View Dependent Surface Material Recognition
Result description
The paper presents a detailed study of surface material recognition dependence on the illumination and viewing conditions which is a hard challenge in a realistic scene interpretation. The results document sharp classification accuracy decrease when using usual texture recognition approach, i.e., small learning set size and the vertical viewing and illumination angle which is a very inadequate representation of the enormous material appearance variability. The visual appearance of materials is considered in the state-of-the-art Bidirectional Texture Function (BTF) representation and measured using the upper-end BTF gonioreflectometer. The materials in this study are sixty-five different wood species. The supervised material recognition uses the shallow convolutional neural network (CNN) for the error analysis of angular dependency. We propose a Gaussian mixture model-based method for robust material segmentation.
Keywords
convolutional neural networktexture recognitionBidirectional Texture Function recognition
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
View Dependent Surface Material Recognition
Original language description
The paper presents a detailed study of surface material recognition dependence on the illumination and viewing conditions which is a hard challenge in a realistic scene interpretation. The results document sharp classification accuracy decrease when using usual texture recognition approach, i.e., small learning set size and the vertical viewing and illumination angle which is a very inadequate representation of the enormous material appearance variability. The visual appearance of materials is considered in the state-of-the-art Bidirectional Texture Function (BTF) representation and measured using the upper-end BTF gonioreflectometer. The materials in this study are sixty-five different wood species. The supervised material recognition uses the shallow convolutional neural network (CNN) for the error analysis of angular dependency. We propose a Gaussian mixture model-based method for robust material segmentation.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
GA19-12340S: Surface material recognition under variable optical observation conditions
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Visual Computing : 14th International Symposium on Visual Computing (ISVC 2019)
ISBN
978-3-030-33719-3
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
12
Pages from-to
156-167
Publisher name
Springer
Place of publication
Cham
Event location
Lake Tahoe
Event date
Oct 7, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—
Result type
D - Article in proceedings
OECD FORD
Automation and control systems
Year of implementation
2019