intelligent image processing
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Jake Larrimore, Andrew Stewart, Esteban Bernal
Intelligent Image Processing
What will be discussed?
1. What is Image Processing?2. History3. Push- Consumer driven 4. Pull- Industry (internally driven)5. How does Image Processing work?6. Advantages and Disadvantages7. Current Applications8. State of the art9. Future direction
What is Intelligent Image Processing
•Well, can anyone define Intelligence?•A rat can process visual data and
interpret it in order to solve problems, but we would not consider a rat intelligent in comparison to a human. Yet this simple task is extraordinarily difficult to program a computer to do, and we have come nowhere close to enabling computers to match the image processing perceptibility of rats.
Image Processing Defined
•Computer vision broadly refers to the discipline where extraction of useful 2D and/or 3D information from one or more images is of interest [Chellepe et. al, 2005]
•Many computers and hand held devices have cameras embedded in them, but they do not process that information to perform a task, therefore we cannot say that such a device has vision
History
•Artificial Intelligence•We live in a three-dimensional and
dynamic world. Therefore, in order for a robot or other A.I. artifact to interact with its surroundings, it must be able to obtain and process information through some sort of sensing ability.
Defining the field
•What information should be extracted from the outputs of visual sensors
•How is this information extracted•How should this information be
represented•How must this information be used to
allow a robot system to perform its task [Faugeras, 1949]
New Fields
•Neuromorphic Engineering - recreate the way the eye and other neurobiological sensing systems work and applying it to silicon chips.
• Imaging device must contend with shadows and sunlight; conventional sensors, such as those in digital cameras, can't capture pictures well under these conditions
Push
•We now have regular access to computers with dual core processors, and some with multi-level processors that can manage multiple GHz.
•high speed networking
Pushing, continued• artificial neural networks
▫mathematical models derived from biological neural networks
• After the development of the “back-propagation learning algorithm for neural networks, [it was for the] first time...feasible to train a non-linear neural network equipped with layers of the so-called hidden nodes [Egmont-Petersena et. al, 2001].”▫preprocessing images, image reconstruction, image
restoration, image enhancement, data reduction and feature extraction, and image compression
Pull
•Robotic Vision▫Developed to give autonomous robots the
ability to interact freely with their environment
▫Some scientists say that “autonomous navigation has become a mandatory function of mobile intelligent robots [Kim et. al, 2008].”
Pull- Defense and Security
•The ability to detect threats to the public without human interaction would be vital to reducing the cost, time, and efficiency of such security▫TSA and Airports▫Crowd Control/Riot Control▫Tracking of Fugatives
Pull- Safety on the Roads• Traffic and automobiles
implemented in automobiles, traffic lights, and city streets
•National Highway Traffic and Safety Administration reports that there were a reported 6.4 million car accidents on the streets of the U.S. causing over 230 billion dollars in damage. These accidents killed almost 30 thousand people and injury about 2.9 million people
Pull-Medical Fields • (CT) scans are generally used to make the
diagnostic and to plan the surgery for liver cancer▫radiologist must trace the contour of the liver
manually as well as the tumor and the main vessels (which show up very similarly on scans)
• If we had intelligent processes to investigate these scans to more accurately determine the condition of patients and to enhance the scans to produce better and more vivid images
Still Pulling- Manufacturing Automation•computer–based machine vision system
applied in computer-aided inspection- Chips, coffee beans, etc
•Safer work areas to ensure that workers are not injured by automated devices
Pull- ENTERTAINMENT!• 20 percent of households with more than $77,000 a
year in pretax income, more money is spent on entertainment - $4,516 a year - than on health care, utilities, clothing or food eaten at home [Darlin, 2005]
• Billions of dollars driving the market toward more user friendly computer interaction
• We should be able to communicate in a more intuitive manner, directly with a context-aware environment, thus enabling them to achieve their goals more easily and freeing their minds to think even further ahead of their current tasks and problems [Meyer et al, 2003]
More Entertainment
