Penginderaan Visual untuk berbagai keperluan

  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

   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 Penelitian Computer Vision di Gunadarma

  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

• – 

Menu Seminar kita hari ini…

  

Penggunaan Vision Hari Ini

Computer Vision Anatomy

Penglihatan (Vision) itu Tidak Sederhana

  

Apakah Computer Vision?

Kesimpulan

   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

  Computer vision World model Computer graphics World model

• Sensor untuk robotika

  Defenisi Komputer Grafik ? (transformasi 3D->2D)

  3D geometri Simulasi proyeksi

   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

   Rendering Ambil “citra” dengan camera

   Dua-duanya dapat selesai dengan commercial software: Autodesk Maya ^TM ,3D Studio Max ^TM ,

  Blender ^TM , etc. Jadi Defenisi Komputer vision (2D->3D)

  3D Geometri

  Estimasi dan Computer Graphics

  rendering shape estimation modeling modeling

• shape - shape

  surface design motion estimation

• light - light
• motion - motion

  animation recognition

• optics - optics
• images - images

  2D modeling user-interfaces

   IP

   IP 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

  Citra (Image-Based Modeling)

  image processing graphics Geometry (3D) Photometry + Images (2D) shape appearance vision

  2.1 Geometric image

  2.2 Photometric formation image formation

  5 Camera calibration

  6 Structure alignment from motion

  8 Mosaics

  9 Stereo correspondence

  11 Model-based

  12 Photometric Disiplin Ilmu yang Terkait : Kecerdasan Buatan

  Kaitan ilmu modern terkini untuk Computer Vision

Lingkup Kurikulum Computer Vision

  Pattern Recognition Computer Vision

  Machine Learning Multi-view

  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 Penglihatan (Vision) itu Tidak Sederhana

  Mata Manusia Vs Kamera 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 ? 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) Ilusi Adelson Checkerboard

  Persepsi brightness adalah fungsi rumit dari nilai piksel Warna yang konstan (Color Constancy)

  Warna Piksel sangat dipengaruhi oleh iluminasi Persepsi dari konstannya suatu warna dikelola oleh otak kita Ukuran yang Konstan (Size Constancy)

  Ukuran obyek VS kedalaman obyek 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 Ilusi Thatcher

  

(Due to P. Thompson) Ilusi Thatcher

• – Hardware & Interaction

   HIGH RESOLUTION

   HIGH BRIGHTNESS

   LARGE VIEWING ANGLE

   HIGH WRITING SPEEDS

   LARGE COLOUR GAMUT

   HIGH CONTRAST

   LESS WEIGHT AND SIZE

   LOW POWER CONSUMPTION

   LOW COST

  Teknologi Display Screenless / Hologram technology

  

Teknologi Surface /

Wearable Teknologi Stereoscopic

• – Hardware & Interaction

   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:

  

• – Hardware & Interaction

   Multi form Output 

  Cell Phones/PDAs (smartphones), _{Apple iPhone™ Samsung Galaxy SIII (Android)} laptop/desktops/tablets, Microsoft Surface

   Microsoft PPI display

  

  3D immersive virtual reality systems such as Brown’s new Cave being built at 180 George Street

  Microsoft PPI display Brown’s Timeline Teknologi Computer Vision

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

Computer Vision Anatomy

  

Pada dasarnya sistem Computer atau Machine Vision dibuat untuk

membantu menggantikan keahlian manusia pada bagian visual

  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

  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 Citra Digital - Problem Domain Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Image Aquisition Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Image Enhancement Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Image Restoration Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Morphological Processing Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Segmentation Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Object Recognition Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Representation & Description Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Image Compression Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain Citra Digital - Colour Image Processing Image Morphologic Restoration al Processing Image

  Segmentation Enhancement Image Object Acquisition

  Recognition

  Representation Problem Domain

Computer Vision Anatomy

  

1. Lighting

  2. Staging

  3. Lenses

  

4. Cameras Computer Vision Anatomy : Lighting

  Computer Vision Anatomy : Staging

   Parameter-parameter penting dalam sistem pencitraan (imaging system).

  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 : Relationship between focal length, object and camera plane. _{(Image copyright of Edmund Industrial Optics).}

   Wide area lens (catadioptric, fisheye) Vs Basic Lens (zoom, macro, telesentric) Computer Vision Anatomy : Kamera dan Lensa

   Resolution :

   Focus : Resolusi citra B lebih baik dari A.

  (Image copyright of Edmund Industrial Optic).

• – Model dan Geometri Kamera

  Pinhole camera or

  Geometric transformations in 2D and 3D

• – Camera

  Calibration 

  Know 2D/3D correspondences, compute projection matrix also radial distortion (non-linear) Aplikasi Perangkat Lunak Vision

   HALCON dari MVTEC 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.

  Menu Seminar kita hari ini…

Penggunaan Vision Hari Ini

  Vehicle wheel Animal leg head Four-legged Mammal Move on road

  Can run, jump Is herbivorous Facing right

  Contoh state-of-the-art

  Industrial Vision

  Industrial Vision Deteksi kecacatan pada mulut botol

  Recovery 3D layout dan context

  BED

  

Editing images as if they were 3D scenes

  Earth viewers (3D modeling)

  Building Rome in a Day: Agarwal et al. 2009

  3D from thousands of images Hoiem Efros Hebert SIGGRAPH 2005

  3D from one image

  Optical character recognition (OCR) Technology to convert scanned docs to text

• If you have a scanner, it probably came with OCR software Digit recognition, AT&T labs

   Many new digital cameras now detect faces 

  Face detection

  Smile detection?

   Object recognition (in supermarkets)

   “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… “

  “How the Afghan Girl was Identified by Her Iris Patterns” Read the

  Vision-based biometrics

  Fingerprint scanners on Face recognition systems now beginning to appear more widely

  Login without a password…

  Object recognition (in mobile phones)

  Special effects: shape capture

  Special effects: motion capture

  Special effects: motion capture

  Sports Sportvision first down line

  Nic

  Smart cars 

   

  Vision systems currently in high-end BMW, GM, Volvo models

  Smart Vision Drone

  Google cars

  Interactive Games: Kinect 

  Object Recog 

   

  3D 

  Robot:

  Vision in space captured this westward view from atop a low plateau where Spirit spent the closing months of 2007.

Vision systems (JPL) used for several tasks

• Panorama stitching
• 3D terrain modeling

  Vision-guided robots position nut runners on wheels Industrial robots

  Mobile robots

  Saxena et al. 2008 at Stanford

  NASA’s Mars Spirit Rover Penggunaan Vision Hari Ini

  Image guided surgery

  3D imaging Medical imaging

  Entertainment : Video Mapping

  

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. Kesimpulan 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?

Thank You

   merci

  Terima Kasih Question?