Abstract-An overview of present computer techniques of parti-
tioning continuous-tone images into meaningful segments and of
characterizing these segments by sets of "features" is presented.
Segmentation often consists of two methods: boundary detection and
texture analysis. Both of these are discussed. The design of the
segmenter and feature extractor are intimately related to the design
of the rest of the image analysis system-particularly the preproces-
sor and the classifier. Toward aiding this design, a few guidelines and
illustrative examples are included.
IMAGE segmentation and feature extraction are two
major components of modern computerized image
analysis or "machine vision." This paper provides an over-
view of these two components.
A computerized image analyzer usually consists of
many sometimes all of the following major components:
1) source of radiant energy or illumination, 2) scene, 3)
sensor, 4) scanner, 5) digitizer, 6) preprocessor, 7) boundary
detector, 8) texture analyzer, 9) feature extractor, 10)
classifier, 11) knowledge bank, 12) summarizer, 13) adviser,
14) questioner, 15) display, 16) interactive controller, 17)
human user (last in list, but first in importance). These
components are usually organized approximately as in-
dicated in Fig. 1. Among these components the texture
analyzer and boundary detector together form the segmen-
ter, which identifies meaningful connected components
("4segments") of the picture. The feature extractor computes
a set of descriptors which facilitate classifying and labeling
the segments into categories. These categories are used
subsequently in the generation of a summary or description
of the scene.
In addition to discussing the segmenter we shall describe
several of the types of features that have proved successful
for labeling the segments in various applications.
(Sometimes-as in the case of Fourier harmonics ofintrinsic
equations  of boundaries-the features are also used for
improving the segmentation process.) We shall omit discus-
sion of the selection of a best subset of features from a larger
set-a subject for which there is a substantial literature and
which would take us beyond the intended scope of this
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