2. Image Objects by Segmentation
The fundamental step of any eCognition
image analysis is to do segmentation of a
scene— representing an image—into image
object primitives. Thus, initial segmentation is
the subdivision of an image into separated
regions represented by basic unclassified
image objects called image object primitives.
For successful and accurate image analysis,
defining object primitives of suitable size and
shape is of utmost importance. As a rule of
thumb, good object primitives are as large as
possible, yet small enough to be used as
building blocks for the objects to be detected
in the image. Pixel is the smallest possible building block of an image, however it has
mixture of information. To get larger building
blocks, different segmentation methods are
available to form contiguous clusters of pixels
that have larger property space.
Commonly, in image processing,
segmentation is the subdivision of a digital
image into smaller partitions according to
given criteria. Different to this, within the
eCognition technology, each operation that
creates new image objects is called
segmentation, no matter if the change is
achieved by subdividing or by merging
existing objects. Different segmentation
Chessboard Segmentation
Quad Tree Based Segmentations
9 Hands on Exercise Using eCognition Developer
algorithms provide several methods of
creating of image object primitives.
The new image objects created by
segmentation are stored in a new image
object level. Each image object is defined
by a contiguous set of pixels, where each
pixel belongs to exactly one image object.
Each of the subsequent image object
related operations like classification,
reshaping, re-segmentation, and
information extraction is done within an
image object level. Simply said, image
object levels serve as internal working areas
of the image analysis.