182 Computer vision Criteria for Multi-purpose Projects

What is involved in Computer vision

Find out what the related areas are that Computer vision connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a Computer vision thinking-frame.

How far is your company on its Computer vision journey?

Take this short survey to gauge your organization’s progress toward Computer vision leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.

To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.

Start the Checklist

Below you will find a quick checklist designed to help you think about which Computer vision related domains to cover and 182 essential critical questions to check off in that domain.

The following domains are covered:

Computer vision, Active contour model, Sega VR, Content-based image retrieval, Outline of artificial intelligence, Medical scanner, QR code, Video tracking, Feature detection, The Sword of Damocles, Free viewpoint television, Camera resectioning, Signal processing, 360-degree video, Graphics processing units, Solid-state physics, Corner detection, Ridge detection, Blob detection, Side-scan sonar, Microsoft HoloLens, Oculus Rift, Pattern recognition, Bin Picking, Mars Exploration Rover, Simultaneous localization and mapping, Unmanned aerial vehicle, Samsung Gear VR, Synthetic aperture sonar, Image stitching, Simulated reality in fiction, Pervasive game, Optical head-mounted display, VR photography, Markov random field, Image restoration, Machine vision, Image sensor, Computer-human interaction, MNIST database, Assembly line, Image recognition, Facial recognition system, Magic Leap, Open Source Virtual Reality, Real-time computer graphics, Visual perception, BMVA Summer School, Digital image, Omnidirectional treadmill, Optical flow, Edge detection, Neural network, Virtual graffiti, Image segmentation, Interest point detection, Projection augmented model, Persistent world, 3D pose estimation, Digital image processing, Automated species identification, Artificial neural networks, Wired glove, Teknomo–Fernandez algorithm, Machine vision glossary, Haptic suit, Simulated reality, Augmented virtuality, Polyhedron model, People counter, Light field, Autonomous vehicle:

Computer vision Critical Criteria:

Consult on Computer vision results and customize techniques for implementing Computer vision controls.

– What management system can we use to leverage the Computer vision experience, ideas, and concerns of the people closest to the work to be done?

– How do we Lead with Computer vision in Mind?

Active contour model Critical Criteria:

Discuss Active contour model failures and budget for Active contour model challenges.

– What are the success criteria that will indicate that Computer vision objectives have been met and the benefits delivered?

– What are the barriers to increased Computer vision production?

– What are our Computer vision Processes?

Sega VR Critical Criteria:

See the value of Sega VR tasks and probe using an integrated framework to make sure Sega VR is getting what it needs.

– What role does communication play in the success or failure of a Computer vision project?

– What new services of functionality will be implemented next with Computer vision ?

– How will we insure seamless interoperability of Computer vision moving forward?

Content-based image retrieval Critical Criteria:

Value Content-based image retrieval decisions and adjust implementation of Content-based image retrieval.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Computer vision services/products?

– How can we incorporate support to ensure safe and effective use of Computer vision into the services that we provide?

– In what ways are Computer vision vendors and us interacting to ensure safe and effective use?

Outline of artificial intelligence Critical Criteria:

Win new insights about Outline of artificial intelligence risks and cater for concise Outline of artificial intelligence education.

– What are the short and long-term Computer vision goals?

– How will you measure your Computer vision effectiveness?

Medical scanner Critical Criteria:

Conceptualize Medical scanner issues and spearhead techniques for implementing Medical scanner.

– Consider your own Computer vision project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?

– What are our needs in relation to Computer vision skills, labor, equipment, and markets?

QR code Critical Criteria:

Gauge QR code quality and get going.

– What are your most important goals for the strategic Computer vision objectives?

– Who are the people involved in developing and implementing Computer vision?

– What sources do you use to gather information for a Computer vision study?

Video tracking Critical Criteria:

Conceptualize Video tracking failures and point out Video tracking tensions in leadership.

– How do your measurements capture actionable Computer vision information for use in exceeding your customers expectations and securing your customers engagement?

– To what extent does management recognize Computer vision as a tool to increase the results?

Feature detection Critical Criteria:

Concentrate on Feature detection goals and be persistent.

