Integration of color and texture cues in a rough set–based segmentation method

Lizarraga-Morales, Rocio A.; Sánchez-Yáñez, Raúl E.; Ayala-Ramírez, Víctor; Correa-Tome, Fernando E.

doi:10.1117/1.jei.23.2.023003

Cited by 6 publications

(3 citation statements)

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“…Rough sets theory is a data analysis theory to quantify inaccurate, inconsistent and incomplete information and knowledge, it was proposed by the Polish mathematician Pawlak in 1982(Lizarraga-Morales et al 2014. In this theory, the nonempty finite set of objects is called the universe, denoted U.…”

Section: Smoke Image Segmentation Based On Rough Setmentioning

confidence: 99%

A smoke image segmentation algorithm based on rough set and region growing

Wang¹,

Chen²

2019

J. For. Sci.

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Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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Section: Smoke Image Segmentation Based On Rough Setmentioning

confidence: 99%

A smoke image segmentation algorithm based on rough set and region growing

Wang¹,

Chen²

2019

J. For. Sci.

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“…(10)- (12) are calculated as functions of image intensity values, as shown in Eqs. (13) and (14). After obtaining the gradient image G, its histogram is calculated, and the threshold variance σ 2 T is obtained from this histogram.…”

Section: Automatic Variance Threshold Selectionmentioning

confidence: 99%

“…Here, the SD images are used only to present the ISM methodology. However, despite its simplicity, the SD images have obtained good results in segmentation applications, as in the work of Lizarraga-Morales et al 14 The results obtained were evaluated using the normalized probabilistic random (NPR) index. The NPR index is a robust methodology developed by Unnikrishnan et al 15,16 to evaluate the quality of image segmentations.…”

Section: Introductionmentioning

confidence: 99%

Integral split-and-merge methodology for real-time image segmentation

Correa-Tome

Sánchez-Yáñez

2015

J. Electron. Imaging

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Abstract. The segmentation of images is a critical step in many computer vision applications. Additionally, some applications require the achievement of acceptable segmentation quality while the algorithm is executed in real time. In this study, we present a split-and-merge segmentation methodology that uses integral images to improve the execution time. We call our methodology integral split and merge (ISM) segmentation. The integral images are used here to calculate statistics of the image regions in constant time. Those statistics are used to guide the splitting process by identifying the homogeneous regions in the image. We also propose a merge criterion that performs connected component analysis of the homogeneous regions. Moreover, the merging procedure is able to group regions of the image showing gradients. Furthermore, the number of regions resulting from the segmentation process is determined automatically. In a series of tests, we compare ISM against other state-of-theart algorithms. The results from the tests show that our ISM methodology obtains image segmentations with a comparable quality, using a simple texture descriptor instead of a combination of color-texture descriptors. The proposed ISM methodology also has a piecewise linear computational complexity, resulting in an algorithm fast enough to be executed in real time.

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