Multiphase segmentation based on new signed pressure force functions and one level set function

Ali, Haider; Badshah, Noor; Chen, Ke; Khan, Gulzar Ali; Zikria, Nosheen

doi:10.3906/elk-1606-260

Cited by 14 publications

(19 citation statements)

References 18 publications

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“…In order to show the segmentation performance of the proposed model we test the model on gray and color clean images. Figures 2 and 3 show the obtained results of the proposed method in comparison with the segmentation performance of the SPF [26] and GSPF [24] models. As expected, the application of the LBF model to the same set of images gives similar results as the proposed method, not being repetitive.…”

Section: Segmentation Accuracy Of the Proposed Methods For Clean Imagementioning

confidence: 99%

“…Although Akram et al [23] improved the performance of the region-based active contour models by efficiently stopping the contours at weak or blurred edges, the model is not designed to handle images having multiple intensity objects. To improve the local SPF model, Ali et al [24] proposed a combination of the SPF function with the generalized average concept in a new segmentation framework. Their model is given as follows:…”

Section: I(x)mentioning

confidence: 99%

“…The results obtained in Figure 4 and 3. The first column is the given vector-valued image, the second column shows the local SPF results [26], the third column shows GSPF results [24], and the fourth column shows the result of our method.…”

Section: Segmentation Accuracy For Fog/haze Images and Its Comparisonmentioning

confidence: 99%

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A joint image dehazing and segmentation model

Ali¹,

Sher²,

Zikria³

et al. 2019

Turk J Elec Eng & Comp Sci

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Objects and their feature identification in hazy or foggy weather conditions has been of interest in the last decades. Improving image visualization by removing weather influence factors for easy image postprocessing, such as object detection, has benefits for human assistance systems. In this paper, we propose a novel variational model that will be capable of jointly segmenting and dehazing a given image. The proposed model incorporates atmospheric veil estimation and locally computed denoising constrained surfaces into a level set function by performing a robust and efficient image dehazing and segmentation scheme for both gray and color outdoor images. The proposed model not only shows efficient segmentation of objects in foggy images by outperforming state-of-the-art methods but also produces dehazed object results in the same time.

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Section: Segmentation Accuracy Of the Proposed Methods For Clean Imagementioning

confidence: 99%

Section: I(x)mentioning

confidence: 99%

See 1 more Smart Citation

A joint image dehazing and segmentation model

Ali¹,

Sher²,

Zikria³

et al. 2019

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…If , the approach is identical to CV. In [2], the authors assert that the proposed adjustments have the following properties. As , and approach the maximum and minimum intensity in the foreground and background of the image, respectively.…”

Section: Related Approachesmentioning

confidence: 99%

“…Alternative choices of data fitting terms can be used when different assumptions are made on the image, z . Examples include [1, 2, 16, 25, 40, 43]. We note that multiphase approaches [9, 42] are also closely related to this formulation although in this paper we focus on the two-phase problem due to associated applications of interest.…”

Section: Introductionmentioning

confidence: 99%

Chan–Vese Reformulation for Selective Image Segmentation

Roberts

Spencer

2019

J Math Imaging Vis

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Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type. Typically, such methods involve introducing additional constraints to generic segmentation approaches. However, we show that this is often inconsistent with respect to common assumptions about the image. The proposed method introduces a new fitting term that is more useful in practice than the Chan–Vese framework. In particular, the idea is to define a term that allows for the background to consist of multiple regions of inhomogeneity. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods.

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Image-selective segmentation model for multi-regions within the object of interest with application to medical disease

et al. 2020

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Detection and extraction of an object of interest and accurate boundaries segmentation in a given image has been of interest in the last decades due to its application in different fields. To successfully segment a single object, interactive/selective segmentation techniques has been developed as a supplement to the existing global segmentation techniques. Even though existing interactive/ selective segmentation techniques perform well in segmenting the images with prominent edges those methods are less efficient or even fail in segmenting images having muti-regions of different intensity scale. In this paper, we design a new variational selective segmentation model which incorporates the idea of area-based fitting term along with a signed pressure force function based on a generalized average into a variational energy function. The new model is capable to capture the object of interest which can be single or multi-region within the object of interest. To evaluate the performance of our new model, we compare our results with state of the art models by showing same efficiency and reliability on detecting single-region and an outperforming for multi-region selective segmentation. Comparison tests were carried out on synthetic and real data images.

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Multiphase segmentation based on new signed pressure force functions and one level set function

Cited by 14 publications

References 18 publications

A joint image dehazing and segmentation model

A joint image dehazing and segmentation model

Chan–Vese Reformulation for Selective Image Segmentation

Image-selective segmentation model for multi-regions within the object of interest with application to medical disease

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