Over 3 billion photos are uploaded and shared on the internet every day. Visual content is 40x more likely to be shared on social media than text. What are your customers sharing on social media? And what are they tagging your brand in? And what impact do these visual conversations drive for your brand? You need comprehensive visual analytics to understand what’s really happening on social media.

Join us to learn about NetBase’s next-generation artificial intelligence for image analysis capabilities. We’ll explain how it analyzes visual posts to identify brand logos and keywords to give the most comprehensive view of a brand’s performance. The platform also accurately classifies images by facial emotions, common scenery, and everyday objects to provide the greatest depth of insight.

– More accurately track share of voice and protect your brands reputation by including the analysis of visual brand mentions and understanding their context
– Discover creative inspiration and improve campaign performance through learning what is driving visual engagement and inspiring your customers
– Understand more accurately when, where and how your customers are using your products and services

An iOS app that can detect human emotions, objects and lot more. Made using coreML image detection API.

Thank you for Watching. Please don’t forget to subscribe.

~
## Inspiration

Inspired by a few blind people who use echolocation to “see” things around them, we have developed an interface that would help a blind person listen to have a feeling of where they belong in the society and enjoy the little things and experiences of everyday life.

## What it does

The interface is connected to a camera (which could be then possibly integrated with cameras on pen tips) which records real time videos of events, parse it into multiple frames – analyze it piece by piece and finally using a text to speech interface, dictates what it sees.

## How we built it

For Building the interface, we used the Apple Artificial Intelligence API that contains a pre-trained data set that could be readily used. However on experimenting we learned that real time video/image has a lot of noise, and that it would take a long time to train the data set for practical purposes. Therefore we created a small data set with real time images (taken using phone cameras) and further trained the available data set to a considerable degree of accuracy. With enough time, this can further be implemented and generalized into more diverse data sets, achieving its intended purpose.

## Challenges we ran into

As previously mentioned, we learned that the time it takes to train a simple data set is longer than we had previously anticipated. Therefore we had to restrict our data set and only work towards training specific limited amount of data.
We also tried using the Microsoft Azure API to integrate into our interface – however, we soon learned that we had a few dependency issues that we could not resolve. We wasted over seven to eight hours trying to get that to work. In the end, we moved to using the Apple’s AI API

## Accomplishments that we’re proud of

In the course of this Hackathon, we managed to code and implement successfully in 3 languages – Python, Java and ios. Even though we did not pursue our completed Python project to integrate into an android app (we had to download and learn to use a cross platform program to execute that) – we were able to successfully implement and get positive results in all three languages.

## What we learned

We learned various new methods of coding with AI and Machine Learning Algorithms. We also gained a clearer picture on how to use APIs and integrate them into a common framework that we had in mind. Also, in addition to that, we were also able to learn a bit of implementation using neural networks.

## What’s next for BlindCare

We hope to perfect our code in the future, so that it could be then used in various diverse environments. We are also trying to get to use pre-existing APIs and make the best use of them, therefore extending the reach and impact of BlindCare

This tutorial would help you understand Deep learning frameworks, such as convolutional neural networks (CNNs), which have almost completely replaced other machine learning techniques for specific tasks such as image recognition using large training datasets. In this webinar, we will go over how CNNs, their training methods, and hardware evolved since LeNet first appeared in the late 1990’s. We will examine the challenges that came along, and some key innovations that helped overcome these challenges. We will also look at a guide on how to get started with CNNs, some common pitfalls, and tips and tricks in training CNNs. Advanced Technology Group (ATG) of the CTO Office at NetApp. The ATG group is responsible for investigations, through early product prototypes, and leveraging technologies expected to become mainstream in 3+ years.

About us:
HackerEarth is the most comprehensive developer assessment software that helps companies to accurately measure the skills of developers during the recruiting process. More than 500 companies across the globe use HackerEarth to improve the quality of their engineering hires and reduce the time spent by recruiters on screening candidates. Over the years, we have also built a thriving community of 2.5M+ developers that come to HackerEarth to participate in hackathons and coding challenges to assess their skills and compete in the community.