– Is there a Computer vision Communication plan covering who needs to get what information when?

– Think of your Computer vision project. what are the main functions?

– What are the Essentials of Internal Computer vision Management?

The Sword of Damocles Critical Criteria:

Do a round table on The Sword of Damocles engagements and perfect The Sword of Damocles conflict management.

– In a project to restructure Computer vision outcomes, which stakeholders would you involve?

– Is maximizing Computer vision protection the same as minimizing Computer vision loss?

– What vendors make products that address the Computer vision needs?

Free viewpoint television Critical Criteria:

Have a round table over Free viewpoint television planning and diversify disclosure of information – dealing with confidential Free viewpoint television information.

– Meeting the challenge: are missed Computer vision opportunities costing us money?

Camera resectioning Critical Criteria:

Troubleshoot Camera resectioning failures and devote time assessing Camera resectioning and its risk.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this Computer vision process?

Signal processing Critical Criteria:

Review Signal processing risks and test out new things.

– Does Computer vision analysis show the relationships among important Computer vision factors?

– How do we make it meaningful in connecting Computer vision with what users do day-to-day?

– What about Computer vision Analysis of results?

360-degree video Critical Criteria:

Interpolate 360-degree video leadership and look for lots of ideas.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new Computer vision in a volatile global economy?

– Who will be responsible for documenting the Computer vision requirements in detail?

– What is Effective Computer vision?

Graphics processing units Critical Criteria:

Apply Graphics processing units tactics and do something to it.

– Will Computer vision deliverables need to be tested and, if so, by whom?

– What are the usability implications of Computer vision actions?

– Have all basic functions of Computer vision been defined?

Solid-state physics Critical Criteria:

X-ray Solid-state physics projects and probe the present value of growth of Solid-state physics.

– How do we measure improved Computer vision service perception, and satisfaction?

– Does the Computer vision task fit the clients priorities?

Corner detection Critical Criteria:

Guide Corner detection projects and learn.

– What will be the consequences to the business (financial, reputation etc) if Computer vision does not go ahead or fails to deliver the objectives?

– How do we ensure that implementations of Computer vision products are done in a way that ensures safety?

– Do the Computer vision decisions we make today help people and the planet tomorrow?

Ridge detection Critical Criteria:

Gauge Ridge detection governance and document what potential Ridge detection megatrends could make our business model obsolete.

– Does Computer vision appropriately measure and monitor risk?

– Who needs to know about Computer vision ?

Blob detection Critical Criteria:

Read up on Blob detection risks and probe Blob detection strategic alliances.

– Are accountability and ownership for Computer vision clearly defined?

– Who will provide the final approval of Computer vision deliverables?

– Are assumptions made in Computer vision stated explicitly?

Side-scan sonar Critical Criteria:

Co-operate on Side-scan sonar failures and inform on and uncover unspoken needs and breakthrough Side-scan sonar results.

– How will you know that the Computer vision project has been successful?

– Which Computer vision goals are the most important?

Microsoft HoloLens Critical Criteria:

Design Microsoft HoloLens management and know what your objective is.

– Risk factors: what are the characteristics of Computer vision that make it risky?

Oculus Rift Critical Criteria:

Define Oculus Rift results and diversify by understanding risks and leveraging Oculus Rift.

– At what point will vulnerability assessments be performed once Computer vision is put into production (e.g., ongoing Risk Management after implementation)?

Pattern recognition Critical Criteria:

Gauge Pattern recognition management and slay a dragon.

– Does Computer vision create potential expectations in other areas that need to be recognized and considered?

Bin Picking Critical Criteria:

Categorize Bin Picking adoptions and gather Bin Picking models .

– Can Management personnel recognize the monetary benefit of Computer vision?

– What are internal and external Computer vision relations?

– Are there Computer vision Models?

Mars Exploration Rover Critical Criteria:

Chat re Mars Exploration Rover leadership and find the essential reading for Mars Exploration Rover researchers.

– Why should we adopt a Computer vision framework?

– What are current Computer vision Paradigms?

Simultaneous localization and mapping Critical Criteria:

Frame Simultaneous localization and mapping adoptions and probe Simultaneous localization and mapping strategic alliances.

