Master Thesis - Fachbereich Informatik

6 CHAPTER 1. INTRODUCTION
There are several useful books addressing the fundamental methods, techniques and
algorithms used to develop machine vision applications in a comprehensive fashion [7, 16,
18, 62].
Dimensional measuring of objects requires knowledge of an object’s boundaries. A
common indicator for object boundaries, both in human and artificial vision, are edges.
Edge detection is a widely investigated area of vision research dating back from 1959 in the
field of TV signal processing [37] to the present. The edge detection methods considered
in this thesis are related to the work of Sobel [36, 51], Marr and Hildreth [45] and Canny
[13].
In addition, anisotropic approaches have been proposed [69], i.e. orientation selective
edge detectors. These filters have many applications for example in texture analysis or
in the design of steerable filters that efficiently control the orientation and scale of filters
to extract certain features in an adaptive way [25, 49]. Many of these approaches are
motivated in early human vision. In their investigation of the visual cortex, Hubel and
Wiesel discovered orientation selective cells in the striate cortex V1 [33]. In several theories
it is assumed that humans perceive low-level features such as edges or lines by combinations
of the response of these cells [27]. Many computer vision researchers, however, adapted
the idea of orientation selective cells or filters which can be combined to produce a certain
response. Such sets of filters are often called filter banks. Malik and Perona [44] used a
filter bank based on even symmetric difference of offset Gaussians (DOOG) for texture
discrimination.
The discrete pixel grid resolution of CCD camera images limits the measuring accuracy.
Thus, several techniques have been proposed that compute subpixel edge positions [6, 41,
66, 56, 71].
A common task in vision applications is to search whether a particular pattern is part
of an image, and if so, where it is located [28]. Template matching is one method to
tackle this problem. Cross-correlation techniques are widely used as measure of similarity
[64, 18, 62]. In stereo vision, correlation is used to solve the problem of correspondences
between the left and right view [21, 65]. Other practical applications can be found in
feature trackers, pattern recognition, or registration of e.g. medical image data.
Accurate visual measurements often require a camera calibration step to relate 3D points
in the real world to image coordinates and to compensate for lens distortions. One early
approach was presented by Tsai [39, 67]. An extensive introduction into calibration is
given by Faugeras [21] or Hartley and Zisserman [30]. The calibration approach in this
thesis work is closely related to the work of Zhang [74] and Heikkilä and Silvén [31].
1.5. Thesis Outline
The remainder of this thesis is organized as follows: Chapter 2 provides the theoretical
background on models and techniques used in later sections with regard to measuring with
video cameras. This chapter also gives an overview on different illumination techniques
used for machine vision applications.
In Chapter 3, the physical design of the system is introduced. Especially the camera
and lens selection as well as the illumination setup are discussed in detail in this chapter.