Regularity analysis for patterned texture inspection

This paper considers regularity analysis for patterned texture material inspection. Patterned texture-like fabric is built on a repetitive unit of a pattern. Regularity is one of the most important features in many textures. In this paper, a new patterned texture inspection approach called the regular bands (RB) method is described. First, the properties of textures and the meaning of regularity measurements are presented. Next, traditional regularity analysis for patterned textures is introduced. Many traditional approaches such as co-occurrence matrices, autocorrelation, traditional image subtraction and hash function are based on the concept of periodicity. These approaches have been applied for image retrieval, image synthesis, and defect detection of patterned textures. In this paper, a new measure of periodicity for patterned textures is described. The Regular Bands method is based on the idea of periodicity. A detailed description of the RB method with definitions, procedures, and explanations is given. There is also a detailed evaluation using the Regular Bands of some patterned textures. Three kinds of patterned fabric samples are used in the evaluation and a high detection success rate is achieved. Finally, there is a discussion of the method and some conclusions. © 2006 IEEE.published_or_final_versio

Robust Defect Detection in Plain and Twill Fabric Using Directional Bollinger Bands

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Patterned fabric defect detection using a motif-based approach

This paper proposed a patterned fabric defect detection method for sixteen out of seventeen wallpaper groups using a motif-based approach. From the symmetry properties of motifs, the energy of moving subtraction and its variance among motifs are mapped onto an energy-variance space. By learning the distribution of defect-free and defective patterns in this space, boundaries conditions can be determined for defect detection purpose. The proposed method is evaluated on four wallpaper categories, from which all 16 wallpaper groups can be generalized. Altogether, 160 defect-free lattices samples are used for learning the decision boundaries; and 200 other defect-free and 138 other defective samples are used for testing. An overall detection accuracy has reached 93.61%, which outperforms previous approaches. © 2007 IEEE.published_or_final_versionThe 14th IEEE International Conference on Image Processing (ICIP), San Antonio, TX., 16-19 September 2007. In Proceedings of 14th ICIP, 2007, v. 2, p. II-33-II-3

Novel geometric coordination registration in cone-beam computed tomogram

Paper ID: AIPR-140701-9The use of cone-beam computed tomography (CBCT) in medical field can help the clinicians to visualize the hard tissues in head and neck region via a cylindrical field of view (FOV). The images are usually presented with reconstructed three-dimensional (3D) imaging and its orthogonal (x-, y-and z-planes) images. Spatial relationship of the structures in these orthogonal views is important for diagnosis of diseases as well as planning for treatment. However, the non-standardized positioning of the object during the CBCT data acquisition often induces errors in measurement since orthogonal images cut at different planes might look similar. In order to solve the problem, this paper proposes an effective mapping from the Cartesian coordinates of a cube physically to its respective coordinates in 3D imaging. Therefore, the object (real physical domain) and the imaging (computerized virtual domain) can be linked up and registered. In this way, the geometric coordination of the object/imaging can be defined and its orthogonal images would be fixed on defined planes. The images can then be measured with vector information and serial imagings can also be directly compared. © 2014 IEEE.published_or_final_versio

Validation of a novel geometric coordination registration using manual and semi-automatic registration in cone-beam computed tomogram

Session - Image Processing: Machine Vision Applications 9Cartesian coordinates define on a physical cubic corner (CC) with the corner tip as the origin and three corresponding line angles as (x, y, z)-axes. In its image (virtual) domains such as these obtained by cone-beam computed tomography (CBCT) and optical surface scanning, a single coordinate can then be registered based on the CC. The advantage of using a CC in registration is simple and accurate physical coordinate measurement. The accuracy of image-to-physical (IP) and imageto-image (II) transformations, measured by target registration error (TRE), can then be validated by comparing coordinates of target points in the virtual domains to that of the physical control. For the CBCT, the registration may be performed manually using a surgical planning software SimPlant Pro (manual registration (MR)) or semi-automatically using MeshLab and 3D Slicer (semiautomatic registration (SR)) matching the virtual display axes to the corresponding (x-y-z)-axes. This study aims to validate the use of CC as a surgical stereotactic marker by measuring TRE in MR and SR respectively. Mean TRE is 0.56 +/- 0.24 mm for MR and 0.39 +/- 0.21 mm for SR. The SR results in a more accurate registration than the MR and point-based registration with 20 fiducial points. TRE of the MR is less than 1.0 mm and still acceptable clinically.postprin