– Is Computer vision Required?

Unmanned aerial vehicle Critical Criteria:

Examine Unmanned aerial vehicle management and forecast involvement of future Unmanned aerial vehicle projects in development.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your Computer vision processes?

– How do mission and objectives affect the Computer vision processes of our organization?

Samsung Gear VR Critical Criteria:

Look at Samsung Gear VR quality and acquire concise Samsung Gear VR education.

– What tools do you use once you have decided on a Computer vision strategy and more importantly how do you choose?

– Do we have past Computer vision Successes?

Synthetic aperture sonar Critical Criteria:

Refer to Synthetic aperture sonar planning and triple focus on important concepts of Synthetic aperture sonar relationship management.

Image stitching Critical Criteria:

Accelerate Image stitching projects and probe using an integrated framework to make sure Image stitching is getting what it needs.

– Do you monitor the effectiveness of your Computer vision activities?

Simulated reality in fiction Critical Criteria:

Weigh in on Simulated reality in fiction goals and figure out ways to motivate other Simulated reality in fiction users.

– For your Computer vision project, identify and describe the business environment. is there more than one layer to the business environment?

– How would one define Computer vision leadership?

Pervasive game Critical Criteria:

Shape Pervasive game tactics and plan concise Pervasive game education.

– Have the types of risks that may impact Computer vision been identified and analyzed?

Optical head-mounted display Critical Criteria:

Consolidate Optical head-mounted display results and give examples utilizing a core of simple Optical head-mounted display skills.

– How does the organization define, manage, and improve its Computer vision processes?

– Which individuals, teams or departments will be involved in Computer vision?

VR photography Critical Criteria:

Infer VR photography tasks and triple focus on important concepts of VR photography relationship management.

– Who sets the Computer vision standards?

Markov random field Critical Criteria:

Grade Markov random field governance and find answers.

– What are your results for key measures or indicators of the accomplishment of your Computer vision strategy and action plans, including building and strengthening core competencies?

– Do those selected for the Computer vision team have a good general understanding of what Computer vision is all about?

– How do we go about Comparing Computer vision approaches/solutions?

Image restoration Critical Criteria:

Substantiate Image restoration governance and frame using storytelling to create more compelling Image restoration projects.

– How important is Computer vision to the user organizations mission?

– How can the value of Computer vision be defined?

Machine vision Critical Criteria:

Weigh in on Machine vision engagements and arbitrate Machine vision techniques that enhance teamwork and productivity.

– What other jobs or tasks affect the performance of the steps in the Computer vision process?

Image sensor Critical Criteria:

Derive from Image sensor risks and finalize the present value of growth of Image sensor.

– How do senior leaders actions reflect a commitment to the organizations Computer vision values?

Computer-human interaction Critical Criteria:

Co-operate on Computer-human interaction tasks and visualize why should people listen to you regarding Computer-human interaction.

– What is the source of the strategies for Computer vision strengthening and reform?

– Does Computer vision analysis isolate the fundamental causes of problems?

– What are the business goals Computer vision is aiming to achieve?

MNIST database Critical Criteria:

Understand MNIST database strategies and oversee MNIST database requirements.

– Who will be responsible for deciding whether Computer vision goes ahead or not after the initial investigations?

– How do we keep improving Computer vision?

Assembly line Critical Criteria:

Confer re Assembly line outcomes and work towards be a leading Assembly line expert.

– Are we Assessing Computer vision and Risk?

Image recognition Critical Criteria:

Review Image recognition adoptions and ask what if.

– Can we self insure for disaster recovery or do we use a recommend vendor certified hot site?

– How can we improve Computer vision?

Facial recognition system Critical Criteria:

Reorganize Facial recognition system risks and remodel and develop an effective Facial recognition system strategy.

– Where do ideas that reach policy makers and planners as proposals for Computer vision strengthening and reform actually originate?

– What knowledge, skills and characteristics mark a good Computer vision project manager?

– How do we manage Computer vision Knowledge Management (KM)?

