Authors:
Eric Gabriel
1
;
Ferdinand Hahmann
1
;
Gordon Böer
1
;
Hauke Schramm
2
and
Carsten Meyer
2
Affiliations:
1
Kiel University of Applied Sciences, Germany
;
2
Kiel University of Applied Sciences, Faculty of Engineering and Kiel University (CAU), Germany
Keyword(s):
Object Detection, Object Localization, Feature Extraction, Edge Detection, Canny Edge Detection, Structured Edge Detection, Discriminative Generalized Hough Transform.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
Automatic localization of target objects in digital images is an important task in Computer Vision. The Generalized
Hough Transform (GHT) and its variant, the Discriminative Generalized Hough Transform (DGHT),
are model-based object localization algorithms which determine the most likely object position based on accumulated
votes in the so-called Hough space. Many automatic localization algorithms - including the GHT and
the DGHT - operate on edge images, using e.g. the Canny or the Sobel Edge Detector. However, if the image
contains many edges not belonging to the object of interest (e.g. from other objects, background clutter, noise
etc.), these edges cause misleading votes which increase the probability of localization errors. In this paper
we investigate the effect of a more sophisticated edge detection algorithm, called Structured Edge Detector,
on the performance of a DGHT-based object localization approach. This method utilizes information on the
shape of the target object to
substantially reduce the amount of non-object edges. Combining this technique
with the DGHT leads to a significant localization performance improvement for automatic pedestrian and car
detection.
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