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1372 results in Ebooks on robotics

Page 8 of 69

  • For the Instructor

  • Wesley E. Snyder, North Carolina State University, Hairong Qi, University of Tennessee
  • Book:
    Fundamentals of Computer Vision
    Published online:
    25 October 2017
    Print publication:
    28 September 2017, pp xv-xvi

  • Summary

    This is a first course in Computer Vision. It is intended for seniors or first-level graduate students in math-intensive curricula, such as electrical engineering, computer engineering, math, physics, or computer science.

    While this book provides a “foundation” in computer vision, a foundation in universitylevel mathematics is required for this book. We suggest that all students have the usual three-semester course in differential calculus, at least one course in differential equations, and one or two courses in concepts of matrices and linear algebra.

    The authors feel strongly that a student in Computer Vision should have a reasonably good grasp of what the computer is actually doing – and that means programming. While MATLAB is a fantastic research tool, and provides wonderful rendering tools, using MATLAB may prevent the student from gaining this insight into the basic operations (unless, of course, the student actually writes MATLAB code and doesn't use a toolbox). We prefer the student learn and use C or C++.

    We have a software package available with which we have had considerable success, described in Appendix C. It can be downloaded for free for MacOSX64, Linux64, and most versions of Windows, along with a lot of images, from www.cambridge.org/ 9781107184886. This package is, however, not required to use the book. The instructor may choose any software platform, including MATLAB.

    One style of teaching this course, the one we usually follow, is to have a number of small projects, roughly a week or two in duration, instead of a single end-of-class project.

    In teaching this course, the principal problems we have encountered are with students who don't know the difference between a compiler and a fishing rod, and students who think an eigenvalue is made with sugar and served with syrup. The emphasis on knowing linear algebra is very important.

    We also recommend that prior to taking this course, students take a class in image processing, in which they learn what a pixel is, what restoration does, and the difference between enhancement and filtering.

  • 5 - Kernel Operators

  • from Part II - Preprocessing
  • Wesley E. Snyder, North Carolina State University, Hairong Qi, University of Tennessee
  • Book:
    Fundamentals of Computer Vision
    Published online:
    25 October 2017
    Print publication:
    28 September 2017, pp 51-91

  • Summary

    You need boundaries … Even in our material creations, boundaries mark the most beautiful of places, between the ocean and the shore, between the mountains and the plains, where the canyon meets the river

    – Wm. Paul Young

    Introduction

    In this chapter, linear operations on images are investigated. The derivative, probably the most common linear operator, is considered first. That discussion is extended into edge detection, the result of applying derivative kernels on images. A variety of methods for accomplishing this objective are considered.

  • • (Section 5.2)We first define what makes a linear operator and explain the kernel operator, or sum of products, which is the type of linear operator that will appear most frequently in this book.

  • • (Section 5.3) We show in this section how to use vector representation of images to convert kernel operators to the matrix multiplication form in order to facilitate some analytical study.

  • • (Section 5.4) We focus on the discussion of derivatives, since it is probably the most common kernel operator used in Computer Vision applications. We discuss how to find the derivative kernel operators through the definition of derivatives, by function fitting, using vector representation of images, and taking derivatives of special blurring kernels.

  • • (Section 5.5) Applying derivative operators on images would result in edge images. We describe in this section the different types of edges and a popular edge detector, the Canny edge detector.

  • • (Section 5.6) The concept of scale space is explained and how the scale space is associated with the detection of edges at different scales.

  • • (Section 5.7) In Chapter 1, we briefly described the biological vision. Here, we revisit this issue and ask the question how human beings perform lower-level vision, i.e., edge detection. We introduce the Gabor filter, which is believed to well characterize the functionality of receptive fields in the visual cortex.

  • • (Section 5.8) We wrap up this chapter by performing an experimental study on the various types of derivative kernels in terms of their ability in detecting edges (both angle and magnitude) of different orientations.