Magic Leap Critical Criteria:

Huddle over Magic Leap governance and separate what are the business goals Magic Leap is aiming to achieve.

– What are all of our Computer vision domains and what do they do?

– How can skill-level changes improve Computer vision?

Open Source Virtual Reality Critical Criteria:

Model after Open Source Virtual Reality results and simulate teachings and consultations on quality process improvement of Open Source Virtual Reality.

– Who will be responsible for making the decisions to include or exclude requested changes once Computer vision is underway?

– How do we Identify specific Computer vision investment and emerging trends?

– Why are Computer vision skills important?

Real-time computer graphics Critical Criteria:

Participate in Real-time computer graphics visions and optimize Real-time computer graphics leadership as a key to advancement.

– Why is it important to have senior management support for a Computer vision project?

Visual perception Critical Criteria:

Guide Visual perception adoptions and finalize the present value of growth of Visual perception.

– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which Computer vision models, tools and techniques are necessary?

– Is there any existing Computer vision governance structure?

– How to Secure Computer vision?

BMVA Summer School Critical Criteria:

Illustrate BMVA Summer School projects and define what our big hairy audacious BMVA Summer School goal is.

Digital image Critical Criteria:

Bootstrap Digital image projects and look at it backwards.

– What are the key elements of your Computer vision performance improvement system, including your evaluation, organizational learning, and innovation processes?

– Can we add value to the current Computer vision decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

Omnidirectional treadmill Critical Criteria:

Deduce Omnidirectional treadmill failures and separate what are the business goals Omnidirectional treadmill is aiming to achieve.

– Think about the functions involved in your Computer vision project. what processes flow from these functions?

Optical flow Critical Criteria:

Adapt Optical flow engagements and correct better engagement with Optical flow results.

– Will Computer vision have an impact on current business continuity, disaster recovery processes and/or infrastructure?

– Does Computer vision systematically track and analyze outcomes for accountability and quality improvement?

– How to deal with Computer vision Changes?

Edge detection Critical Criteria:

Inquire about Edge detection risks and budget for Edge detection challenges.

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding Computer vision?

– How do we go about Securing Computer vision?

Neural network Critical Criteria:

Audit Neural network planning and ask questions.

– How do we Improve Computer vision service perception, and satisfaction?

– What business benefits will Computer vision goals deliver if achieved?

Virtual graffiti Critical Criteria:

Track Virtual graffiti tasks and customize techniques for implementing Virtual graffiti controls.

Image segmentation Critical Criteria:

Tête-à-tête about Image segmentation projects and use obstacles to break out of ruts.

Interest point detection Critical Criteria:

Guard Interest point detection projects and get out your magnifying glass.

– Is the scope of Computer vision defined?

Projection augmented model Critical Criteria:

Extrapolate Projection augmented model outcomes and point out Projection augmented model tensions in leadership.

Persistent world Critical Criteria:

Merge Persistent world management and probe Persistent world strategic alliances.

– What are your current levels and trends in key measures or indicators of Computer vision product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?

3D pose estimation Critical Criteria:

Be responsible for 3D pose estimation goals and know what your objective is.

– Among the Computer vision product and service cost to be estimated, which is considered hardest to estimate?

Digital image processing Critical Criteria:

Contribute to Digital image processing projects and perfect Digital image processing conflict management.

– Which customers cant participate in our Computer vision domain because they lack skills, wealth, or convenient access to existing solutions?

– Will new equipment/products be required to facilitate Computer vision delivery for example is new software needed?

Automated species identification Critical Criteria:

Jump start Automated species identification issues and remodel and develop an effective Automated species identification strategy.

Artificial neural networks Critical Criteria:

Accommodate Artificial neural networks failures and catalog what business benefits will Artificial neural networks goals deliver if achieved.

– What are specific Computer vision Rules to follow?

Wired glove Critical Criteria:

Inquire about Wired glove goals and use obstacles to break out of ruts.

Teknomo–Fernandez algorithm Critical Criteria:

Rank Teknomo–Fernandez algorithm strategies and diversify disclosure of information – dealing with confidential Teknomo–Fernandez algorithm information.

– How do we maintain Computer visions Integrity?

