Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images

Orjuela, S. A.; Rooms, Filip; Philips, Wilfried; Meulemeester, Simon De; Keyser, Robain De

doi:10.1109/andescon.2010.5632443

Cited by 3 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some parts of the method have been explained in detail in previous papers [13][14][15][23][24][25][26]. In this paper we present an optimal combination of the previous findings.…”

Section: Introductionmentioning

confidence: 78%

“…This linear description has the benefit that it gives the same probability to each AR grade. We propose to optimally combine the κ-values using regression models for linearly representing the AR grades [23]. One linear regression model is built for each AR reference.…”

Section: Features Combinationmentioning

confidence: 99%

“…The performance of classical scanners has been improved by placing the carpet on a drum, which results in better capturing of the piles defining the structure of the carpet. We have proposed an automatic grading system based on image analysis algorithms using images obtained from the scanner based on a drum together with photographs [23]. Linear models for quantifying changes in appearance of fatigued specimens are computed by combining texture features based on local binary patterns from both intensity and range images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic grading of appearance retention of carpets using intensity and range images

Vargas

Ortiz-Jaramillo

Vansteenkiste

et al. 2012

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. Textiles are mainly used for decoration and protection. In both cases, original appearance and its retention are important factors for customers. Therefore, evaluation of appearance parameters are critical for quality assurance purposes, while and after manufacturing, to determine lifetime and/or beauty of textile products. Particularly, appearance retention of textile products is commonly certified with grades, which are currently assigned by human experts. However, manufacturers would prefer a more objective system. In this paper we present an objective system for grading appearance retention, particularly, for textile floor coverings.Changes in appearance are quantified by using linear regression models on texture features extracted from intensity and range images. Range images are obtained by our own laser scanner, reconstructing the carpet surface using two methods that have been previously presented. We extract texture features using a variant of the local binary pattern technique based on detecting those patterns whose frequencies are related to the appearance retention grades. We test models for eight types of carpets. Results show that the proposed approach describes the degree of wear with a precision within the range allowed to human inspectors by international standards. The methodology followed in this experiment has been designed to be general for evaluating global deviation of texture in other types of textiles as well as other surface materials.

show abstract

“…Some parts of the method have been explained in detail in previous papers [13][14][15][23][24][25][26]. In this paper we present an optimal combination of the previous findings.…”

Section: Introductionmentioning

confidence: 78%

Section: Features Combinationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic grading of appearance retention of carpets using intensity and range images

Vargas

Ortiz-Jaramillo

Vansteenkiste

et al. 2012

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…They still are not good enough to meet the required discrimination of the AR grades imposed by standards on a sufficient wide of carpets [10]. Recently, we have proposed an automatic assessment system based on extracting texture parameters from intensity color and depth (range) images [11]. The range images are obtained using our own scanner specifically designed for carpets [12].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

Quiñones

Orjuela

Ortiz-Jaramillo

et al. 2011

Advanced Concepts for Intelligent Vision Systems

Self Cite

View full text Add to dashboard Cite

Abstract. Quality assessment in carpet manufacturing is performed by humans who evaluate the appearance retention (AR) grade on carpet samples. To quantify the AR grades objectively, different research based on computer vision have been developed. Among them Local Binary Pattern (LBP) and its variations has shown promising results. Nevertheless, the requirements of quality assessment on a wide range of carpets have not been met yet. One of the difficulties is to distinguish between consecutive AR grades in carpets. For this, we adopt an extension of LBP called Geometrical Local Binary Patterns (GLBP) that we recently proposed. The basis of GLBP is to evaluate the grey scale differences between adjacent points defined on a path in a neighbourhood. Symmetries of the paths in the GLBPs are evaluated. The proposed technique is compared with an invariant rotational mirror based LBP technique. The results show that the GLBP technique performs better to distinguish consecutive AR grades in carpets.

show abstract

“…9,11,12 Also, approaches related to assessment by using changes in intensity color have been developed. 13 All these researches have shown promising results, mainly to assess carpets type cut and loop pile. For carpets with high pile construction (shaggy carpets), an automatic assessment has not been fully successful given that, for some of them, the texture and the color do not change strongly.…”

mentioning

confidence: 99%

Measuring hairiness in carpets by using surface metrology

et al. 2011

Self Cite

View full text Add to dashboard Cite

Recently, an automatic system for grading appearance retention in carpets using our own scanner and image analysis techniques was proposed. A system for carpets with low pile construction and without color patterns has been developed. Appearance changes in carpets with high pile construction were still not well detected. We present an approach based on surface metrology that extract information given by the hairs on the carpet surface. These features are complementary to the texture features previously explored. By combining both features, we expand the use of the automatic grading system including some carpets types with high pile construction.

show abstract

Automated wear label assessment in carpets by using local binary pattern statistics on depth and intensity images

Cited by 3 publications

References 29 publications

Automatic grading of appearance retention of carpets using intensity and range images

Automatic grading of appearance retention of carpets using intensity and range images

Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

Measuring hairiness in carpets by using surface metrology

Contact Info

Product

Resources

About