decisions about real physical objects and scenes based on sensed images
Ballard and Brown: The construction of explicit, meaningful description of physical objects from images
Forsyth and Ponce: Extracting descriptions of the world from pictures or sequences of pictures
Defenisi Computer Vision : Permodelan berbasiskan
Citra (Image-Based Modeling)
Images (2D) Geometry (3D)
shape Photometry appearance
+
graphics
vision
image processing
2.1 Geometric image formation
2.2 Photometric image formation
3 Image processing
4 Feature extraction 5 Camera calibration
6 Structure from motion
7 Image alignment
8 Mosaics
9 Stereo correspondence
11 Model-based reconstruction
12 Photometric recovery
14 Image-based rendering
Lingkup Kurikulum Computer Vision
Pattern
Recognition
Computer
Vision
Machine
Learning
Multi-view
Geometry
Intelligent
Robotics
Autonomous
Robotics
Multi-Robot
Systems
Image
Processing
Computer
Graphics
Computational
Perception
Menu Seminar kita hari ini…
Penglihatan (Vision) itu Tidak Sederhana
• Karakteristik Human Vision • Ilusi Adelson Checkerboard • Warna yang konstan (Color Constancy) • Ukuran yang Konstan (Size Constancy) • Ilusi Thatcher
• Area Fokus Komputer Grafik dan Vision –
Hardware & Interaction
• Timeline Teknologi Computer Vision
Penglihatan itu Tidak Sederhana
Penglihatan (vision) prestasi terbesar dari kecerdasan alami (natural intelligence ) manusia
Visual cortex menempati sekitar 50% dari bagian otak Macaque
Seakan2 otak manusia dikhususkan utk menangani urusan vision
Itu raja atau perdana
menteri ya ?
22
Karakteristik Human Vision
Penglihatan adalah proses kontruktif Persepsi kesadaran dari yang kita lihat adalah ILUSI yang
dibuat oleh otak kita (dengan proses yang luar biasa rumit).
Contoh : kecerahan (brightness), warna (color), dan ukuran yang konstan (size constancy)
23
Ilusi Adelson Checkerboard Persepsi brightness adalah fungsi rumit dari nilai piksel
(Image courtesy of Ted Adelson) Brightness constancy problem
Warna yang konstan (Color Constancy)
Warna Piksel sangat dipengaruhi oleh iluminasi
Persepsi dari konstannya suatu warna dikelola oleh otak kita
Sunlight Fluorescent light
(Images courtesy of David Heeger)
Ukuran yang Konstan (Size Constancy) Ukuran obyek VS kedalaman obyek
(Images copyright John H. Kranz, 1999)
Karakteristik Human Vision
Penglihatan akan menyelesaikan tugas tertentu saja dalam konteks yang juga spesifik Umumnya kemampuan visual itu terikat langsung dengan
kebutuhan dan konteks seseorang (kebiasaan hidup, emosional, dll).
Contoh : Thatcher illusion
HIGH RESOLUTION HIGH BRIGHTNESS LARGE VIEWING ANGLE HIGH WRITING SPEEDS LARGE COLOUR GAMUT HIGH CONTRAST LESS WEIGHT AND SIZE LOW POWER CONSUMPTION LOW COST
Area Fokus Komputer Grafik dan Vision – Hardware &
Interaction
Teknologi Display
Screenless / Hologram technology
Teknologi Surface / Touch screen
Wearable Teknologi
Stereoscopic
Perangkat Input Mouse, tablet & stylus, multi-touch, force feedback, dan game controller lainnya
(seperti Wii), scanner, digital camera (images, computer vision), dsb.
Semua bagian tubuh menjadi devais interaksi: http://www.xbox.com/kinect
Area Fokus Komputer Grafik dan Vision – Hardware &
Interaction
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Apple iPhone™
Multi form Output Cell Phones/PDAs (smartphones),
laptop/desktops/tablets,
Microsoft PPI display
3D immersive virtual reality systems such as Brown’s new Cave being built at 180 George Street
Area Fokus Komputer Grafik dan Vision – Hardware &
Interaction
Brown’s old Cave & new Cave
Samsung Galaxy SIII (Android)
Microsoft Surface
Microsoft PPI display
ILMU LANJUT : Interaksi Manusia Komputer
Timeline Teknologi Computer Vision
# Computer Vision History graph from the book of Richard Szeliski
Menu Seminar kita hari ini…
Computer Vision Anatomy
• Langkah2 dalam Pengolahan Citra Digital • Sistem Pencahayaan (Lighting system) • Staging • Lensa dan Kamera • Aplikasi Perangkat Lunak Vision
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Computer Vision Anatomy
Pada dasarnya sistem Computer atau Machine Vision dibuat untuk
membantu menggantikan keahlian manusia pada bagian visual
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Level Pengolahan citra
Level 0: Representasi citra (akuisisi, sampling, kuantisasi, kompresi)
Level 1: transformasi Image-to-image (enhancement, restoration, segmentation)
Level 2: Transformasi Image-to-parameter (feature selection)
Level 3: transformasi Parameter-to-decision (recognition and interpretation)
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Kedudukan DIP, ComVis
Image Processing: Levels 0 and 1
Image Analysis: Levels 1 and 2
Computer/Robot Vision: Levels 2 and 3
Computer Graphics/Animation ?