Machine vision glossary Critical Criteria:

Judge Machine vision glossary strategies and innovate what needs to be done with Machine vision glossary.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these Computer vision processes?

– What are the disruptive Computer vision technologies that enable our organization to radically change our business processes?

Haptic suit Critical Criteria:

Be clear about Haptic suit projects and differentiate in coordinating Haptic suit.

Simulated reality Critical Criteria:

Weigh in on Simulated reality decisions and integrate design thinking in Simulated reality innovation.

– How likely is the current Computer vision plan to come in on schedule or on budget?

– What are the record-keeping requirements of Computer vision activities?

Augmented virtuality Critical Criteria:

Trace Augmented virtuality adoptions and create a map for yourself.

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Computer vision. How do we gain traction?

– What are the top 3 things at the forefront of our Computer vision agendas for the next 3 years?

– Do several people in different organizational units assist with the Computer vision process?

Polyhedron model Critical Criteria:

Canvass Polyhedron model results and adjust implementation of Polyhedron model.

– Why is Computer vision important for you now?

People counter Critical Criteria:

Revitalize People counter outcomes and customize techniques for implementing People counter controls.

Light field Critical Criteria:

Judge Light field adoptions and track iterative Light field results.

– Think about the kind of project structure that would be appropriate for your Computer vision project. should it be formal and complex, or can it be less formal and relatively simple?

Autonomous vehicle Critical Criteria:

Pay attention to Autonomous vehicle risks and budget for Autonomous vehicle challenges.


This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the Computer vision Self Assessment:


Author: Gerard Blokdijk

CEO at The Art of Service | http://theartofservice.com

[email protected]


Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

Computer vision External links:

GumGum | Applied Computer Vision

Computer Vision for Recognizing Human Behavior

Sighthound – Industry Leading Computer Vision

Active contour model External links:

CSOM-CV Active Contour Model Demo #2 – YouTube

algorithm – SNAKES: Active Contour Model – Stack Overflow

Active Contour Model – YouTube

Sega VR External links:

Sega VR-1 (Game) – Giant Bomb

Sega VR – YouTube

Sega-16 – Sega VR: Great Idea or Wishful Thinking?

Content-based image retrieval External links:

CiteSeerX — Content-based Image Retrieval

Content-based image retrieval – SAO/NASA ADS

Medical scanner External links:

Medical Scanner 3D Models for Download | TurboSquid

Advanced Medical Scanner – Other – LoversLab

Star Trek Medical Scanner – YouTube

QR code External links:

The QR Code Generator – Official Site

How to scan the WhatsApp QR code – Quora

Mar 16, 2016 · HOW THE APP WORKS To scan a QR code or barcode simply open the app, point the camera at the code, and you’re done! There is no need to take a …

Video tracking External links:

WebMD Video Tracking Nerve Damage

Wistia Help: Google Analytics Video Tracking

Feature detection External links:

feature detection Study Sets and Flashcards | Quizlet

Modernizr: the feature detection library for HTML5/CSS3

α-shapes for local feature detection – ScienceDirect

The Sword of Damocles External links:

What Did Cicero Mean by the Sword of Damocles?

The Sword of Damocles – Rocky Horror UK – YouTube

What was the sword of Damocles? – Ask History

Free viewpoint television External links:

Free viewpoint television – STANFORD TALKS

Overview of free viewpoint television – ScienceDirect

Signal Processing in Free Viewpoint Television – YouTube

Signal processing External links:

Embedded Signal Processing Laboratory

José Bermudez – Digital Signal Processing

360-degree video External links:

Experience the Blue Angels in 360-degree video – YouTube

Graphics processing units External links:


Solid-state physics External links:

Hole | solid-state physics | Britannica.com

Corner detection External links:

matlab – Harris Corner Detection – Stack Overflow

[PDF]Machine learning for high-speed corner detection

Harris Corner Detection – YouTube

Ridge detection External links:

Ridge Detection – ImageJ

Blob detection External links:

Coin Blob Detection in LabVIEW – Image Processing – YouTube

Blob Detection Using OpenCV ( Python, C++ ) | Learn OpenCV

c# – Emgu CV Blob Detection – Stack Overflow

Microsoft HoloLens External links:

Microsoft HoloLens | The leader in mixed reality technology

Microsoft HoloLens (@HoloLens) | Twitter

Microsoft HoloLens • r/HoloLens – reddit

Oculus Rift External links:

Buy Oculus Rift + Touch – Microsoft Store

Pattern recognition External links:

Mike the Knight Potion Practice: Pattern Recognition

Dora’s Ballet Adventure Game: Pattern Recognition – Nick Jr.

