COMPUTER VISION Team B MTH 221 Church Mickel

1. COMPUTER VISIONTeam B MTH 221Church Mickel Tamara Reveron, Sammy Hinson, Shawn Gonsalves, Tony Desero, Pierre Wyrick, Ron Fowler

2. Table of Content • Introduction • Computer Vision • Applications Medical Application Industrial Application Military Application • Fundamentals, Notations and Concepts Enumeration Application Algorithm Algorithmic Concepts Application Graph theory Trees Data Structure • List of Applications • Overview: Relations of Computer Vision and other fields

3. Introduction • In this presentation we will review what Computer Vision is, what are some of the use and purposes, and the relationship it has with other fields within artificial intelligence. • We will also discuss, in brief, certain mathematical properties that are used in Computer Vision. • In conclusion we will attempt to show how these properties/systems come together in an effort to create what we call COMPUTER VISION.

4. Computer Vision • Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. • It has also been described as the enterprise of automating and integrating a wide range of processes and representations for vision perception. • As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a medical scanner.

5. Applications • Medical Application • Industrial Application • Military Application

6. Medical Application • One of the most prominent application fields is medical computer vision or medical image processing. This area is characterized by the extraction of information from image data for the purpose of making a medical diagnosis of a patient. Generally, image data is in the form of microscopy images, X-ray images, angiography images, ultrasonic images, and tomography images.

7. Industrial Application • Computer vision used in industry is sometimes called machine vision. This is where information is extracted for the purpose of supporting a manufacturing process. One example is quality control where details or final products are being automatically inspected in order to find defects.

8. Military Application • Military applications are probably one of the largest areas for computer vision. The obvious examples are detection of enemy soldiers or vehicles and missile guidance. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors, including image sensors, provide a rich set of information about a combat scene which can be used to support strategic decisions.

9. Fundamentals, Notations& Concepts • Enumeration • Algorithm • Graph theory • Trees

10. Enumeration • An enumeration type (also named an enumeration or an enum) provides an efficient way to define a set of named integral constants that may be assigned to a variable. • For example, assume that you have to define a variable whose value will represent a day of the week. There are only seven meaningful values which that variable will ever store. To define those values, you can use an enumeration type, which is declared by using the enumkeyword.

11. Application • Assume that you have to define a variable whose value will represent a day of the week. There are only seven meaningful values which that variable will ever store. To define those values, you can use an enumeration type, which is declared by using the enum keyword. • enum Days { Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday }; • enum Months : byte { Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec }

12. Algorithm • An algorithm is an effective method expressed as a finite listof well-defined instructionsfor calculating a function. • Starting from an initial state and initial input (perhaps empty),the instructions describe a computation that, when executed, proceeds through a finite number of well-defined successive states, eventually producing "output“ and terminating at a final ending state. • The transition from one state to the next is not necessarily deterministic; some algorithms, known as randomized algorithms, incorporate random input.

13. Algorithmic ConceptsApplications • Algorithm development are essential for software development. • An algorithm defines the steps necessary to provide a solution a given task. • An algorithm is a step-by-step method for solving a problem, the programmer must provide the order and instructions to solve the problem.

14. Graph theory • Branch of mathematics concerned with networks of points connected by lines. The subject of graph theory had its beginnings in recreational math problems but it has grown into a significant area of mathematical research with applications in chemistry, operations research, social sciences, and computer science.

15. Trees • A tree can be defined recursively (locally) as a collection of nodes (starting at a root node), where each node is a data structure consisting of a value, together with a list of nodes (the "children"), with the constraints that no node is duplicated. • It can also be defined as a mathematical structure that can be viewed as either a graph or as a data structure. The two views are equivalent, since a tree data structure contains not only a set of elements, but also connections between elements, giving a tree graph.

16. Data Structure It is also a set of straight line segments connected at their ends containing no closed loops (cycles). In other words, it is a simple, undirected, connected, acyclic graph (or, equivalently, a connected forest). A simple unordered tree; in this diagram, the node labeled 7 has two children, labeled 2 and 6, and one parent, labeled 2. The root node, at the top, has no parent.

17. Applications • The following is a non-complete list of applications which are studied in computer vision. In this category, the term application should be interpreted as a high level function which solves a problem at a higher level of complexity. Typically, the various technical problems related to an application can be solved and implemented in different ways.

18. List of Applicable Area

19. Relationship of Computer Vision and other fields

20. Conclusion • Computer Vision has been explained and how it is applied to “real world” situations by showing the applications within the medical, industrial and military arenas. • Examples of uses in other areas and presented a visional chart of the relationship it has with other fields. • Examples of fundamentals and notations {Enumeration, Algorithm, Graph theory and Trees} used in computer vision defined. This give understanding as to how these elements come together and show how evident it is that one is an “extension” of the other, in other words “one relates and works within the other.”

21. References • http://msdn.microsoft.com/en-US/library/vstudio/cc138362.aspx • http://en.wikipedia.org/wiki/Algorithm • http://www.britannica.com/EBchecked/topic/242012/graph-theory • http://en.wikipedia.org/wiki/Computer_vision • http://en.wikipedia.org/wiki/Tree_(computer_science) • http://mathworld.wolfram.com/Tree.html • http://en.wikipedia.org/wiki/Category:Applications_of_computer_vision

22. QUESTIONS