Pendekatan dalam “creating images” atau membuat “visual effects” dari deksripsi yang diberikan pada level sebelumnya.
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Problem Domain
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Image Aquisition
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Image Enhancement
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Image Restoration
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Morphological Processing
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Segmentation
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Object Recognition
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Representation & Description
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Image Compression
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
Computer Vision Anatomy : Langkah2 dalam Pengolahan
Citra Digital - Colour Image Processing
Image
Acquisition
Image
Restoration
Morphologic
al Processing
Segmentation
Representation
& Description
Image
Enhancement
Object
Recognition
Problem Domain
Colour Image
Processing
Image
Compression
49
Computer Vision Anatomy : Staging
Parameter-parameter penting dalam sistem pencitraan (imaging system).
50
Computer Vision Anatomy : Kamera dan Lensa
Kamera dan Lensa :
Jenis Sensor : CCD Vs CMOS (complimentary metal-oxide semiconductor)
Ukuran Sensor :
Cara Pembacaan : area scanning and line scanning.
CCD/CMOS Size. (Image copyright of Edmund Optics).
Computer Vision Anatomy : Kamera dan Lensa
Sistem Lensa :
Wide area lens (catadioptric, fisheye) Vs Basic Lens (zoom, macro, telesentric)
Sistem Filter Lensa : Polarization, IR, UV, …
Relationship between focal length, object and camera plane.
(Image copyright of Edmund Industrial Optics).
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Computer Vision Anatomy : Kamera dan Lensa
Resolution :
Focus :
Resolusi citra B lebih baik dari A.
(Image copyright of Edmund
Industrial Optic).
Computer Vision Anatomy : Kamera dan Lensa – Model
dan Geometri Kamera
Pinhole camera
Geometric transformations in 2D and 3D
or
53
Computer Vision Anatomy : Kamera dan Lensa – Camera
Calibration
Know 2D/3D correspondences, compute projection matrix
also radial distortion (non-linear)
Aplikasi Perangkat Lunak Vision
HALCON dari MVTEC http://www.mvtec.com/halcon/
HALCON is the comprehensive standard software with an integrated
development environment (IDE) for machine vision that is used worldwide. It leads to cost savings and improved time to market: HALCON's flexible architecture facilitates rapid development of machine vision, medical imaging, and image analysis applications. HALCON provides outstanding performance and a comprehensive support of multi-core platforms, MMX, and SSE2. It serves all industries by a library of more than 1400 operators for blob analysis, morphology, pattern matching, measuring, identification, and 3D vision, to name just a few.
Aplikasi Perangkat Lunak Vision
COGNEX (http://www.cognex.com/Main.aspx) Vision Systems : All-in-one systems that combine camera, processor and vision software into a
single rugged package, with a simple and flexible user interface for configuring your application.
Vision Software : Vision software gives you the most flexibility for combining the full library of powerful Cognex vision tools with the cameras, frame grabbers and peripherals of your choice, and enables easy integration with PC-based data and control programs.
Vision Sensors : Easy, affordable sensors that can be used in place of photoelectric sensors for more reliable inspection, error-proofing and part detection.
Industrial ID : Fast, reliable 1D and 2D code reading and verification for direct part mark or high-contrast applications.
Industry-Specific Products: A result of over 25 years of vision experience solving the most difficult vision applications, these products include wafer identification, surface mount device placement guidance, cylindrical product inspection and more.
Web and Surface Inspection : Industry-leading technology for detecting and classifying defects during the continuous production of metals, paper, nonwovens, plastics and glass.
