Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO)

Feng, Du; Shi, Wenyu; Liangzhou, Chen; Deng, Yong; Zhu, Zhigang

doi:10.1016/j.patrec.2004.11.002

Cited by 126 publications

(23 citation statements)

References 13 publications

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“…A related approach was introduced in [19], where PSO was used to tune thresholds in 2D-histograms, maximizing the entropy to segment infrared images.…”

Section: Related Workmentioning

confidence: 99%

A Swarm Intelligence inspired algorithm for contour detection in images

Kirchmaier

Hawe

Diepold

2013

Applied Soft Computing

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Swarm Intelligence uses a set of agents which are able to move and gather local information in a search space and utilize communication, limited memory, and intelligence for problem solving. In this work, we present an agent-based algorithm which is specifically tailored to detect contours in images. Following a novel movement and communication scheme, the agents are able to position themselves distributed over the entire image to cover all important image positions. To generate global contours, the agents examine the local windowed image information, and based on a set of fitness functions and via communicating with each other, they establish connections. Instead of a centralized paradigm, the global solution is discovered by some principal rules each agent is following. The algorithm is independent of object models or training steps. In our evaluation we focus on boundary detection as a major step towards image segmentation. We therefore evaluate our algorithm using the Berkeley Segmentation Dataset (BSDS) and compare its performance to existing methods via the BSDS benchmark and Pratt's Figure of Merit.

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“…A related approach was introduced in [19], where PSO was used to tune thresholds in 2D-histograms, maximizing the entropy to segment infrared images.…”

Section: Related Workmentioning

confidence: 99%

A Swarm Intelligence inspired algorithm for contour detection in images

Kirchmaier

Hawe

Diepold

2013

Applied Soft Computing

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“…A subsequent comparison may lead to a fuzzy classification, if we are able to evaluate, by means of statistical tools (as proposed in [1]) the degree of concordance of each pixel to each one of those identified regions. Of course, other complementary techniques may be also considered (see, e.g., [6,8,9,13,16]). …”

Section: Final Commentsmentioning

confidence: 99%

A coloring fuzzy graph approach for image classification

Gómez

Montero

Yáñez

2006

Information Sciences

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“…EAs, such as Genetic Algorithm (GA) [19], Evolutionary Programming (EP) [20,21], and Evolution Strategy (ES) [22,23], are inspired from natural selection and survival of the fittest in the natural world. Owing to the simplicity and flexibility of EAs and SI, various methods are developed for image engineering, which almost 2 Mathematical Problems in Engineering cover all related fields, including image enhancement, image denoising, super resolution restoration, image registration, digital watermarking, edge detection, image fusion, image compression, texture classification, image retrieval, image recognition, and image segmentation [24][25][26][27][28][29][30][31][32][33][34][35][36][37]. Similar to the existing nature-inspired algorithms, a new mimic algorithms on the basis of the behavior of grey wolves was proposed in the last few years.…”

Section: Introductionmentioning

confidence: 99%

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

et al. 2018

Mathematical Problems in Engineering

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An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

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Infrared image segmentation with 2-D maximum entropy method based on particle swarm optimization (PSO)

Cited by 126 publications

References 13 publications

A Swarm Intelligence inspired algorithm for contour detection in images

A Swarm Intelligence inspired algorithm for contour detection in images

A coloring fuzzy graph approach for image classification

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

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