CV_Overview

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Overview of Computer Vision:

Overview of Computer Vision CS491E/791E

What is Computer Vision?:

What is Computer Vision? Deals with the development of the theoretical and algorithmic basis by which useful information about the 3D world can be automatically extracted and analyzed from a single or multiple o 2D images of the world.

Computer Vision, Also Known As ...:

Computer Vision, Also Known As ... Image Analysis Scene Analysis Image Understanding

Some Related Disciplines:

Some Related Disciplines Image Processing Computer Graphics Pattern Recognition Robotics Artificial Intelligence

Image Processing:

Image Processing Image Enhancement

Image Processing (cont’d):

Image Processing (cont’d) Image Restoration( e.g., correcting out-focus images)

Image Processing (cont’d):

Image Processing (cont’d) Image Compression

Computer Graphics:

Computer Graphics Geometric modeling

Computer Vision:

Computer Vision

Robotic Vision:

Robotic Vision Application of computer vision in robotics. Some important applications include : Autonomous robot navigation Inspection and assembly

Pattern Recognition:

Pattern Recognition Has a very long history (research work in this field started in the 60s). Concerned with the recognition and classification of 2D objects mainly from 2D images. Many classic approaches only worked under very constrained views (not suitable for 3D objects). It has triggered much of the research which led to today’s field of computer vision. Many pattern recognition principles are used extensively in computer vision.

Artificial Intelligence:

Artificial Intelligence Concerned with designing systems that are intelligent and with studying computational aspects of intelligence. It is used to analyze scenes by computing a symbolic representation of the scene contents after the images have been processed to obtain features. Many techniques from artificial intelligence play an important role in many aspects of computer vision. Computer vision is considered a sub-field of artificial intelligence.

Why is Computer Vision Difficult?:

Why is Computer Vision Difficult? It is a many-to-one mapping A variety of surfaces with different material and geometrical properties, possibly under different lighting conditions, could lead to identical images Inverse mapping has non unique solution (a lot of information is lost in the transformation from the 3D world to the 2D image) It is computationally intensive We do not understand the recognition problem

Practical Considerations:

Practical Considerations Impose constraints to recover the scene Gather more data (images) Make assumptions about the world C omputability and robustness Is the solution computable using reasonable resources? Is the solution robust? Industrial computer vision systems work very well Make strong assumptions about lighting conditions Make strong assumptions about the position of objects Make strong assumptions about the type of objects

An Industrial Computer Vision System:

An Industrial Computer Vision System

The Three Processing Levels:

The Three Processing Levels Low-level processing Standard procedures are applied to improve image quality Procedures are required to have no intelligent capabilities.

The Three Processing Levels (cont’d):

The Three Processing Levels (cont’d) Intermediate-level processing Extract and characterize components in the image Some intelligent capabilities are required.

The Three Processing Levels (cont’d):

The Three Processing Levels (cont’d) High-level processing Recognition and interpretation. Procedures require high intelligent capabilities.

Recognition Cues:

Scene interpretation, even of complex, cluttered scenes is a straightforward task for humans. Recognition Cues

Recognition Cues (cont’d):

How are we able to discern reality and an image of reality? What clues are present in the image? What knowledge do we use to process this image? Recognition Cues (cont’d)

The role of color:

What is this object? Does color play a role in recognition? Might this be easier to recognize from a different view? The role of color

The role of texture:

The role of texture Characteristic image texture can help us readily recognize objects.

The role of shape:

The role of shape

The role of grouping:

The role of grouping

Mathematics in Computer Vision:

Mathematics in Computer Vision In the early days of computer vision, vision systems employed simple heuristic methods. Today, the domain is heavily inclined towards theoretically, well-founded methods involving non-trivial mathematics. Calculus Linear Algebra Probabilities and Statistics Signal Processing Projective Geometry Computational Geometry Optimization Theory Control Theory

Computer Vision Applications:

Computer Vision Applications Industrial inspection/quality control Surveillance and security Face recognition Gesture recognition Space applications Medical image analysis Autonomous vehicles Virtual reality and much more …...

Visual Inspection:

Visual Inspection

Character Recognition:

Character Recognition

Document Handling:

Document Handling

Signature Verification:

Signature Verification

Biometrics:

Biometrics

Fingerprint Verification / Identification:

Fingerprint Verification / Identification

Fingerprint Identification Research at UNR:

Fingerprint Identification Research at UNR Minutiae Matching Delaunay Triangulation

Object Recognition:

Object Recognition

Object Recognition Research at UNR:

Object Recognition Research at UNR reference view 1 reference view 2 novel view recognized

Indexing into Databases:

Indexing into Databases Shape content

Indexing into Databases (cont’d):

Indexing into Databases (cont’d) Color, texture

Target Recognition:

Target Recognition Department of Defense (Army, Airforce, Navy)

Interpretation of Aerial Photography:

Interpretation of aerial photography is a problem domain in both computer vision and photogrammetry. Interpretation of Aerial Photography

Autonomous Vehicles:

Autonomous Vehicles Land, Underwater, Space

Traffic Monitoring:

Traffic Monitoring

Face Detection:

Face Detection

Face Recognition:

Face Recognition

Face Detection/Recognition Research at UNR:

Face Detection/Recognition Research at UNR

Facial Expression Recognition:

Facial Expression Recognition

Face Tracking:

Face Tracking

Face Tracking (cont’d):

Face Tracking (cont’d)

Hand Gesture Recognition:

Hand Gesture Recognition Smart Human-Computer User Interfaces Sign Language Recognition

Human Activity Recognition:

Human Activity Recognition

Medical Applications:

Medical Applications skin cancer breast cancer

Astronomy Applications Research at UNR:

Astronomy Applications Research at UNR Identify radio galaxies having a special morphology called “bent-double” (in collaboration with Lawrence Livermore National Laboratory )

Morphing:

Morphing

Inserting Artificial Objects into a Scene:

Inserting Artificial Objects into a Scene

Computer Vision and Related Courses at UNR :

Computer Vision and Related Courses at UNR CS474/674 Image Processing and Interpretation CS480/680 Computer Graphics CS479/679 Pattern Recognition CS476/676 Artificial Intelligence CS773A Machine Intelligence CS791Q Machine Learning CS7xx Neural Networks CS7xx Computer Vision

More information on Computer Vision:

More information on Computer Vision Computer Vision Home Page http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html Home Page http://www.cs.unr.edu/CRCD UNR Computer Vision Laboratory http://www.cs.unr.edu/CVL

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