Download:
The circuit diagram and Project programming can be downloaded by clicking on the link below

Arduino Image Processing based Entrance lock Control System

Download Libraries:
https://www.electroniclinic.com/arduino-libraries-download-and-projects-they-are-used-in-project-codes/

Image Processing based Eyepupil Tracking:
https://youtu.be/xQrTNoSgNDQ

Human machine tracking using image processing:
https://youtu.be/HnKRy26NXMU

Watch other tutorials:

9: Image processing based entrance control system
https://youtu.be/TYllIMfJ3Eg

8: GSM and GPS based car accident location monitoring
https://youtu.be/tumEQioxT6I

7: GSM based GAS leakage detection and sms alert
https://youtu.be/Ar4LowNT_HI

6: Wireless Tongue controlled wheelchair
https://youtu.be/WNCn062YzXc

5: Human Posture Monitoring System
https://youtu.be/6bxZyTi6m-4

4: RFID based bike anti theft system
https://youtu.be/iGtu6TQ-_ao

3: RFID based students attendance system
https://youtu.be/gc4LLN1vftk

2: Piezo Electric generator
https://youtu.be/n6FFnJVq5cQ

1: iot car parking monitoring system
https://youtu.be/tjLAjGi6O5Q

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Project Description:
********************

This is a very detailed tutorial on how to make image processing based human recognition system for entrance controlling. In this project we will be using Arduino uno for controlling the electronic door lock, The Arduino will receive command from the vb.net application when a human will be detected. We will be using xml file for human face detection. This xml file will be used in vb.net application to track a human face. The application designed in vb.net visual basic make use of the emguCv.

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DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I will receive a small commission. This helps support the channel and allows me to continue to make videos like this. Thank you for the support!

About the Electronic Clinic:
Electronic Clinic is the only channel on YouTube that covers all the engineering fields. Electronic Clinic helps the students and workers to learn electronics designing and programming. Electronic Clinic has tutorials on
Gsm based projects ” gsm security system, gsm messages sending and receiving, gsm based controlling, gsm based request data”

wireless projects using bluetooth, radio frequency ” rf ” , ir remote based or infrared remote based.
electronics projects
wheelchair projects
robots
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security systems
pcb designing
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computer desktop applications designing.
email systems.
and much more.

For more Projects and tutorials visit my Website:
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Hello! Today I will show you how to make image recognition bots as fast as possible using Python. I will cover the basics of Pyautogui, Python, win32api and by the end, you should be able to make a bot for pretty much any game.

Here are the commands to run and code to paste: https://github.com/KianBrose/Image-Recognition-Botting-Tutorial/blob/master/README.txt

All code can be found here: https://github.com/KianBrose/Image-Recognition-Botting-Tutorial

If this video helped you please consider subscribing and leaving a like, it helps a ton!

If you have any errors/suggestions please let me know!

Discord server: https://discord.com/invite/8NcumxN

When you tap on Flow, the camera activates and Flow begins to analyze the objects you put in front of it.

There are many ways Glass can help simplify your life. And one of those daily chores is Grocery shopping. Either from the grocery store or right in your home, Glass, Cloud and Catchoom’s Image Recognition come together to make the perfect application of Glass in your every day life. Either from the grocery store – shop, scan, leave and your products get delivered to your home — or directly from your kitchen. This video shows how easy it is to grocery shop with Glass from your kitchen. Scan the product you need to replace, put it in your shopping cart, pay, check out and schedule delivery right from you kitchen. Glass, powered by Catchoom Glass SDK, puts your life right at your fingertips. Learn more about CraftAR Image Recognition: http://catchoom.com/solutions/image-recognition/

The new Moultrie camera system features an updated MV2 modem and an integrated camera which is the XV7000i. It also features an Image recognition software that allows you to sort images by their content.

Introducting EarthCam’s AI-powered recognition technology for identifying obstructions and performing quality control for premium time-lapse content. Using its newly-developed AI algorithms, EarthCam is currently able to process over half a million high-resolution images a day to detect if camera images are obscured by foreign objects, dirt, fog or have the presence of rain droplets on the lens. The smart software looks for 16 different components in an image, both desirable and unwanted features, and then creatively re-edits the video. Cost savings are immediately realized with instant access to presentation-ready time-lapses, free of expensive editing processes and production wait times. Clients will still enjoy hand-edited time-lapse videos at the end of their project and can now download entertaining AI-edited movies at any time on-demand. The unique videos come complete with music and on-screen graphics, to present informative updates to stakeholders and share social media-ready content for public outreach.