Bin Picking External links:

IRobFeeder – Fast Robotic Bin Picking – Home | Facebook

Mars Exploration Rover External links:

Mars Exploration Rover Mission: People

Mars Exploration Rover Mission: Press Release Images: Spirit

Missions | Mars Exploration Rover – Spirit

Simultaneous localization and mapping External links:

Simultaneous Localization and Mapping (SLAM) – YouTube

[PDF]Simultaneous Localization and Mapping (SLAM) – USNA

[PDF]TUTORIAL Simultaneous Localization and Mapping: Part I

Unmanned aerial vehicle External links:

Unmanned aerial vehicle | military aircraft | Britannica.com

15W Unmanned Aerial Vehicle Operator | National Guard

Amazon.com: Dromida Ominus Unmanned Aerial Vehicle (UAV) Quadcopter Ready-to-Fly (RTF) Drone with Radio System, Batteries and USB Charger (Green): Toys & Games

Samsung Gear VR External links:

Amazon.com: Samsung Gear VR w/Controller (2017) – Latest Edition – Note 8, GS8s, GS7s, Note 5, GS6s (US Version w/ Warranty): Cell Phones & Accessories

Mobile virtual reality is finally here with the Samsung Gear VR, powered by select Samsung phones. Free shipping available. Get it from Verizon.

Samsung Gear VR • r/GearVR – VR on Reddit

Synthetic aperture sonar External links:

Synthetic Aperture Sonar. (eBook, 1971) [WorldCat.org]

[PDF]Synthetic Aperture Sonar System – AUVAC.org
http://auvac.org/uploads/manufacturer_spec_sheet_pdf_sonar/PROSAS Sonar.pdf

Image stitching External links:

Example Image Stitching – BoofCV

Image Stitching – MICROSOFT CORP – Free Patents Online

[PDF]Image Stitching – Isikdogan

Simulated reality in fiction External links:

Simulated reality in fiction – update.revolvy.com
https://update.revolvy.com/topic/Simulated reality in fiction

Simulated Reality in Fiction | LibraryThing

Simulated Reality In Fiction? – Okela

Pervasive game External links:

What is a Pervasive Game? – Simplicable

Optical head-mounted display External links:

EPL202 Fall 2015 Optical Head-Mounted Display – YouTube

Tag: Optical head-mounted display – INTERFACE LOVE.

Optical head-mounted display – YouTube

VR photography External links:

VR Photography – Home | Facebook

NadirPatch.com VR photography tools

Markov random field External links:

GitHub – andreydung/MRF: Markov Random Field

Markov random field – YouTube

[PDF]Estimating a Separably-Markov Random Field …

Image restoration External links:

Photo Restoration Process | Image Restoration | ScanCafe

A Survey on Digital Image Restoration – ScienceDirect

Rejuvenate Image Restoration – Welcome To Rejuvenate …

Machine vision External links:

Lucid Vision Labs | Modern Machine Vision Cameras

Cognex | Machine Vision and Barcode Readers

Image sensor External links:

New 2.3Mp CMOS Digital Image Sensor from ON …

CMOS Image Sensor – Semiconductor & Electronics

IMAGE SENSOR – SK hynix Inc. – Free Patents Online

Computer-human interaction External links:

HICHI: Hawaii Computer-Human Interaction Lab | …

People | HICHI: Hawaii Computer-Human Interaction Lab

CHIFOO is the Computer-Human Interaction Forum of Oregon

MNIST database External links:

MNIST database of handwritten digits — dataset_mnist • keras

Deep Learning with Tensorflow – The MNIST Database – …

Assembly line External links:

Assembly Line – BrainPOP

Fascinating 1936 Footage of Car Assembly Line – YouTube

Assembly line | industrial engineering | Britannica.com

Image recognition External links:

TensorFlow Image Recognition on a Raspberry Pi

Online CAPTCHA Solving and Image Recognition Service.

