Authors:
Loreta Adriana Suta
1
;
Mihaela Scuturici
2
;
Serge Miguet
2
;
Laure Tougne
2
and
Mircea-Florin Vaida
1
Affiliations:
1
Technical University of Cluj Napoca, Romania
;
2
Université de Lyon and Université Lyon 2, France
Keyword(s):
Local Blur Detection, No-reference Blur Metric, Wavelet Analysis.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image Enhancement and Restoration
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
Abstract:
This paper presents a local no-reference blur assessment method in natural macro-like images. The purpose is to decide the blurriness of the object of interest. In our case, it represents the first step for a plant recognition system. Blur detection works on small non-overlapping blocks using wavelet decomposition and edge classification. At the block level the number of edges is less than on global images. A new set of rules is obtained by a supervised decision tree algorithm trained on a manually labelled base of 1500 blurred/un-blurred images. Our purpose is to achieve a qualitative decision of the blurriness/sharpness of the object of interest making it the first step towards a segmentation process. Experimental results show this method outperforms two other methods found in literature, even if applied on a block basis. Together with a pre-segmentation step, the method allows to decide if the object of interest (leaf, flower) is sharp in order to extract precise botanical key ide
ntification features (e. g. leaf border).
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