Augmented Reality image recognition with Vuforia SDK. Placing 3D animated model on top of recognized image pattern inside of school book. www.creatifesprit.asia

Take a look at Junaio Glue’s new feature – the ability to recognize images in real-time and overlay objects on that image. The feature has been available for Android, but only with iOS 4 has the iPhone been able to do image recognition in augmented reality (AR) apps like Junaio.

Use Bixby Image Recognition to identify and find similar images of any picture on your phone or taken with your camera. Great for identifying unknown plants, toys, objects and more.
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Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML

A friendly explanation of how computer recognize images, based on Convolutional Neural Networks.
All the math required is knowing how to add and subtract 1’s. (Bonus if you know calculus, but not needed.)
For a brush up on Neural Networks, check out this video: https://www.youtube.com/watch?v=BR9h47Jtqyw

In this video am going to show a new development board, the Sipeed M1 Dock which features the revolutionary $10 K210 AI chip. Just like the ESP32, this chip is going to change everything, bringing hardware AI to the maker community.

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You can download my latest Android Game which is called Quiz of Knowledge here:

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notes-https://viden.io/knowledge/image-processing-1

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CamFind is an iOS app that utilizes image recognition to power its visual search.

It means that you no longer have to type queries into a search engine to find information, instead you can simply take a picture!

CamFind’s search process begins once the user takes a picture of an object of interest ­at any angle. The app then identifies the object and presents an intent screen with the following modules:

• Internet Search Results
• Related/Similar Images
• Price Comparisons and Online Shopping
• Related Places and Address Finder
• Film Poster/DVD Recognition
• Social Sharing

CamFind also offers the following time-saving functions:

• Language Translator
• QR and Barcode Reader
• Text Search
• Voice Search
• Voiceover of Identified Objects
• Automatic Flash
• Automatic Focus
• Ability to Save Images to the Camera Roll/Upload Images from Camera Roll

Visit our website to learn more: http://CamFindApp.com

Follow us: http://Twitter.com/CamFind or LIKE us on http://Facebook.com/CamFind

App For Apple App Store….
http://goo.gl/yfF75
App For Google Play:
http://bit.ly/1drpKIJ

CamFind is an iOS app that utilizes image recognition to power its visual search.

It means that you no longer have to type queries into a search engine to find information, instead you can simply take a picture!

CamFind’s search process begins once the user takes a picture of an object of interest ­at any angle. The app then identifies the object and presents an intent screen with the following modules:

• Internet Search Results
• Related/Similar Images
• Price Comparisons and Online Shopping
• Related Places and Address Finder
• Film Poster/DVD Recognition
• Social Sharing

CamFind also offers the following time-saving functions:

• Language Translator
• QR and Barcode Reader
• Text Search
• Voice Search
• Voiceover of Identified Objects
• Automatic Flash
• Automatic Focus
• Ability to Save Images to the Camera Roll/Upload Images from Camera Roll

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Looking for a quick way to use technology to help you ensure that your products are being displayed accurately at customer sites? Image recognition compares your predefined planogram with your pictures of the actual shelf display and calls out any suggestions for changes.

Monet or Picasso? In this episode, we’ll train our own image classifier, using TensorFlow for Poets. Along the way, I’ll introduce Deep Learning, and add context and background on why the classifier works so well. Here are links to learn more, thanks for watching, and have fun!

TensorFlow for Poets Codelab: https://goo.gl/QTwZ3v

Google’s Udacity class on Deep Learning: https://goo.gl/iRqXsy

TensorFlow tutorial: https://goo.gl/0Oz7B5

Google Research blog on Inception: https://goo.gl/CSrfJ1

You can follow me on Twitter at https://twitter.com/random_forests for updates on episodes, and of course – Google Developers.

Subscribe to Google Developers: http://goo.gl/mQyv5L –
Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG
And here’s our playlist: https://goo.gl/KewA03

World-class image recognition service for smartphones – NEC GAZIRU
http://www.diginfo.tv/v/12-0100-r-en.php

DigInfo TV – http://diginfo.tv

30/5/2012 Wireless Japan 2012

NEC
GAZIRU

http://cnet.co/1ngoPB0
The new wave of cameras is changing how you use your phone and tablet; why are batteries so far behind the advancement of other technology? And are tablets already hitting their plateau?