Vehicle
wheel
Animal
leg
head Four-legged
Mammal
Move on road
Facing right
Can run, jump
Is herbivorous
Facing right
Penggunaan vision Hari Ini
Contoh state-of-the-art
Industrial Vision Penggunaan vision Hari Ini
Benyamin, 2013 - Inspeksi Produksi Botol Susu plastik
Deteksi kecacatan pada mulut botol
Deteksi kecacatan dalam botol Deteksi kecacatan pinggir botol
Earth viewers (3D modeling)
Image from Microsoft’s Virtual Earth
(see also: Google Earth)
Penggunaan vision Hari Ini
65
Building Rome in a Day: Agarwal et al. 2009
3D from thousands of images
Hoiem Efros Hebert SIGGRAPH 2005
3D from one image
Penggunaan vision Hari Ini
66
Digit recognition, AT&T labs
http://www.research.att.com/~yann/
Technology to convert scanned docs to text
• If you have a scanner, it probably came with OCR software
License plate readers http://en.wikipedia.org/wiki/Automatic_number_plate_recognition
Optical character recognition (OCR) Penggunaan vision Hari Ini
Many new digital cameras now detect faces
Canon, Sony, Nikon …
Face detection
Penggunaan vision Hari Ini
Sony Cyber-shot® T70 Digital Still Camera
Smile detection? Penggunaan vision Hari Ini
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Object recognition (in supermarkets)
LaneHawk by EvolutionRobotics
“A smart camera is flush-mounted in the checkout
lane, continuously watching for items. When an
item is detected and recognized, the cashier
verifies the quantity of items that were found under
the basket, and continues to close the transaction.
The item can remain under the basket, and with
LaneHawk, you are assured to get paid for it… “
Penggunaan vision Hari Ini
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“How the Afghan Girl was Identified by Her Iris Patterns” Read the story
wikipedia
Vision-based biometrics Penggunaan vision Hari Ini
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Fingerprint scanners on
many new laptops,
other devices
Face recognition systems now
beginning to appear more widely http://www.sensiblevision.com/
Login without a password… Penggunaan vision Hari Ini
Point & Find, Nokia
Google Goggles
Object recognition (in mobile phones)
Penggunaan vision Hari Ini
The Matrix movies, ESC Entertainment, XYZRGB, NRC
Special effects: shape capture Penggunaan vision Hari Ini
Pirates of the Carribean, Industrial Light and Magic
Special effects: motion capture Penggunaan vision Hari Ini
Based-on Ega Hegarini 2015 - Motion Analysis for sport science
Special effects: motion capture Penggunaan vision Hari Ini
76
Sports
Sportvision first down line
Nice explanation on www.howstuffworks.com
http://www.sportvision.com/video.html
Penggunaan vision Hari Ini
Mobileye
Vision systems currently in high-end BMW, GM, Volvo models
By 2010: 70% of car manufacturers.
Slide content courtesy of Amnon Shashua
Smart cars Penggunaan vision Hari Ini
http://www.nytimes.com/2010/10/10/science/10google.html?ref=artificialintelligence
Google cars Penggunaan vision Hari Ini
Object Recognition: http://www.youtube.com/watch?feature=iv&v=fQ59dXOo63o
Mario: http://www.youtube.com/watch?v=8CTJL5lUjHg
3D: http://www.youtube.com/watch?v=7QrnwoO1-8A
Robot: http://www.youtube.com/watch?v=w8BmgtMKFbY
Interactive Games: Kinect Penggunaan vision Hari Ini
Vision systems (JPL) used for several tasks
• Panorama stitching
• 3D terrain modeling
• Obstacle detection, position tracking
• For more, read “Computer Vision on Mars” by Matthies et al.
NASA'S Mars Exploration Rover Spirit captured this westward view from atop
a low plateau where Spirit spent the closing months of 2007.
Vision in space Penggunaan vision Hari Ini
http://www.robocup.org/
NASA’s Mars Spirit Rover
http://en.wikipedia.org/wiki/Spirit_rover
Saxena et al. 2008
STAIR at Stanford
Mobile robots
Penggunaan vision Hari Ini
Penggunaan Vision Hari Ini
Image guided surgery
Grimson et al., MIT 3D imaging
MRI, CT
Medical imaging
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Entertainment : Video Mapping
www.artisuniversalis.com/educational
1. Uses projection to place videographics on a
physical object.
2. Creates an optical illusion using light.
3. Transforms ordinary objects into magical living
entities.
Penggunaan vision Hari Ini
Hari ini sudah sama-sama kita bicarakan :
Definisi
Dasar Ilmu yang harus dikuasai
Tantangannya
Anatominya
Implementasi Computer Vision dalam kehidupan
Selanjutnya ?
Terserah anda… (mau jadi player?
Atau mau jadi penonton saja?)
Kesimpulan
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