Improving textures discrimination in the local binary patterns technique by using symmetry &amp;amp; group theory

Orjuela, S. A.; Quiñones, Rolando; Ortiz-Jaramillo, B.; Rooms, Filip; Keyser, Robain De; Philips, Wilfried

doi:10.1109/icdsp.2011.6004978

Cited by 9 publications

(6 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NTLBP [29] , SLBP [68], LBC [69], NLBP [70], RSCILBP [71], NELBP [28], HLBP [72] and scLBP S [73] are closely related LBP variants which are designed to make better use of the nonuniform patterns instead of discarding them, with illustrations given in Fig. 4.…”

Section: Heuristically Grouping Lbps Into Classesmentioning

confidence: 99%

Local binary features for texture classification: Taxonomy and experimental study

Liu

Fieguth

Guo

et al. 2017

Pattern Recognition

326

175

View full text Add to dashboard Cite

Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. Truly a large number of LBP variants has been proposed, to the point that it can become overwhelming to grasp their respective strengths and weaknesses, and there is a need for a comprehensive study regarding the prominent LBP-related strategies. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered.

show abstract

Section: Heuristically Grouping Lbps Into Classesmentioning

confidence: 99%

Local binary features for texture classification: Taxonomy and experimental study

Liu

Fieguth

Guo

et al. 2017

Pattern Recognition

326

175

View full text Add to dashboard Cite

show abstract

“…20, 23 We use a computer with an intel I5 processor at 1.5 Ghz, with 4GB of ram and a NVIDIA GeForce GT 525M. The results of this approach show that accuracy in recognizing structural defects as well as in describing textural changes in carpets is higher when using the GLBP technique compared to the LBPSym technique.…”

Section: Resultsmentioning

confidence: 97%

“…22 Symmetric versions of intensity variations in the neighbourhood can be labelled with the same LBP-code using a look up table. 23 A histogram of the occurrences of the LBP-codes on an image is used as a feature to characterize the textural properties of the image. A real time implementation of the LBP algorithms can be performed by computing the interpolation, Boolean comparisons, LBP-code computation and look-up matching in parallel for blocks of pixels using a Graphic Processing Unit.…”

Section: Introductionmentioning

confidence: 99%

Fast texture evaluation of textiles using the GLBP technique in GPU architecture

et al. 2012

Self Cite

View full text Add to dashboard Cite

Commonly, visual inspection tasks in the textile industry are performed by human experts. The major drawback of this type of inspection is the human subjectivity, which affects accuracy and repeatability. Objectivity, accuracy and repeatability can be achieved by analysing visual characteristics of the products using computer vision. Particularly, automatic real time inspection systems based on texture analysis can be implemented using Local Binary Pattern (LBP) techniques. A recent variation of the LBP techniques, named Geometric Local Binary Pattern (GLBP) technique, showed an increase in the performance for detecting small changes of local texture. In this paper a real time implementation of the algorithm is presented by using a Graphic Processing Unit (GPU). The LBP and GLBP techniques are compared in terms of speed and accuracy while implemented on a Central Processing Unit (CPU) and GPU environments. Algorithms are tested for detecting defects in fabrics as well as for evaluating global deviations of texture, which are due to the degradation of the surface in carpets. Results show that higher discriminant power between similar textures is obtained when using the GLBP technique

show abstract

“…Grouping patterns makes the representation becomes more compact and invariant to noise. We group mirror, complement and rotational versions of LBP patterns using look up tables [37].…”

Section: Texture Feature Extractionmentioning

confidence: 99%

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

Improving textures discrimination in the local binary patterns technique by using symmetry & group theory

Cited by 9 publications

References 10 publications

Local binary features for texture classification: Taxonomy and experimental study

Local binary features for texture classification: Taxonomy and experimental study

Fast texture evaluation of textiles using the GLBP technique in GPU architecture

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

Contact Info

Product

Resources

About