Image de-fencing framework with hybrid inpainting algorithm

Farid, Muhammad Shahid; Mahmood, Arif; Grangetto, Marco

doi:10.1007/s11760-016-0876-7

Cited by 23 publications

(46 citation statements)

References 29 publications

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“…The results show that the proposed technique is also effective for traditional object segmentation tasks. The results of the experiments presented above show the ability (a) Input Image (b) Ground Truth (c) [23] (d) [19] (e) [22] (f) Ours [19], (e) results of [22], and (f) our results.…”

Section: Experimental Evaluationmentioning

confidence: 82%

“…We also performed qualitative and quantitative evaluations of the proposed algorithm and compared the results with the well-known object segmentation approaches: Grab-Cut [19], GrowCut [22] and De-fencing method [23]. To perform quantitative evaluation, ground truth segmentation is required.…”

Section: Qualitative Evaluation and Comparisonsmentioning

confidence: 99%

“…In each experiment reported in the following, image (a) shows the input image and its ground truth is shown in (b). The results of [23], [19], and [22] are shown in (c), (d) and (e) respectively, and the results achieved by our method are shown in (f). The shortcomings or inaccuracies in the segmentation results are highlighted with red circles.…”

Section: Qualitative Evaluation and Comparisonsmentioning

confidence: 99%

“…Other techniques e.g., [21][22][23] expect few user strokes marked on the foreground and/or background regions which are used to model and then segment the two regions. Some methods e.g., [24,25,41] require the user to draw edges around the object boundary which serves as a rough segmentation and is refined though matting tools [26].…”

Section: Introductionmentioning

confidence: 99%

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DOST: a distributed object segmentation tool

Farid

Lucenteforte

Grangetto

2017

Multimed Tools Appl

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This paper presents a novel distributed object segmentation framework that allows one to extract potentially large coherent objects from digital images. The proposed approach requires minimum user supervision and permits to segment the objects accurately. It works in three steps starting with the user input in form of few mouse clicks on the target object. First, based on user input, the statistical characteristics of the target distributed object are modeled with Gaussian mixture model. This model serves as the primary segmentation of the object. In the second step, the segmentation result is refined by performing connected component analysis to reduce false positives. In the final step the resulting segmentation map is dilated to select the neighboring pixels that are potentially incorrectly classified; this allows us to recast the segmentation as a graph partitioning problem that can be solved using the well-known graph cut technique. Extensive experiments have been carried out on heterogeneous images to test the accuracy of the proposed method for the segmentation of various types of distributed objects. Examples of application of proposed technique in remote sensing to segment roads and rivers from aerial images are also presented. The visual and objective evaluation and comparison with the existing techniques show that the proposed tool can deliver optimal performance when applied to tough object segmentation tasks.

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Section: Experimental Evaluationmentioning

confidence: 82%

Section: Qualitative Evaluation and Comparisonsmentioning

confidence: 99%

Section: Qualitative Evaluation and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DOST: a distributed object segmentation tool

Farid

Lucenteforte

Grangetto

2017

Multimed Tools Appl

Self Cite

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“…Various interactive solutions to this problem have been proposed in literature, e.g. [1][2][3][4][5][6][7] and that usually require human user assistance to obtain satisfactory results. User assistance in object segmentation is used to guide the segmentation process and it is usually provided in the form of few scribbles on the target object and on the background.…”

Section: Introductionmentioning

confidence: 99%

Semi-automatic Segmentation of Scattered and Distributed Objects

Farid

Lucenteforte

Khan

et al. 2017

Advances in Intelligent Systems and Computing

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This paper presents a novel object segmentation technique to extract objects that are potentially scattered or distributed over the whole image. The goal of the proposed approach is to achieve accurate segmentation with minimum and easy user assistance. The user provides input in the form of few mouse clicks on the target object which are used to characterize its statistical properties using Gaussian mixture model. This model determines the primary segmentation of the object which is refined by performing morphological operations to reduce the false positives. We observe that the boundary pixels of the target object are potentially misclassified. To obtain an accurate segmentation, we recast our objective as a graph partitioning problem which is solved using the graph cut technique. The proposed technique is tested on several images to segment various types of distributed objects e.g. fences, railings, flowers. We also show some remote sensing application examples, i.e. segmentation of roads, rivers, etc. from aerial images. The obtained results show the effectiveness of the proposed technique.

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