Best Image Recognition Software in 2018 | G2 Crowd

Magic Leap External links:

Buzzy Startup, Magic Leap, Unveils Augmented Reality …

Magic Leap – reddit

Magic Leap – Official Site

Real-time computer graphics External links:

ERIC – Real-Time Computer Graphics Simulation of …

Ferran Sole | Real-Time Computer Graphics Programming

Visual perception External links:

Visual Perception – theran.de

VISUAL PERCEPTION – Psychology Dictionary

Visual perception (eBook, 2006) [WorldCat.org]

BMVA Summer School External links:

BMVA Summer School (@BmvaCvss) | Twitter

BMVA Summer School – Stuart James

Digital image External links:

American Lock® Digital Image Library

Holy Land Digital Image Collections – dla.library.upenn.edu

Optical flow External links:

New Aosenma CG035 Drone (with Optical Flow) – YouTube

Estimate optical flow using Lucas-Kanade method – MATLAB

Optical Flow – OpenCV documentation index

Edge detection External links:

Edge Detection – MATLAB & Simulink – MathWorks

[1504.06375] Holistically-Nested Edge Detection – arXiv

Canny Edge Detection in OpenCV – OpenCV documentation …

Neural network External links:

Neural Network Console – dl.sony.com

What is bias in artificial neural network? – Quora

Neural Network Console

Virtual graffiti External links:

Virtual Graffiti Wall – Phoenix Amusements

Virtual Graffiti – YouTube

Virtual Graffiti, Inc., Irvine, CA. 31K likes. Virtual Graffiti, Inc helps you make smart IT buying decisions.

Image segmentation External links:

Phd Thesis On Image Segmentation

Image segmentation
http://In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as superpixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze.

Tutorial Image Segmentation – BoofCV

Interest point detection External links:

Lecture 04 – Interest Point Detection – YouTube

Persistent world External links:

Persistent World Mod for Mount & Blade: Warband – Mod DB

3D pose estimation External links:

3D Pose Estimation Using OpenCV – YouTube

[PDF]3D Pose Estimation – Brown University

Digital image processing External links:

Digital Image Processing Introduction – tutorialspoint.com

Digital image processing (Book, 2017) [WorldCat.org]

Digital Image Processing Tutorial

Automated species identification External links:

Re: automated species identification – Google Groups

Automated species identification
http://Automated species identification is a method of making the expertise of taxonomists available to ecologists, parataxonomists and others via computers, mobile devices and other digital technology. The automated identification of biological objects such as insects (individuals) and/or groups (e.g., species, guilds, characters) has been a dream among systematists for centuries.

Artificial neural networks External links:

How Do Artificial Neural Networks Learn? – Futurism

Machine vision glossary External links:

Machine Vision Glossary – RoboRealm

Machine Vision glossary – translationdirectory.com

AVA machine vision glossary. (1985 edition) | Open Library

Haptic suit External links:

Null Space Haptic Suit Demonstation – YouTube

HardLight haptic suit : oculus – reddit

Simulated reality External links:

Simulated Reality and Individual Universes – YouTube

Simulated Reality: The Universe – Solved


Augmented virtuality External links:

augmented virtuality | Business and Management INK

Augmented Virtuality – Dario Laverde – Medium

Augmented Virtuality Prototypes – YouTube

Polyhedron model External links:

16+1 polyhedron models | The collection so far | Flickr

Animated Polyhedron Models – Math Is Fun

Polyhedron Models – Susquehanna University

People counter External links:

AXIS IVM120 People Counter | Axis Communications

Bluefox.io – Smart People Counter

DOORCOUNTS.com: People Counter | The Retail Doorman

Light field External links:

Avegant | Light Field

Light Field Toolbox for MATLAB – Google+

2017 BUD LIGHT FIELD GOAL CONTEST – Charlotte Agenda