The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B3b. Beijing 2008
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Figure 2. Sketch map of different edge types. (a)(b)(c)
Intensity, gradient and detected shape of strong
edge. (d)(e)(f) Intensity, gradient and detected
shape of weak edges. (g)(h)(i) Intensity, gradient
and detected shape of false edges.
The aim of the algorithm is to keep useful edges (include strong
and weak edges) and get rid of useless edges (include false
edges). The strong edges have distinct characteristic, they are
hard to confuse with the other two. So, the crux is how to
distinguish weak edges from false edges. There are some
similar characteristic between weak edges and false edges: they
are both detected as broken segments and the difference in
intensity is not so obvious. But the distribution of weak edges is
so regular that it is sensitive to our vision. However, the
previous edge detection algorithms ignored the regular
distributing, they mainly focused on local intensity change and
the neighbouring relation between certified and candidate edge
points. It led to the losing of weak edges with slim difference in
intensity. The main idea of the proposed algorithm is that
simulate the sensitive perception of human vision to regular
distribution, and investigate the distribution characteristic of
edges in a global view, then pick, link and expand the weak
edges. For example, an edge is short and with low gradient
magnitude, if it can form into a line with some other edges, the
edge will be marked as candidate edge to gain further
processing, otherwise it will be deleted.
3. ALGORITHM DESCRIPTION
The flow of the nroposed algorithm is shown as Figure 2. In
preliminary edge detection procedure, credible edges can be
gained by previous edge detection algorithm with tight
thresholds and candidate edges can be gained by the same
algorithm with loose thresholds. All the detected edges are
fitted into line segments, and the segments’ parameters in polar
coordinates system are calculated in the line fitting procedure.
Then, the candidate edges are grouped, picked and linked under
the idea of linear perception, in this procedure, most false edges
are deleted. And then, expand the edges from their endpoints to
gain more complete edges. At last, the result of edge detection
with more weak edges and less false edges are output.
Input Image
Figure 3. The flow chart of the proposed algorithm
3.1 Preliminary Edge Detection
Edison algorithm(Meer and Georgescu 2001) is improved based
on Canny edge detection algorithm, and it applies embedded
confidence to all procedures of edge detection and satisfies the
three performance criteria for detecting edges: good detection,
good localization and only response to a single edge(Canny
1986). Embedded confidence is the information which is
independent from gradient estimation and used for evaluating
the comparability between data model and ideal edge model,
thus, can detect edges of all kinds of figures by reducing the
uncertainty in edge detection(Jiang 2004). So, we adopt Edison
algorithm to perform preliminary edge detection.
In this procedure, credible edges can be gained by using tight
thresholds and candidate edges can be gained by using loose
ones. Credible edges are strong edges, most of them are useful
edges; while candidate edges consist of weak and false edges,
both useful and useless edges are included. Therefore, candidate
edges are main part in the next procedures.
3.2 Line Fitting
Line fitting is aimed at finding out the linear feature shown by
the edges and calculating the parameters of 0 and p in polar
coordinates system. It could be a good preparation for the next
procedures. Hough transform has been widely used as a typical
algorithm. But Hough transform is a global calculating process,
it is hard to detect short segments, because the voting mode and
accumulation has problems that the peak value caused by the
isolated points forming into line by accident is higher than the
peak value caused by short segments, and the segments at a
distance but with similar parameters effect each other(Furukawa
and Shinagawa 2003). The proposed algorithm solves these
problems with localized Hough transform. We clustered edge
points into edge strips according to their neighbouring
relationship. Then, calculate the parameters of each edge strip
respectively by Hough transform, the process could avoid the
negative effect brought by global calculating, and gain a clear
peak value even when processing on short segments. To reduce
the computational complexity and accelerate the computational
speed of Hough transform, the numerical interval of parameter
space can be extend properly, after voting, get the points voting
for the peak value to fit an accurate line by least square.
3.3 Grouping and Linking Edges
Traditional methods of perception grouping only considered
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