Penginderaan Visual untuk berbagai keperluan

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Computer Vision

Penginderaan Visual untuk berbagai keperluan

Dr. Mohammad Iqbal @ 2016

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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

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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

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Menu Seminar kita hari ini…

Penggunaan Vision Hari Ini

Computer Vision Anatomy

Penglihatan (Vision) itu Tidak Sederhana

Apakah Computer Vision?

Kesimpulan

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Mengapa perlu belajar tentang Computer vision?

 Jutaan citra di capture setiap waktu

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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

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Apakah Computer Vision?

• Kebalikan dari Komputer Grafik

• Pemahaman komputer terhadap Citra (Image Understanding) secara AI, atau menganalisis perilaku (behavior) / pola Citra

• Sensor untuk robotika

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Grafik

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

3D geometri

Sifat fisik

Simulasi

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 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 Max}TM_,

BlenderTM_{, etc.}

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Jadi Defenisi Komputer vision (2D->3D)

3D Geometri

Sifat fisik

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Defenisi Computer Vision : Irisan antara Computer Vision dan Computer Graphics

modeling surface design

Computer Graphics

shape estimation

motion estimation

recognition

2D modeling modeling

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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

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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

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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

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Penglihatan (Vision) itu Tidak Sederhana

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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

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Karakteristik Human Vision

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_{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}

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Ilusi Adelson Checkerboard

Persepsi brightness adalah fungsi rumit dari nilai piksel

(Image courtesy of Ted Adelson)

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Warna yang konstan (Color Constancy) Warna Piksel sangat dipengaruhi oleh iluminasi

Persepsi dari konstannya suatu warna dikelola oleh otak kita

Sunlight Fluorescent light

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Ukuran yang Konstan (Size Constancy)

Ukuran obyek VS kedalaman obyek

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Karakteristik Human Vision

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_{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).

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Ilusi Thatcher

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Ilusi Thatcher

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 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

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 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}

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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

Brown’s

old Cave & new

Cave

Samsung Galaxy SIII (Android)

Microsoft Surface

Microsoft PPI display

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Timeline Teknologi Computer Vision

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Computer Vision Anatomy

• Langkah2 dalam Pengolahan Citra Digital

• Sistem Pencahayaan (Lighting system)

• Staging

• Lensa dan Kamera

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Computer Vision Anatomy

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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)

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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

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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

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Image Aquisition

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Image Enhancement

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Image Restoration

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Morphological Processing

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Segmentation

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Object Recognition

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Representation & Description

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Image Compression

Segmentation

Problem Domain

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Computer Vision Anatomy : Langkah2 dalam Pengolahan Citra Digital - Colour Image Processing

Segmentation

Problem Domain

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Computer Vision Anatomy

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Computer Vision Anatomy : Staging

 Parameter-parameter

penting dalam sistem

pencitraan (imaging

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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.

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 Sistem Lensa :

 Wide area lens (catadioptric, fisheye) Vs Basic Lens (zoom, macro, telesentric)

 Sistem Filter Lensa : Polarization, IR, UV, …

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 Resolution :

 Focus :

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Computer Vision Anatomy : Kamera dan Lensa – Model

dan Geometri Kamera

Pinhole camera

Geometric transformations in 2D and 3D

or

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Computer Vision Anatomy : Kamera dan Lensa – Camera Calibration

 _{Know 2D/3D correspondences,}

compute projection matrix

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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,

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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.

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Vehicle

wheel

Animal

leg

head Four-legged Mammal

Move on road Facing right

Can run, jump Is herbivorous Facing right

Penggunaan vision Hari Ini

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Damar Darbito, 2013 - Inspeksi Produksi Kartu Seluler

Industrial Vision

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Industrial Vision

Penggunaan vision Hari Ini

Benyamin, 2013 - Inspeksi Produksi Botol Susu plastik

Deteksi kecacatan pada mulut botol

Deteksi kecacatan dalam botol

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Recovery 3D layout dan context

BED

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Editing images as if they were 3D scenes

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Earth viewers (3D modeling)

Image from Microsoft’s Virtual Earth

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Building Rome in a Day: Agarwal et al. 2009

3D from thousands of images

Hoiem Efros Hebert SIGGRAPH 2005

3D from one image

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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)

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 Many new digital cameras now detect faces

 Canon, Sony, Nikon …

Face detection

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Sony Cyber-shot® T70 Digital Still Camera

Smile detection?

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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… “

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

Vision-based biometrics

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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…

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Point & Find, Nokia Google Goggles

Object recognition (in mobile phones)

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The Matrix movies, ESC Entertainment, XYZRGB, NRC

Special effects: shape capture

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Pirates of the Carribean, Industrial Light and Magic

Special effects: motion capture

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Based-on Ega Hegarini 2015 - Motion Analysis for sport science

Special effects: motion capture

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Sports

Sportvision first down line

Nice explanation on www.howstuffworks.com http://www.sportvision.com/video.html

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 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

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Smart Vision Drone

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http://www.nytimes.com/2010/10/10/science/10google.html?ref=artificialintelligence

Google cars

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 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

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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

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Vision-guided robots position nut runners on wheels

Industrial robots

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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

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Penggunaan Vision Hari Ini

Image guided surgery

Grimson et al., MIT

3D imaging MRI, CT

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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.

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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?)

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Penginderaan Visual untuk berbagai keperluan

Computer Vision

Gunadarma University

Mengapa perlu belajar tentang Computer vision?

Simulasi

Karakteristik Human Vision

_{Penglihatan adalah proses kontruktif}

Karakteristik Human Vision

_{Penglihatan akan menyelesaikan tugas tertentu}

saja dalam konteks yang juga spesifik

Terima Kasih

Thank You

Question?

merci

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