Pixy2 from DFRobot – https://www.dfrobot.com/product-1752.html
Full article with code at https://dbot.ws/pixy2
More articles at https://dronebotworkshop.com
Tell me what videos YOU want me to make for You! Join the newsletter at https://dbot.ws/dbnews

The Pixy2 is a low cost yet powerful camera that is capable of object recognition, line tracking and simple barcode reading. The device is the latest iteration of the Pixy Cam, a project built by Charmed Labs in conjunction with the Robotics Institute at Carnegie Mellon University.

With a variety of interfaces and lots of code libraries and samples the Pixy2 can be used with an Arduino, Raspberry Pi, Beaglebone Black or just about any computer, microcomputer or microcontroller.

In this video I will show you how the Pixy2 works, how to hook it up and how to train it using software called PixyMon which runs on Windows, Linux and Mac OS X. I’ll then show you how to easily hook up your Pixy2 to an Arduino and run code to detect object, lines, intersections and simple barcodes.

Thanks to its onboard processor the coding for Pixy2 is very simple. It provides a very easy method of adding vision to your next Arduino or Raspberry Pi project.

Here is the table of contents for this video:

Pixy2 Introduction – 2:18
Pixy2 Unboxing – 5:53
Using PixyMon – 8:02
Color Signature Training – 14:01
Arduino Hookup – 20:32
CCC Hello World Demo – 21:48
Line Tracking Intro – 27:44
Line Hello World Demo – 29:24

As always there is a detailed article on the DroneBot Workshop website, you will find it here – https://dbot.ws/pixy2. And while you are there please consider joining my newsletter, it’s my way of keeping in touch and finding out what videos and articles you would like me to make for you – https://dbot.ws/dbnews.

NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet aims to provide many of the tools, functionality and implementations that are essential for medical image analysis but missing from standard general purpose toolkits. Due to its modular structure, NiftyNet makes it easier to share networks and pre-trained models, adapt existing networks to new imaging data, and quickly build solutions to your own image analysis problems. This talk will explore the whys, the whats and the hows of this open source framework.

I have a BSc in Biomedical Engineering (2006) and an MSc in Medical Electronics and Signal Processing for Biomedical Engineering (2008) from the Universidade do Minho, Portugal, followed by a PhD (2008-2012) and PostDoc (2012-2015) in medical image analysis, machine learning and biomarker development between CMIC and the Dementia Research Centre at UCL. In June 2015 I have been appointed Lecturer in Quantitative Neuroradiology at the Translational Imaging Group, part of CMIC, in collaboration with the National Hospital for Neurology and Neurosurgery, working on developing, translating and integrating artificial intelligence-based quantitative imaging biomarkers into the clinical environment.

In this article i will show you, how to compare two images in asp.net c# like finger print biometric system. In this system i used base64String method to convert the stream object into string.

Vegetables Recognition Using Image Processing on Android Device
Student Project 2016
by
Ratchawut Keunmamuang
Sethikarn Shotuk

Link To App: https://x.thunkable.com/copy/1f65f20d964f3f9477bf5a6836e17a0e
Learn how to build a camera app and an image recognition app!

http://cnet.co/1ngoPB0
Brian Cooley discusses rise of image recognition software on smartphones and tablets, and why the technology is gaining momentum in the retail market.

With a few pictures taken in a retail store, the Vispera Image Recognition Services is able to identify products on shelves, report out-of-stock SKUs and check planogram compliance. This is a modern all-in-one approach for integrated retail execution and auditing tasks powered by Vispera Image Recognition Technology.

We think FMCG producers and distributors may very much enjoy what we’re bringing to the domain.

http://vispera.co

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HOW TO MAKE IMAGE RECOGNITION BOT VERY EASY AND FUN TUTORIAL WITH SIKULI

by Dolce Panna
Download Java from here
http://www.java.com/en/download

Download Sikuli from here
https://launchpad.net/sikuli/sikulix/x1.0-rc3/+download/Sikuli-X-1.0rc3%20%28r905%29-win32.exe

Google Tech Talks
December, 13 2007

ABSTRACT

This tech talk series explores the enormous opportunities afforded by the emerging field of quantum computing. The exploitation of quantum phenomena not only offers tremendous speed-ups for important algorithms but may also prove key to achieving genuine synthetic intelligence. We argue that understanding higher brain function requires references to quantum mechanics as well. These talks look at the topic of quantum computing from mathematical, engineering and neurobiological perspectives, and we attempt to present the material so that the base concepts can be understood by listeners with no background in quantum physics.

