Texture Segmentation Methods Based on Combinatorial of Morphological and Statistical Operations

Kumar, V. Vijaya; Reddy, B. Eswara; Rao, Amrith Raj; Raju, U. S. N.

doi:10.4304/jmm.3.1.36-40

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Matlab® Image Processing Toolbox uses Equation (7) to convert a colour image pixel to a greyscale value by forming a weighted sum of the R , G , and Wavelet transform Wavelet transform is used by many researchers for texture analysis (Reis & Bauer, 2009) and texture segmentation (Kumar, Raju, Mani, & Rao, 2008;Lu, Chung, & Chen, 1997;Unser, 1995) widely applied in fabric defect detection (Hu & Tsai, 2000a, 2000bJasper, Joines, & Brenzovich, 2005;Jianli & Baoqi, 2007), characterization of fabric texture (Henry, Grantham, Yun, & Michael, 2005;Hu & Tsai, 2000;Shakher, Ishtiaque, Singh, & Zaidi, 2004), and for ratings of fabric pilling (Chen, Xu, Chen, Wang, & Li, 2009;Kim & Kang, 2005;Palmer & Wang, 2003Zhang, Wang, & Palmer, 2007a, 2007b.…”

Section: Cloth Fell Positionmentioning

confidence: 99%

Measurement of cloth fell position using image analysis

Yıldırım

Başer

2011

Journal of the Textile Institute

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In this paper, a computer-aided image-analyzing method for measuring cloth fell position directly in a non-contact manner is introduced. The method measures the cloth fell position from an image taken by means of a camera which is mounted over the woven fabric and warp region including cloth fell on a weaving loom. Laser light lines are used as reference lines. The image containing the reference lines, the fabric and warp system is decomposed using wavelet transforms. Horizontal detail coefficient matrix is filtered with local standard deviation of a kernel for textural feature extraction and grouping achieved by means of k -means clustering in order to determine the cloth fell position. Due to the fact that reference line positions remain the same on the fabric, cloth fell position alterations can be calculated by measuring the distance between the reference line and the cloth fell. The conversion of the distance between the cloth fell position and the reference line, measured by the number of pixels, can be accomplished by using the known distance between reference lines as a means of calibration.

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Section: Cloth Fell Positionmentioning

confidence: 99%

Measurement of cloth fell position using image analysis

Yıldırım

Başer

2011

Journal of the Textile Institute

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“…Texture plays an important role in image classification, retrieval, segmentation and age classification. In Literature various texture based methods are derived for image classification [ 15,16,17], face recognition [18,19,20], age and facial expressions identification [21,22], segmentation [23,24,25] image retrieval [26,27,28,29,30,31,32 ]. Texture is salient and indispensable feature for CBIR.…”

Section: Introductionmentioning

confidence: 99%

Image Retrieval using Quantized Local Binary Pattern

Latha¹,

Kumar²,

Obulesu³

2016

IJCA

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Image retrieval is one of the main topics in the field of computer vision and pattern recognition. Local descriptors are gaining more and more recognition in recent years as these descriptors are capable enough to identify the unique features, which suitably and uniquely describe any image for recognition and retrieval. One of the popular and efficient frame works for capturing texture information precisely is the Local binary pattern (LBP). LBP descriptors perform well in varying pose, illumination and lighting conditions. LBP is a structural approach and plays significant role in wide range of applications. One of the disadvantages with LBP based frame work is its dimensionality. The dimensionality of LBP increases, if one increases the number of neighboring pixels. Further statistical approaches gained lot of significance in image retrieval and LBP based methods raises high dimensionality and complexity issues, in deriving statistical features. The present paper addresses these two issues by quantizing the LBP code, to reduce dimensionality and by deriving GLCM features on quantized LBP. The proposed method is experimented on Corel database and compared with other existing methods. The experimental results indicate the high retrieval rate by the proposed method over the existing methods.

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“…The opening and closing. [5] In this study, the closing mathematical operator has been used to close and eliminate the unwanted objects over the building roofs. The successes of this method are about the elimination of the unwanted objects and keep up the structure of the building.…”

Section: Introductionmentioning

confidence: 99%

Building extraction with morphology

Acar

Bayram

2009

2009 4th International Conference on Recent Advances in Space Technologies

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Building extraction is one of the biggest problems of the photogrammetry. Automatic building extraction from aerial imagery in an urban environment is the main focus of this study. The strategy of our approach is to reduce the complexity of the image with the mathematical morphology. True color aerial images have been used as the information source.In this study, the mathematical morphologic operator has been used to close and eliminate the unwanted objects over the building roofs. The successes of this method are about the elimination of the unwanted objects and keep up the structure of the building.Because of different style of urbanization, four different location and six high resolution ikonos satellite image have been used. The results are %85 of success.

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Texture Segmentation Methods Based on Combinatorial of Morphological and Statistical Operations

Cited by 7 publications

References 10 publications

Measurement of cloth fell position using image analysis

Measurement of cloth fell position using image analysis

Image Retrieval using Quantized Local Binary Pattern

Building extraction with morphology

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