Image calibration and registration in cone-beam computed tomogram for measuring the accuracy of computer-aided implant surgery

Medical radiography is the use of radiation to “see through” a human body without breaching its integrity (surface). With computed tomography (CT)/cone beam computed tomography (CBCT), three-dimensional (3D) imaging can be produced. These imagings not only facilitate disease diagnosis but also enable computer-aided surgical planning/navigation. In dentistry, the common method for transfer of the virtual surgical planning to the patient (reality) is the use of surgical stent either with a preloaded planning (static) like a channel or a real time surgical navigation (dynamic) after registration with fiducial markers (RF). This paper describes using the corner of a cube as a radiopaque fiducial marker on an acrylic (plastic) stent, this RF allows robust calibration and registration of Cartesian (x, y, z)- coordinates for linking up the patient (reality) and the imaging (virtuality) and hence the surgical planning can be transferred in either static or dynamic way. The accuracy of computer-aided implant surgery was measured with reference to coordinates. In our preliminary model surgery, a dental implant was planned virtually and placed with preloaded surgical guide. The deviation of the placed implant apex from the planning was x=+0.56mm [more right], y=- 0.05mm [deeper], z=-0.26mm [more lingual]) which was within clinically 2mm safety range. For comparison with the virtual planning, the physically placed implant was CT/CBCT scanned and errors may be introduced. The difference of the actual implant apex to the virtual apex was x=0.00mm, y=+0.21mm [shallower], z=-1.35mm [more lingual] and this should be brought in mind when interpret the results

Novel method for patterned fabric inspection using Bollinger bands

This paper introduces a new application of Bollinger bands for defect detection of patterned fabric. A literature review on previous designed methods for patterned fabric defect detection will be depicted. For data analysis, Bollinger bands are calculated based on standard deviation and are originally used in the financial market as an oversold or overbought indicator for stock. The Bollinger bands method is an efficient, fast and shift-invariant approach, that can segment out the defective regions on the patterned fabric with clear and crystal clean images. The new approach is immune of the alignment problem that often happens in previous methods. In this paper, the upper band and lower band of Bollinger bands, which are sensitive to any subtle change in the input data, have been developed for use to indicate the defective areas in patterned fabric. The number of standard deviation and length of time of Bollinger bands can be easily determined to obtain excellent detection results. The proposed method has been evaluated on three different patterned fabrics. In total, 165 defect-free and 171 defective images have been used in the evaluation, where 98.59% accuracy on inspection has been achieved. © 2006 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Motif-based defect detection for patterned fabric

This paper proposes a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2D patterned texture. It assumes that most patterned texture can be decomposed into lattices and their constituents-motifs. It then utilizes the symmetry property of motifs to calculate the energy of moving subtraction and its variance among different motifs. By learning the distribution of these values over a number of defect-free patterns, boundary conditions for discerning defective and defect-free patterns can be determined. This paper presents the theoretical foundation of the method, and defines the relations between motifs and lattice, from which a new concept called energy of moving subtraction is derived using norm metric measurement between a collection of circular shift matrices of motif and itself. It has been shown in this paper that the energy of moving subtraction amplifies the defect information of the defective motif. Together with its variance, an energy-variance space is further defined where decision boundaries are drawn for classifying defective and defect-free motifs. As the 16 wallpaper groups of patterned fabric can be transformed into three major groups, the proposed method is evaluated over these three major groups, from which 160 defect-free lattices samples are used for defining the decision boundaries, with 140 defect-free and 113 defective samples used for testing. An overall detection success rate of 93.32% is achieved for the proposed method. No other generalized approach can achieve this success rate has been reported before, and hence this result outperforms all other previously published approaches. © 2007 Elsevier Ltd. All rights reserved.link_to_subscribed_fulltex

