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Proceedings, XXth congress

U.G.Sefercik ^ *, O.E.Gülegen * gr
* ZKU, Engineering Faculty, 67100 Zonguldak, Turkey — haritaci67@hkmo.net de
KEY WORDS: Analog, Non-metric, Camera, Geology, Automation, Edge , Extraction
Edge detection is one of the most frequently used techniques in digital image processing. Its application area reaches from ou
astronomy to medicine where isolation of objects focused on from the unwanted background is of great interest. Edge detection has im
also found application for photogrammetric purposes. In this study, edge detection has first been practiced on stratigrafic structures, ev
which are crucial to geologic time scaling, using digitized images from an analog non-metric camera. The raw images have been low
pass filtered in order to suppress the huge amount of unnecessary details. Four types of methods have been used and intercompared In
with each other. One of the problems encountered is long processing times due to gradient operations in both directions on the by
images. Natural layering of structure has been exploited in order to reduce computing time. Edge detection followed then from Sii
single direction. Taking over close grey values of linear edges with the non-required background leads to unclear detection of edges. str
Another practice of this study is to extract the faults in the western part of the north Anatolian Fault Zone on a digitized image taken tec
by TK350 camera. A comparison of extracted details with geological maps has been undertaken. Here, the biggest problem is that is
the faults can be misidentified as geomorphological formations. Hydrological attributes such as streams, water lines, etc. exhibit Sc
same characteristics as with the faults. The joint problem of both applications is the quantization of linear details via dilatation. dig
Automation is the last step of the entire edge detection process and has been still a difficult task. alg
1. INTRODUCTION are the sign of lack of continuity, and ending (B.S Penn et al.
1993). As a result of this transformation, edge image is obtained
In our era, image processing and digital photogrammetry have without encountering any changes in physical qualities of the
been developing rapidly. These disciplines are used in studies main image. Th
with various objectives. In this research; firstly revealing Bl
stratigraphic construction is aimed at by applying edge Objects consist of numerous parts of different color levels. In an mz
detection used in image processing on a photograph which was image with different grey levels, despite an obvious change in tof
taken analogously and transformed into a scanned digital the grey levels of the object, the shape of the image can be Fig
image. In the second phase, by applying same techniques, distinguished (Figure 1). The reason for this is the sensitivity of
discriminating the faults existing on the west extension of the the eye to regional contrast. Contrast alteration can be observed
northern Anatolian Fault Zone is studied by using TK350 by edge detection techniques. Edges can exist in various shapes.
satellite image that has 10m geometric resolution on the ground. These are step edge, ramp edge, line edge and roof edge.
Another target of research is explaining whether the detection
techniques are applicable to all sorts of problems with the The mathematic display of edge detection, an example for
results obtained by these studies. In the sample applications difference operator with single dimension (1-D);
defined, Edges being disclosed automatically in order to avoid
any comment. That is automation is targeted. 1 ;
Twi 2 0 Dy - (2) 0 e
What's edge detection? Can be presented as "x" and *y" or horizontally and vertically.
Edge detection is one of the subjects of basic importance in The methods, that are used to obtain the edge image of
image processing. The parts on which immediate changes in introductory data of a digital image can be examined in 5 parts;
grey tones occur in the images are called "edges". Benefiting Derivative methods, Local statistical method, Filtering methods,
from the direct relation between physical qualities of the Stokastic Gradient methods and Morphologic methods.
materials and their edges, these qualities can be recognized
from edges. Because of these qualities, edge detection 2.1 Derivative Methods Á
techniques gain importance in terms of image processing.
Since these methods are highly sensitive to noise, edge
Edge detection techniques transform images to edge images detection algorisms form faulty edges and they fail to the
benefiting from the changes of grey tones in the images. Edges physical features of the object correctly. One of the most

* Corresponding author.