•QB1-They are using multiple cameras to achieve depth perception in computers, which enables them to have an interface based the user directly touching and manipulating virtual components positioned around his body
•Applications in gaming
Basic Idea
•Intelligent / non-intelligent
•Humanistic Intelligence: Recognizing that the human brain is perhaps the greatest neural network of its kind ▫WearComp▫Eyetap
Basic Idea - WearComp
•“Always ready" device•Six informational paths of interaction
▫ Unmonopolizing of user’s attention▫ Attentive to the environment▫ Communicative to others▫ Unrestrictive▫ Observable▫ Controllable
Basic Idea - Eyetap• Lightspace analyzer• Lightspace modifier• Lightspace synthesizer
Computer and Machine Vision
•CV: "the science and technology of machines that see, where see in this case means that the machine is able to extract information from an image that is necessary to solve some task” - Wikipedia
•CV: focus on the complex real-world situations
•MV: focus on machines that can see
Image Processing Chain (IPC)
•Describe the steps and operations involved to successfully extract data from an image
•General operations utilized across different image processing systems:
IPC – Pre-processing
•Suppress unwilling distortions•Enhance Important features•Divided in three operations
▫Reconstruction▫Restoration▫Enhacement
IPC - Segmentation•Partitioning into correlated and
not overlapped fragments• Statistical pattern recognition • Neural networks
IPC – Object Recognition
•Requires knowledge•Knowledge representation:
▫grammars and languages▫predicate logic▫production rules▫fuzzy logic▫semantic nets▫frames and scripts
IPC – Object Recognition
IPC – Image understanding
•Find a relation between the input images and previously established models of the real world [Sonka et al, 2008]
•eTRIMS project (University of Bonn)
Performance: Advantages
•Technology improvements storage processing power bandwidth and wireless access image resolution Supercomputing processing
• Facilitate human's life (Google goggles)• Improve human's life (Medical usage, traffic
safety)• Improve economic (Manufacturing)
Performance Disadvantages
•Some technologies are expensive To develop To maintain
• Reduce the need of human work?• Technical difficulties
Lost of information Interpretation Noise Too much data
Applications
•Wherever you can image - Just a few examples:
Automotive industry pedestrian detection Potato chips image processing system to
control quality Medical applications - diseases detection Traffic control Autonomous driven cars
•Limitations? Human's ability of understanding the brain
The state of the art
•Image processing formerly the domain of large institutions
•Very specific applications
•Large projects
•Imaging technology is now widely available
•Consumer products
Google Goggles
•First came text-based text searching•Then came text-based image searching•Now image-based image searching
Microsoft Photosynth
•Stitches together a three-dimensional scene from several images of the same subject
•Creates a navigable scene
Microsoft Photosynth
Autonomous Driving
•An example of computer vision•Norman Bel Geddes’ Futurama (1939)
•Still a ways off…
Lane Departure Warning System
Lane Departure Warning System•Canny edge detection algorithm
•Line extraction by Hough transformation
Pedestrian tracking system
•Shape-based voting algorithm•Similar Gaussian and Hough methods•Other applications
▫Automatic doors▫Light usage (efficiency)▫Cash register (security)
Hand & gesture tracking
•Control of entertainment systems▫XBox Kinnect
•Sign language
Face detection
•Many applications▫Bankcard identification▫Access control▫Security monitoring▫Biometrics systems
•Advancement based on▫Large image databases▫Advances in algorithms▫Methods for evaluating performance
•More difficult than simple line detection
Face detection
•Traditional methods▫PCA▫Neural networks▫Sparse graph
matching▫HMMs▫Template matching
•Newer methods▫Improved template
matching w/ 3D models
▫Line Edge Map (LEM)
▫SVMs
Face recognition
Face recognition
Medical imaging
•Computerized tomography•Magnetic resonance imaging•Ultrasound•Nuclear medicine imaging•Computerized hematological cell analysis
Medical imaging
•Knowledge based systems▫Rule based expert systems▫Structural-functional correlation▫Artifact reduction
•Trending towards convergence of artificial intelligence and image analysis
Looking ahead…
Hyperspectral imaging
•Beyond the visible spectrum
Hyperspectral imaging
Hyperspectral imaging
Hyperspectral imaging
LIDAR
•Light Detection and Ranging•Remote optical sensing technology•Three-dimensional contoured imaging
LIDAR
LIDAR
Biologically motivated processing
Nonlinear methods
•Linear methods OUT▫Human visual system too complicated for
linear models▫Have difficulty removing unwanted noise
•Nonlinear methods IN▫Generally superior in edge smoothing,
enhancement, filtering, feature extraction, etc
▫Computationally expensive▫Reduced cost makes these practical and
effective