Automated fabric defect detection-A review

This paper provides a review of automated fabric defect detection methods developed in recent years. Fabric defect detection, as a popular topic in automation, is a necessary and essential step of quality control in the textile manufacturing industry. In categorizing these methods broadly, a major group is regarded as non-motif-based while a minor group is treated as motif-based. Non-motif-based approaches are conventional, whereas the motif-based approach is novel in utilizing motif as a basic manipulation unit. Compared with previously published review papers on fabric inspection, this paper firstly offers an up-to-date survey of different defect detection methods and describes their characteristics, strengths and weaknesses. Secondly, it employs a wider classification of methods and divides them into seven approaches (statistical, spectral, model-based, learning, structural, hybrid, and motif-based) and performs a comparative study across these methods. Thirdly, it also presents a qualitative analysis accompanied by results, including detection success rate for every method it has reviewed. Lastly, insights, synergy and future research directions are discussed. This paper shall benefit researchers and practitioners alike in image processing and computer vision fields in understanding the characteristics of the different defect detection approaches. © 2011 Elsevier B.V. All rights reserved.link_to_subscribed_fulltex

Performance evaluation for motif-based patterned texture defect detection

This paper carries an extensive evaluation on the performance of a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2-D patterned texture. The motif-based method evolves from the concept that every wallpaper group is defined by a lattice, which contains a further constituent-motif. It utilizes the symmetry properties of motifs to calculate the energy of moving subtraction and its variance among motifs. Decision boundaries are determined by learning the distribution of those values among the defect-free and defective patterns in the energy-variance space. In this paper, shape transform for irregular motif has been demonstrated according to the three basic motif shapes: rectangle, triangle, and parallelogram. An error analysis for the misclassifications has also been delivered. In the database of fabrics and other patterned textures, a total of 381 defect-free lattices are used for formulation of boundaries while further 340 defect-free and 233 defective lattices are for testing. The motif-based method has a consistent result and reaches adetection success rate of 93.86%. Note to Practitioners-This paper is motivated by the need to develop a generalized approach that can detect defects on most of the 2-D patterned textures defined so far. It proposes a novel motif-based defect detection method for 16 out of 17 wallpapergroups. A new concept called energy of moving subtraction is defined using norm metric measurement between a collection of circular shift matrices of motif and itself. Together with its variance, an energy-variance space is defined where decision boundaries are drawn for classifying defective and defect-free motifs. The method has been evaluated by two categories of patterned textures. The first category is produced from patterned fabric samples from p2, pmm, p4m, pm, and cm groups. The second category is produced from various patterned texture samples from p4, pg, pmg, cmm, p4g, pgg, p31m, p6, p6m, and p3m1 groups. For the former, a total of 280 defect-free lattices samples are used for deriving the decision boundaries, and further 340 defect-free and 206 defective lattices are used for evaluation. The detection success rate is found to be 93.92%. For the latter, they are the images from painting, tile, ornament, painted porcelain, vessel, earthenware, mat, tapestry, cloth, and wall tiling. A total of 101 defect-free lattices are acquired and 27 defective lattices are used for defect detection. The detection success rate for the second category is 92.59%. An overall detection success rate of 93.86% is achieved for the motif-based method. No other (generalized) approach was able to handle such a large number of wallpaper groups of 2-D patterned textures, and hence this result outperforms all other previously published approaches. This result contributes to the quality assurance of production of textile, wallpaper, ceramics, ornament, and tile. © 2008 IEEE.link_to_subscribed_fulltex