Image Processing and Computer Vision in iOS
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Image Processing and Computer Vision in iOS

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Invited talk at iOS Day 2013 (Uberaba, MG, Brazil)

Invited talk at iOS Day 2013 (Uberaba, MG, Brazil)

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Image Processing and Computer Vision in iOS Image Processing and Computer Vision in iOS Presentation Transcript

  • Image  Processing  and     Computer  Vision  in  iOS   Oge Marques, PhD omarques@fau.edu Uberaba, MG - Brazil 18 December 2013
  • Take-home message Mobile image processing and computer vision applications are coming of age. There are many opportunities for building successful apps that may improve the way we capture, organize, edit, share, annotate, and retrieve images and videos using iPhone and iPad.
  • Disclaimer #1 •  I'm a teacher, researcher, graduate advisor, author, … •  … not a developer
  • Disclaimer #2 •  I'm a trained engineer •  … not an artist / designer
  • Disclaimer #3 •  I'm an Apple fan! –  Since 2001… •  4 iPod, 3 iPhone, 2 iPad, 2 iMac, 4 MacBook, and more (AirPort, AirPort Express, Apple TV, etc.) –  Since 2010… •  Created and co-taught iOS Programming classes at FAU
  • In 2013… •  1.4 billion people have a smartphone with camera •  350 million photos uploaded to Facebook every day •  Instagram reaches 150 million users, with a total of 16 billion photos shared and 1 billion likes each day
  • In 2013… •  "Selfie" was the Oxford Dictionary's new word of the year
  • And speaking of new words…
  • Background and Motivation Two  sides  of  the  coin     •  The maturity and popularity of image processing and computer vision techniques and algorithms •  The unprecedented success of mobile devices, particularly the iPhone and the iPad
  • Motivation •  Rich capabilities of iPhone/iPad for image and video processing •  Apple support for image and multimedia: frameworks, libraries, etc. •  Third-party support for iPhone-based development: open APIs, OpenCV, etc. •  Success stories and ever-growing market
  • Motivation •  Q: Why DIP and CV? •  A: Because they are still relevant and growing fields whose techniques and can help solve many problems. •  Q: Why iOS / mobile? •  A: Because some problems are better solved in that context and some still need to be solved in a away that is consistent with ergonomics (devices' size etc.) and user needs ("quick fix" + filter before sharing).
  • search. As an example, we then present the Stanford Product Search system, a low-latency interactive visual search system. Several sidebars in this article invite the interested reader to dig deeper into the underlying algorithms. each query feature vector with all t base and is the key to very fast retr features they have in common wit of potentially similar images is sele Finally, a geometric verificatio most similar matches in the datab spatial pattern between features of didate database image to ensure Example retrieval systems are pres For mobile visual search, ther to provide the users with an int deployed systems typically transm the server, which might require t large databases, the inverted file in memory swapping operations slow ing stage. Further, the GV step and thus increases the response t the retrieval pipeline in the follow the challenges of mobile visual se Example: a natural use case for CBIR ROBUST MOBILE IMAGE RECOGNITION Today, the most successful algorithms for content-based image retrieval use an approach that is referred to as bag of features (BoFs) or bag of words (BoWs). The BoW idea is borrowed from text retrieval. To find a particular text document, such as a Web page, it is sufficient to use a few well-chosen words. In the database, the document itself can be likewise represented by a •  Content-Based Image Retrieval (CBIR) using the "Query-By-Example" (QBE) paradigm –  The example is right there, in front of the user! Query Image [FIG1] A snapshot of an outdoor mobile visual search system being used. The system augments the viewfinder with information about the objects it recognizes in the image taken with a camera phone. Feature Extraction [FIG2] A Pipeline for image retrieva from the query image. Feature mat images in the database that have m with the query image. The GV step feature locations that cannot be pl in viewing position.
  • Example: Stanford DIP class •  Course page: http://www.stanford.edu/class/ee368/index.html •  YouTube playlist
  • iPhone photo apps •  400+ photo- and video-related apps available in iTunes store –  Entire sites for reviews, discussions, etc. –  Subcategories include: •  Camera enhancements •  Image editing and processing •  Image sharing •  Image printing, wireless transfer, etc.
  • An app about apps
  • iPhone photo apps •  Fresh from the oven…
  • iPhone photo apps
  • iPhone photo apps
  • Developing DIP/CV apps for iOS •  Checklist: –  Get a Mac running OS X –  Sign up to become a registered iOS developer –  Download / install xCode and latest version of iOS SDK –  Download / install iOS simulator –  Learn Objective-C and the basics of iOS programming –  Get an iPhone, iPod Touch, or iPad (optional)
  • Developing DIP/CV apps for iOS •  Topics to study in greater depth: –  The main classes that you need to understand in order to develop basic applications involving images, camera, and photo library for the iPhone are: •  UIImageView •  UIImagePickerController •  UIImage –  Check out the documentation for the A/V foundation
  • Developing DIP/CV apps for iOS •  Topics to study in greater depth (cont'd): –  Learn about Core Image and its main classes: •  CIFilter: a mutable object that represents an effect. A filter object has at least one input parameter and produces an output image. •  CIImage: an immutable object that represents an image. •  CIContext: an object through which Core Image draws the results produced by a filter.
  • Core Image •  Image processing and analysis technology designed to provide near real-time processing for still and video images. •  Hides the details of low-level graphics processing by providing an easy-to-use API. •  Brought into iOS since iOS 5 (Oct'11)
  • OpenCV •  OpenCV (Open Source Computer Vision) is a library of programming functions for realtime computer vision. •  OpenCV is released under a BSD license; it is free for both academic and commercial use. •  Goal: to provide a simple-to-use computer vision infrastructure that helps people build fairly sophisticated vision applications quickly. •  The library has 2000+ optimized algorithms. –  It is used around the world, has >2M downloads and >40K people in the user group.
  • OpenCV •  5 main components: 1.  CV: basic image processing and higher-level computer vision algorithms 2.  ML: machine learning algorithms 3.  HighGUI: I/O routines and functions for storing and loading video and images 4.  CXCore: basic data structures and content upon which the three components above rely 5.  CvAux: defunct areas + experimental algorithms; not well-documented.
  • OpenCV and iOS Example Contrast  with  equivalent  func=onality  using  Core  Image  
  • A bit of advice… •  Go beyond the DIP/CV and iOS boxes –  Learn about ergonomics, human factors, human psych, HCI, UX •  Don't reinvent the wheel! –  Reuse code and ideas whenever possible •  Avoid the trap of building solutions looking for problems •  Tackle ONE problem and solve it well! •  Beware of competition •  Beware of narrow windows of opportunity and ephemeral success: timing is everything!
  • Learn more about it •  iOS Programming –  Apple online documentation –  Core Image •  OpenCV –  Official website –  "Learning OpenCV" book –  "Instant OpenCV for iOS" book •  Our work –  Slideshare (WVC 2011) –  Upcoming book (Springer Briefs, 2014)
  • Concluding thoughts •  Mobile image processing, image search, and computer vision-based apps have a promising future. •  There is a great need for good solutions to specific problems. •  I hope this talk has provided a good starting point and many useful pointers. •  I look forward to working with some of you!
  • Let's get to work! •  Which computer vision or image processing app would you like to build? •  Contact me with ideas: omarques@fau.edu