  • Part I - Preliminaries

  • Wesley E. Snyder, North Carolina State University, Hairong Qi, University of Tennessee
  • Book:
    Fundamentals of Computer Vision
    Published online:
    25 October 2017
    Print publication:
    28 September 2017, pp 1-2

  • Summary

    In this part of the book, we start getting ready to do Computer Vision (CV). First, we briefly introduce biological vision and the biological motivation of Computer Vision. This is followed by a chapter describing how to write computer programs to process images. We then provide a chapter of math. The emphasis in that chapter is linear algebra, as that is the mathematical discipline most used in CV. We also touch briefly on probability and function minimization.

    After we have the human prepared by taking the prerequisites and reviewing the math, we need for that human to know what an image actually is. It turns out that the word “image” has a number of different meanings in different contexts, and Chapter 4 discusses them.

  • Part II - Preprocessing

  • Wesley E. Snyder, North Carolina State University, Hairong Qi, University of Tennessee
  • Book:
    Fundamentals of Computer Vision
    Published online:
    25 October 2017
    Print publication:
    28 September 2017, pp 49-50

  • Summary

    When the authors first started in Computer Vision, our intention was to write programs using the highest level, most elegant, most sophisticated Artificial Intelligence methods known. Of course, nothing worked.

    Images are never what you expect. Real images all have noise. Even a perfect digital image is sampled, and sampling itself introduces a type of noise. Then there is blur and other distortions.

    We begin this Part of the book by looking at the issue of finding edges in noisy images. That chapter will not only show how to find edges; it will introduce some other basic topics in image manipulation such as convolution.

    Then, we move into removing noise. Blurring an image will certainly remove noise. Of course, it will remove image content also. The trick is to remove the noise but preserve image details, and that topic is discussed in the next chapter.

    The final topic in this Part is morphology, a collection of nonlinear methods for erasing small details and closing gaps. The distance transform, a compact and useful representation for the information in an image, is also included in the morphology chapter.

  • 3 - Formalizing Commonsense Psychology

  • from Part I - Commonsense Psychology
  • Andrew S. Gordon, University of Southern California, Jerry R. Hobbs, University of Southern California
  • Book:
    A Formal Theory of Commonsense Psychology
    Published online:
    01 September 2017
    Print publication:
    07 September 2017, pp 36-59

  • Summary

    COVERAGE AND COMPETENCY

    A central problem in artificial intelligence (AI) is the development of large-scale formalizations of commonsense knowledge (McCarthy, 1959). Progress toward this goal has been slow because (1) sufficiently powerful and expressive formal languages need to be developed, (2) the millions of facts that people know and use must be articulated, (3) these facts need to be encoded in a formal language, and (4) reasoning systems that efficiently use this knowledge need to be developed (Davis and Morgenstern, 2004). Put another way, the challenges of formalizing commonsense knowledge are barriers to both breadth and depth. In breadth, researchers are aiming for formalizations of commonsense knowledge that have broad coverage over the concepts in the domains in which commonsense reasoning problems exist. In depth, researchers require that these formalisms have the competency to solve commonsense problems in an automated manner.

    Achieving both coverage and competency in the formalization of commonsense knowledge has been an elusive prize. The tradition in this field has been to focus on depth (competency) first, and to leave the problem of breadth (coverage) as somebody else's problem. Today, the field could best be described as a system of interactions between a community of authors and a handful of aggregators. In the general case, the set of authors is made up of academic researchers who simultaneously work on (1) a formal language that is slightly more expressive or powerful than previously published languages, (2) capturing a small handful of facts that people know and reason with, and (3) encoding these facts into the formal language that they have developed. These researchers publish their results in commonsense reasoning symposiums, knowledge representation conferences, and selected academic journals. The aggregators take the best products of these efforts and integrate them into large-scale knowledge bases, typically by rewriting them into their own formal languages. An important example of an aggregator is Cycorp Inc., which maintains the CYC knowledge base (Lenat, 1995), a confederation of thousands of micro theories authored by Cycorp knowledge engineers who base their work, when possible, on the best theories from the commonsense reasoning research community.

Page 8 of 69