Please use this identifier to cite or link to this item: https://doi.org/10.1145/2072298.2072024
Title: Blurred image region detection and classification
Authors: Su, B.
Lu, S.
Tan, C.L. 
Keywords: ? channel map
Blurred region detection and classification
Image blur
Singular value decomposition
Issue Date: 2011
Source: Su, B.,Lu, S.,Tan, C.L. (2011). Blurred image region detection and classification. MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops : 1397-1400. ScholarBank@NUS Repository. https://doi.org/10.1145/2072298.2072024
Abstract: Many digital images contain blurred regions which are caused by motion or defocus. Automatic detection and classification of blurred image regions are very important for different multimedia analyzing tasks. This paper presents a simple and effective automatic image blurred region detection and classification technique. In the proposed technique, blurred image regions are first detected by examining singular value information for each image pixels. The blur types (i.e. motion blur or defocus blur) are then determined based on certain alpha channel constraint that requires neither image de-blurring nor blur kernel estimation. Extensive experiments have been conducted over a dataset that consists of 200 blurred image regions and 200 image regions with no blur that are extracted from 100 digital images. Experimental results show that the proposed technique detects and classifies the two types of image blurs accurately. The proposed technique can be used in many different multimedia analysis applications such as image segmentation, depth estimation and information retrieval. Copyright 2011 ACM.
Source Title: MM'11 - Proceedings of the 2011 ACM Multimedia Conference and Co-Located Workshops
URI: http://scholarbank.nus.edu.sg/handle/10635/40694
ISBN: 9781450306164
DOI: 10.1145/2072298.2072024
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