Authors:
Pascal Mettes
;
Robby T. Tan
and
Remco Veltkamp
Affiliation:
Utrecht University, Netherlands
Keyword(s):
Hybrid Water Descriptor, Mode Subtraction, Decision Forests, Markov Random Field, Novel Database.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Segmentation and Grouping
Abstract:
The automatic recognition of water entails a wide range of applications, yet little attention has been paid to
solve this specific problem. Current literature generally treats the problem as a part of more general recognition
tasks, such as material recognition and dynamic texture recognition, without distinctively analyzing
and characterizing the visual properties of water. The algorithm presented here introduces a hybrid descriptor
based on the joint spatial and temporal local behaviour of water surfaces in videos. The temporal behaviour is
quantified based on temporal brightness signals of local patches, while the spatial behaviour is characterized
by Local Binary Pattern histograms. Based on the hybrid descriptor, the probability of a small region of being
water is calculated using a Decision Forest. Furthermore, binary Markov Random Fields are used to segment
the image frames. Experimental results on a new and publicly available water database and a subset of the
DynTex database
show the effectiveness of the method for discriminating water from other dynamic and static
surfaces and objects.
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