Color image segmentation by combining the convex active contour and the Chan Vese model

Deriche, Mohamed; Amin, Asjad; Qureshi, Muhammad Ali

doi:10.1007/s10044-017-0632-9

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…In this section we briefly mention recent work relevant to the proposed model. First is the work of Deriche et al [30], which involves Chan-Vese fitting after a geodesic or dynamic region merging based initialisation, using foreground/backgroud regions provided by the user. Another recent approach is that of Liu et al [15] which works in a similar framework to Cai et al [31], by introducing a new weighting function to their formulation.…”

Section: Alternative Methodsmentioning

confidence: 99%

Parameter-Free Selective Segmentation With Convex Variational Methods

Spencer

Chen

Duan

2019

IEEE Trans. on Image Process.

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Selective segmentation methods involve incorporating user input to partition an image into a foreground and background. Often these methods are sensitive to some aspect of the user input in a counter intuitive manner, making their use in practice difficult. The most robust methods often involve laborious refinement on the part of the user, and sometimes editing/supervision. The proposed method reduces the burden on the user by simplifying the requirements on the input. Specifically, the fitting term does not depend on a distance function and so no selection parameter is introduced. Instead, we consider how the user input relates to some general intensity fitting term to ensure the approach is less sensitive to the decisions or intuition of the user. We give comparisons to existing approaches to show the advantages of the new selective segmentation model.

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Section: Alternative Methodsmentioning

confidence: 99%

Parameter-Free Selective Segmentation With Convex Variational Methods

Spencer

Chen

Duan

2019

IEEE Trans. on Image Process.

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“…CV model has fair segmentation speed and initialization robustness [42,67] . However, 𝑐 1 and 𝑐 2 are only related to the global gray value of the input image.…”

Section: Chan Vese Modelmentioning

confidence: 99%

An overview of intelligent image segmentation using active contour models

Chen¹,

Ge²,

Wang³

et al. 2023

Intell Robot

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The active contour model (ACM) approach in image segmentation is regarded as a research hotspot in the area of computer vision, which is widely applied in different kinds of applications in practice, such as medical image processing. The essence of ACM is to make use ofuse an enclosed and smooth curve to signify the target boundary, which is usually accomplished by minimizing the associated energy function by means ofthrough the standard descent method. This paper presents an overview of ACMs for handling image segmentation problems in various fields. It begins with an introduction briefly reviewing different ACMs with their pros and cons. Then, some basic knowledge in of the theory of ACMs is explained, and several popular ACMs in terms of three categories, including region-based ACMs, edge-based ACMs, and hybrid ACMs, are detailedly reviewed with their advantages and disadvantages. After that, twelve ACMs are chosen from the literature to conduct three sets of segmentation experiments to segment different kinds of images, and compare the segmentation efficiency and accuracy with different methods. Next, two deep learning-based algorithms are implemented to segment different types of images to compare segmentation results with several ACMs. Experimental results confirm some useful conclusions about their sharing strengths and weaknesses. Lastly, this paper points out some promising research directions that need to be further studied in the future.

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“…Thus, the first option that was considered was a semiautomatic segmentation. The original snake model of the active contour proposed by Kass [33]…”

Section: A Active Contours Vs Region Of Interestmentioning

confidence: 99%

Video Processing for Animation at Key Points of Movement in the Mimosa Pudica

Romero-Herrera¹,

Mendez-Segundo²

2020

IJACSA

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The processing of an image of a moving plant is inadequate, for this reason, digital video processing must be incorporated, which allows the behavior of an algorithm to be analyzed over time. A method is presented that takes images of a plant with autonomous movement filmed on video; the frames are digitally processed and the information is used to generate animations. Our representation of the structure is derived from an analysis of the image where the plant is deformed; the projections of the movement of the plant are recovered from the video frames and are used as a basis to generate videograms in an animation based on key points taken from an image; Harris and Brisk algorithms are applied. The main plant used is the Mimosa Pudica. Once the frames have been obtained, correlation is proposed as a mechanism to find movement. The techniques are equally useful for any other moving plant such as carnivores or sunflowers.

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Color image segmentation by combining the convex active contour and the Chan Vese model

Cited by 7 publications

References 20 publications

Parameter-Free Selective Segmentation With Convex Variational Methods

Parameter-Free Selective Segmentation With Convex Variational Methods

An overview of intelligent image segmentation using active contour models

Video Processing for Animation at Key Points of Movement in the Mimosa Pudica

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