In this second talk, we make the case that machine learning and pattern recognition are problem domains well-suited to be handled by quantum routines. We introduce the adiabatic model of quantum computing and discuss how it deals more favorably with decoherence than the gate model. Adiabatic quantum computing can be understood as an annealing process that outperforms classical approaches to optimization by taking advantage of quantum tunneling. We also discuss the only large-scale adiabatic quantum hardware that exists today, built by D-Wave. We present detailed theoretical and experimental evidence showing that the D-Wave chip does indeed operate in a quantum regime. We report about an object recognition system we designed using the adiabatic quantum computer. Our system uses a combination of processing steps, where some are executed on classical hardware while others take advantage of the quantum chip. Both interest point selection and feature extraction are accomplished using classical filter operations reminiscent of receptive field properties of neurons in the early visual pathways. Image matching then proceeds by maximizing geometrical consistency and similarity between corresponding feature points, which is an NP-hard optimization problem. To obtain good solutions, we map this to the problem of finding the minimum energy in an Ising model in which the vertices represent candidate match pairs, bias terms reflect feature similarity, and interaction terms account for geometric consistency. The adiabatic quantum computer is then employed to find a low energetic minimum of the Ising dynamics. We conclude with a look towards which type of machine learning problems maybe most suitable for mapping to a quantum computing architecture.

Speaker: Hartmut Neven
Speaker: Dr. Geordie Rose
Geordie Rose is a founder and CTO of D-Wave. He is known as a leading advocate for quantum computing and physics-based processor design, and has been invited to speak on these topics in venues ranging from the 2003 TED Conference to Supercomputing 2005.

His innovative and ambitious approach to building quantum computing technology has received coverage in BC Business, The Vancouver Sun, Vancouver magazine, The Globe and Mail, The National Post, USA Today, MIT Technology Review magazine, the Harvard Business Review and Business 2.0 magazine, and one of his business strategies was profiled in a Harvard Business School case study. He has received several awards and accolades for his work with D-Wave, including being short-listed for a 2005 World Technology Award.

Dr. Rose holds a PhD in theoretical physics from the University of British Columbia, specializing in quantum effects in materials. While at McMaster University, he graduated first in his class with a BEng in Engineering Physics, specializing in semiconductor engineering.

Since the inception of D-Wave in 1999, Dr. Rose, as founding CEO, raised over $45M on behalf of the company, including a round led by Draper Fisher Jurvetson (DFJ) — the first ever investment by a top-tier US venture capital firm in quantum computing.

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Here’s my updated AI E.D.I.T.H glasses from the new Marvel movie Spider-Man Far From Home. These glasses feature a camera, a Raspberry Pi, and a mini HDMI display. The Smart glasses will take images every 10 seconds, upload them to be processed, and return information about the images such as objects found, text, and more.
Then the information is displayed right on the mini HDMI screen. In the future these can be used to automatically get direction based on street signs, do facial recognition, or any number of artificial intelligent tasks. Enjoy!

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Provides steps for applying Image classification & recognition with easy to follow example.
R file: https://goo.gl/fCYm19
Data: https://goo.gl/To15db
Machine Learning videos: https://goo.gl/WHHqWP
To install EBimage package, you can run following 2 lines;
install.packages(“BiocManager”)
BiocManager::install(“EBImage”)
Uses TensorFlow (by Google) as backend. Includes,
– load keras and EBImage packages
– read images
– explore images and image data
– resize and reshape images
– one hot encoding
– sequential model
– compile model
– fit model
– evaluate model
– prediction
– confusion matrix

Image Classification & Recognition with Keras is an important tool related to analyzing big data or working in data science field.

R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

More info: http://www.csail.mit.edu/fooling_neural_networks_with_3Dprinted_objects
http://www.labsix.org/physical-objects-that-fool-neural-nets/
Paper: https://arxiv.org/pdf/1707.07397.pdf

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