Authors:
Simon Reich
1
;
Alexey Abramov
1
;
Jeremie Papon
1
;
Florentin Wörgötter
1
and
Babette Dellen
2
Affiliations:
1
Georg-August-Universität Göttingen, Germany
;
2
Institut de Robotica i Informatica Industrial (CSIC-UPC), Spain
Keyword(s):
Texture Filter, Image Segmentation, GPU, Real-time, Edge-preserving.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Applications
;
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Robotics
;
Segmentation and Grouping
;
Software Engineering
Abstract:
The segmentation of textured and noisy areas in images is a very challenging task due to the large variety of objects and materials in natural environments, which cannot be solved by a single similarity measure. In this paper, we address this problem by proposing a novel edge-preserving texture filter, which smudges the color values inside uniformly textured areas, thus making the processed image more workable for color-based image segmentation. Due to the highly parallel structure of the method, the implementation on a GPU runs in realtime, allowing us to process standard images within tens of milliseconds. By preprocessing images with this novel filter before applying a recent real-time color-based image segmentation method, we obtain significant improvements in performance for images from the Berkeley dataset, outperforming an alternative version using a standard bilateral filter for preprocessing. We further show that our combined approach leads to better segmentations in terms o
f a standard performance measure than graph-based and mean-shift segmentation for the Berkeley image dataset.
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