Image segmentation by fusion of low level and domain specific information via Markov Random Fields

Karadaǧ, Özge Öztimur; Vural, Fatoş T. Yarman

doi:10.1016/j.patrec.2014.05.010

Cited by 15 publications

(3 citation statements)

References 22 publications

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“…This method combines colour information, NS, and wavelet domain texture information to automatically and efficiently segment colour images. Under the MRF framework, Oztimur Karadag and Yarman Vural [11] proposed a new image segmentation approach fusing a set of top-down and bottom-up segmentation maps. The top-down segmentation map is obtained from the priori information that is known as domain-specific information, while the bottom-up segmentation map obtained through altering the unsupervised segmentation algorithm's parameters.…”

Section: Introductionmentioning

confidence: 99%

Adaptive image segmentation algorithm under the constraint of edge posterior probability

Gong

Lan

Yang

et al. 2017

IET Computer Vision

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Image segmentation is an important step in image processing, but contemporary segmentation algorithms have problems such as poor anti-noise performance, over-segmentation, and imprecise results. To solve these problems, the authors proposed an adaptive image segmentation algorithm under the constraint of edge posterior probability. This algorithm first resolves the problem of over-segmentation by improving the watershed algorithm. Then, the algorithm automatically decides whether to adopt the edge threshold segmentation resulting from the watershed algorithm based on the proposed edge posterior probability model. Experiments showed that the proposed algorithm has excellent anti-noise performance, highly precise segmentation result, and are useful in effectively segmenting low-contrast images.

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Section: Introductionmentioning

confidence: 99%

Adaptive image segmentation algorithm under the constraint of edge posterior probability

Gong

Lan

Yang

et al. 2017

IET Computer Vision

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“…Image fusion technology began to draw attention in the 1980s, at that time, image fusion was nothing but simple weighted average. After that, the technology gradually caught on and people started to apply it in the analysis and processing of remotesensing multi-spectral images [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-source and Multi-feature Image Information Fusion Based on Compressive Sensing

Li¹,

Jiang²

2016

TELKOMNIKA

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“…Graph cuts as one of the most important graph-based methods was introduced in 2001 [16]. Statistical model-based methods use a statistical model that characterizes pixel values [17,18]. Machine learning-based methods use machine learning techniques for image segmentation [19][20][21][22][23][24][25].…”

mentioning

confidence: 99%

Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine

Rezaei¹,

rezaei²,

Rezaei³

2023

jist

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Foreground-background image segmentation has been an important research problem. It is one of the main tasks in the field of computer vision whose purpose is detecting variations in image sequences. It provides candidate objects for further attentional selection, e.g., in video surveillance. In this paper, we introduce an automatic and efficient Foregroundbackground segmentation. The proposed method starts with the detection of visually salient image regions with a saliency map that uses Fourier transform and a Gaussian filter. Then, each point in the maps classifies as salient or non-salient using a binary threshold. Next, a hole filling operator is applied for filling holes in the achieved image, and the area-opening method is used for removing small objects from the image. For better separation of the foreground and background, dilation and erosion operators are also used. Erosion and dilation operators are applied for shrinking and expanding the achieved region. Afterward, the foreground and background samples are achieved. Because the number of these data is large, Kmeans clustering is used as a sampling technique to restrict computational efforts in the region of interest. K cluster centers for each region are set for training of Support Vector Machine (SVM). SVM, as a powerful binary classifier, is used to segment the interest area from the background. The proposed method is applied on a benchmark dataset consisting of 1000 images and experimental results demonstrate the supremacy of the proposed method to some other foreground-background segmentation methods in terms of ER, VI, GCE, and PRI.

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Image segmentation by fusion of low level and domain specific information via Markov Random Fields

Cited by 15 publications

References 22 publications

Adaptive image segmentation algorithm under the constraint of edge posterior probability

Adaptive image segmentation algorithm under the constraint of edge posterior probability

Multi-source and Multi-feature Image Information Fusion Based on Compressive Sensing

Foreground-Back ground Segmentation using K-Means Clustering Algorithm and Support Vector Machine

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