Narrow Band Region-Based Active Contours Model for Noisy Color Image Segmentation

A narrow band active contour model for color image segmentation is proposed, which applies local statistics to improve the robustness against noise. The crux of our approach is to use intensity mean of local region to define the force function within a level set framework, within which a narrow band is implemented to further improve the computational efficiency. In addition, the image is segmented from channel-to-channel, which shows superior performance when the intensities of the object and background are similar. Furthermore, a multichannel segmentation combination method is used to integrate the information of multiple level sets. The proposed model has been applied to both synthetic and real images with expected results, and the comparison with the state-of-the-art approaches demonstrates the accuracy and superiority of our approach.

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Objectness Supervised Merging Algorithm for Color Image Segmentation

Mathematical Problems in Engineering ◽

10.1155/2016/3180357 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Haifeng Sima ◽

Aizhong Mi ◽

Zhiheng Wang ◽

Youfeng Zou

Keyword(s):

Image Segmentation ◽

Color Image ◽

Region Growing ◽

Color Image Segmentation ◽

Semantic Features ◽

Merging Procedure ◽

Proposed Model ◽

Merging Process ◽

High Level ◽

Color Texture

Ideal color image segmentation needs both low-level cues and high-level semantic features. This paper proposes a two-hierarchy segmentation model based on merging homogeneous superpixels. First, a region growing strategy is designed for producing homogenous and compact superpixels in different partitions. Total variation smoothing features are adopted in the growing procedure for locating real boundaries. Before merging, we define a combined color-texture histogram feature for superpixels description and, meanwhile, a novel objectness feature is proposed to supervise the region merging procedure for reliable segmentation. Both color-texture histograms and objectness are computed to measure regional similarities between region pairs, and the mixed standard deviation of the union features is exploited to make stop criteria for merging process. Experimental results on the popular benchmark dataset demonstrate the better segmentation performance of the proposed model compared to other well-known segmentation algorithms.

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Interactive color image segmentation editor driven by active contour model

Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348) ◽

10.1109/icip.1999.817110 ◽

2003 ◽

Cited By ~ 11

Author(s):

J. Gao ◽

A. Kosaka ◽

A. Kak

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Color Image ◽

Active Contour Model ◽

Color Image Segmentation ◽

Contour Model

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Color Image Segmentation Based on a New Geometric Active Contour Model

2010 International Conference on Machine Vision and Human-machine Interface ◽

10.1109/mvhi.2010.75 ◽

2010 ◽

Cited By ~ 4

Author(s):

HaiJun Wang ◽

Ming Liu ◽

WenLai Ma

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Color Image ◽

Active Contour Model ◽

Color Image Segmentation ◽

Contour Model ◽

Geometric Active Contour

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Morphological gradient applied to new active contour model for color image segmentation

Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication - ICUIMC '12 ◽

10.1145/2184751.2184778 ◽

2012 ◽

Cited By ~ 3

Author(s):

Nguyen Tran Lan Anh ◽

Young-Chul Kim ◽

Guee-Sang Lee

Keyword(s):

Image Segmentation ◽

Active Contour ◽

Color Image ◽

Active Contour Model ◽

Color Image Segmentation ◽

Morphological Gradient ◽

Contour Model

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A Chaotic Electromagnetic Field Optimization Algorithm Based on Fuzzy Entropy for Multilevel Thresholding Color Image Segmentation

Entropy ◽

10.3390/e21040398 ◽

2019 ◽

Vol 21 (4) ◽

pp. 398 ◽

Cited By ~ 5

Author(s):

Suhang Song ◽

Heming Jia ◽

Jun Ma

Keyword(s):

Electromagnetic Field ◽

Image Segmentation ◽

Heuristic Algorithms ◽

Color Image ◽

Fitness Function ◽

Fuzzy Entropy ◽

Color Image Segmentation ◽

Superior Performance ◽

Computational Time ◽

Multilevel Thresholding

Multilevel thresholding segmentation of color images is an important technology in various applications which has received more attention in recent years. The process of determining the optimal threshold values in the case of traditional methods is time-consuming. In order to mitigate the above problem, meta-heuristic algorithms have been employed in this field for searching the optima during the past few years. In this paper, an effective technique of Electromagnetic Field Optimization (EFO) algorithm based on a fuzzy entropy criterion is proposed, and in addition, a novel chaotic strategy is embedded into EFO to develop a new algorithm named CEFO. To evaluate the robustness of the proposed algorithm, other competitive algorithms such as Artificial Bee Colony (ABC), Bat Algorithm (BA), Wind Driven Optimization (WDO), and Bird Swarm Algorithm (BSA) are compared using fuzzy entropy as the fitness function. Furthermore, the proposed segmentation method is also compared with the most widely used approaches of Otsu’s variance and Kapur’s entropy to verify its segmentation accuracy and efficiency. Experiments are conducted on ten Berkeley benchmark images and the simulation results are presented in terms of peak signal to noise ratio (PSNR), mean structural similarity (MSSIM), feature similarity (FSIM), and computational time (CPU Time) at different threshold levels of 4, 6, 8, and 10 for each test image. A series of experiments can significantly demonstrate the superior performance of the proposed technique, which can deal with multilevel thresholding color image segmentation excellently.

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