A novel approach for partial blur detection and segmentation

Bahrami, Khosro; Kot, Alex C.; Fan, Jianchao

doi:10.1109/icme.2013.6607493

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…To measure the performance, the ground truth of the blur types are used. Since some of the images may have sharp regions, we use the method in [27] in advance to discriminate blur/sharp areas. We define two classes including tampered region as positive class and authentic region as negative class.…”

Section: Resultsmentioning

confidence: 99%

Image tampering detection by exposing blur type inconsistency

Bahrami

Kot

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we propose a novel method for image tampering detection in multi-type blurred images. After block-based image partitioning, a space-variant prior for local blur kernels is proposed for local blur kernels estimation. Then, the image blocks are clustered using a k-means clustering based on the similarity of local blur kernels to generate blur type invariant regions. Finally, blur types of the regions are classified into out-of-focus or motion blur using a minimum distance classifier. The experimental results show that the proposed method successfully detects and classifies the regions blur types which outperforms the state-of-the-art techniques. Our proposed approach is used to detect inconsistency in the partial blur types of an image as an evidence of image tampering.

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Section: Resultsmentioning

confidence: 99%

Image tampering detection by exposing blur type inconsistency

Bahrami

Kot

2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…{p(x + dix∆x, y + diy∆y) = 1|0∆x|x − xe|, 0∆y|y − ye|}, (9) where p(x + ∆x, y + ∆y) is the binary value of points of expansion, it means reset the points in the region of expansion to be points of our final target object. d i can be found in the set {(-1,-1,),(0,-1),(1,-1),(1,0),(1,1),(0,1),(-1,1),(-1,0)}, which correspond to the label of direction {0,1,2,3,4,5,6,7}.…”

Section: Proposed Object Segmentation Methodsmentioning

confidence: 99%

“…Normally, blur can be divided into two categories according to the forming reasons: defocus blur and motion blur [7] [8]. And according to the degree of spatially complexity, it can be divided into spatially invariant blur and spatially variant blur [9] [10]. The region of the red rectangle in Fig.…”

Section: Introductionmentioning

confidence: 99%

A novel saliency-based object segmentation method for seriously degenerated images

Wang

Liu

Zhang

2015

2015 IEEE International Conference on Information and Automation

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Automatically segmenting the salient object based on the saliency information frequently fails on the non-uniform motion blurred images. We propose a novel saliency-based object segmentation method with a self-expansion mechanism to deal with this problem in this paper. Firstly, to improve the initial localization accuracy for expansion, we integrate a modified local autocorrelation congruency into an initial salient object seed for building a combined salient object seed. Secondly, we present a novel method named Normal Expansion to expand the obtained salient object seed to the real boundaries of the target object. At last, we design a strategy based on superpixels to repair the lost degenerated regions. Based on the proposed method, we can more precisely segment the partially motion blurred object boundaries from a uniformly motion blurred background. Our experimental results show that our method outperforms some state-of-the-art saliency-based object segmentation approaches both quantitatively and qualitatively.

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“…However, this technique is not good in differentiating a wide range of blur degrees. In another work [17], we proposed an approach for blur/non-blur detection. However, the current work is the extension of [17] for two blur levels discrimination and image splicing detection application.…”

Section: Background and Prior Workmentioning

confidence: 99%

Splicing detection in out-of-focus blurred images

Bahrami

Kot

Fan

2013

2013 IEEE International Workshop on Information Forensics and Security (WIFS)

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This paper proposes a novel framework for image splicing detection based on the inconsistency in the blur degree and depth information of an image. Firstly, the blur kernels of image blocks (local blur kernels) are estimated. Next, a multi step reblurring technique is used to measure the relative blur degrees of the local blur kernels. The relative blur degrees are used as a feature to classify the image blocks based on the blur degrees. Furthermore, block-based and pixel-based techniques are incorporated for a fine segmentation of the regions with different blur degrees. Finally, the consistency in the blur degree and depth information of the regions are analyzed. Any inconsistency in the blur degree and depth information can be used as an evidence of image splicing. The experimental results show that the proposed approach can discriminate a wide range of blur degrees in natural and artificial blurred images which outperforms the state-of-the-art approaches. Furthermore, our proposed approach has been evaluated for image splicing detection. The result shows that it can be used successfully for detection of image splicing.

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A novel approach for partial blur detection and segmentation

Cited by 17 publications

References 18 publications

Image tampering detection by exposing blur type inconsistency

Image tampering detection by exposing blur type inconsistency

A novel saliency-based object segmentation method for seriously degenerated images

Splicing detection in out-of-focus blurred images

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