A system for textile design patterns retrieval. Part I: Design patterns extraction by adaptive and efficient color image segmentation method

Lachkar, A.; Benslimane, Rachid; D’Orazio, L.; Martuscelli, E.

doi:10.1533/joti.2005.0124

Cited by 9 publications

(10 citation statements)

References 6 publications

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“…Our method conquers the problem of local optimization by inter-scale iteration, that is, the coarser segmentation at scale (nÀ1) is projected to scale n as the initial, as shown in Figure 7(a) and (b). Figure 8 and the corresponding data in Table 1 illustrate the performance of the modified model in Equation (12) against the traditional model in Equation (11), from which we note that our proposed approach can achieve an obviously good identification accuracy. The reason for this improvement is that our proposed approach takes the spatial noise into account according to the characteristics of the jacquard fabric image.…”

Section: Resultsmentioning

confidence: 97%

“…Under the influence of yarn color, weave structure and the illumination condition, as mentioned above, using Equation (11) in practice it is difficult to describe the distribution of jacquard fabric images. Thus, we propose a modified model for the PDF as follows: …”

Section: Multiresolution Markov Random Modelmentioning

confidence: 99%

“…Despite the fact that much work 9–13 has been conducted for texture segmentation, there is little research aimed at jacquard fabric images. Zhang Y 14 and Zhang D 15 used wavelet transform and K -means clustering for unsupervised identification of Jacquard warp-knitted patterns with nets.…”

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confidence: 99%

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Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Zhang

Jiang

Yao

et al. 2013

Textile Research Journal

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In this paper, we propose a new approach to intelligently segment jacquard warp-knitted fabric images by combining wavelet texture decomposition, multiresolution Markov random field (MRF) modeling and Bayesian parameter estimation. Firstly, we use a flat scanner to capture eight-bit grayscale images of jacquard fabrics and adopt the Gaussian low-pass filter to decrease pixel variation due to discordant light reflection arising from uneven jacquard fabric surface. To overcome the incapacity of single resolution, multiresolution wavelet texture decomposition is employed, inspired by human visual sense procedure. Next, both intra-scale and inter-scale information are taken into account by the MRF model, in which a modified feature field model of the MRF, containing spatial noise with zero-mean Gaussian distribution, is presented in light of the inherent characteristics of jacquard warp-knitted fabric image. Afterward, an adaptive weighting function is used to weaken the defect of the potential parameter set empirically during the process of parameter estimation and image segmentation. Experimental results, used to verify the performance of the proposed algorithm especially the main novelties, prove that the approach is feasible and applicable.

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Section: Resultsmentioning

confidence: 97%

Section: Multiresolution Markov Random Modelmentioning

confidence: 99%

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Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Zhang

Jiang

Yao

et al. 2013

Textile Research Journal

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show abstract

“…When image segmentation is viewed as a clustering process, many approaches to image segmentation have widely been used to solve the segmentation by connected color region (e.g., K-means [2]- [3], fuzzy c-means [4], Gaussian mixture model [5]- [6]- [7]). When the problem of color image segmentation is approached by a color classification, it is however a gap in the sense of spatial representation of different objects for each color region, a classification based on color alone is not sufficient.…”

Section: Introductionmentioning

confidence: 99%

Spatial-region classiﬁcation by Min-Connected algorithm for unsupervised segmentation

Alaoui¹,

Jakimi

Elbermi

2012

Journal of Advanced Computer Science &Amp; Technology

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This work lies within the scope of color image segmentation by spatial-region classification. The spatial distribution of objects in each region of image is difficult to be identified when the region are non-connected clusters. A standard of color identification by conventional methods of segmentation remains weak for capturing the spatial dispersion of the various objects of the same color region. We propose to apply a spatial classification to characterize geographical connected sets that represent the same regions. The originality of this paper lies in our new min-connected algorithm which is derived from a spatial-color compactness model. Our work is a hybrid segmentation that takes into account the distribution of colors in the color space and the spatial location of colors in the image plane. Experimental tests on synthetic and real images show that our technique leads to promising results for segmentation.

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“…Final segmentation result is acquired by projection from coarse resolution to fine resolution. According to the MRF and Bayesian theory, image segmentation is seeking an approximate global optimal estimation of X by the criteria of SMAP [21][22], that is http://www.jeffjournal.org Volume 11, Issue 3 -2016…”

mentioning

confidence: 99%

Image Segmentation of Printed Fabrics with Hierarchical Improved Markov Random Field in the Wavelet Domain

Jing

et al. 2016

Journal of Engineered Fibers and Fabrics

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An improved MRF algorithm-hierarchical Gauss Markov Random Field model in the wavelet domain is presented for fabric image segmentation in this paper, which obtains the relation of inter-scale dependency from the feature field modeling and label field modeling. The Gauss-Markov random field modeling is usually adopted to feature field modeling. The label field modeling employs the inter-scale causal MRF model and the intra-scale non-causal MRF model. After that, parameter estimation is the essential section in the inter-scale, enhancing modeling capabilities of the pixels partial dependency. Sequential maximum a posterior criterion is applied to achieve the results of image segmentation. Comparisons with other hybrid schemes, results are indicated that performance of the presented algorithm is effective and accurate, in terms of classification accuracy and kappa coefficient, for patterned fabric images.

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A system for textile design patterns retrieval. Part I: Design patterns extraction by adaptive and efficient color image segmentation method

Cited by 9 publications

References 6 publications

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

Spatial-region classiﬁcation by Min-Connected algorithm for unsupervised segmentation

Image Segmentation of Printed Fabrics with Hierarchical Improved Markov Random Field in the Wavelet Domain

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