We present a new framework for an interactive image delineation technique, which we term as interactive texture-snapping system (IT-SNAPS). One of the unique features of IT-SNAPS stems from the fact that it can effectively aid the user in accurately segmenting images with complex texture, without placing undue burden on the user. This is made possible through the formulation of IT-SNAPS, which enables it to be user-aware, i.e., it unobtrusively elicits information from the user during the segmentation process, and hence, adapts itself on-the-fly to the boundary being segmented. In addition to generating an accurate segmentation, it is shown that the framework of IT-SNAPS allows for extraction of useful information post-segmentation, which can potentially assist in the development of customized automatic segmentation algorithms. The afore mentioned features of IT-SNAPS are demonstrated on a set of texture images, as well as on a real-world biomedical application. Using appropriate segmentation protocols in conjunction with expert-provided ground truth, experiments are designed to quantitatively evaluate and compare the segmentation accuracy and user-friendliness of IT-SNAPS with another popular interactive segmentation technique. Promising results indicate the efficacy of IT-SNAPS and its potential to positively impact a broad spectrum of computer vision applications.
We present a machine vision system for simultaneous and objective evaluation of two important functional attributes of a fabric, namely, soil release and shrinkage. Soil release corresponds to the efficacy of the fabric in releasing stains after laundering and shrinkage essentially quantifies the dimensional changes in the fabric postlaundering. Within the framework of the proposed machine vision scheme, the samples are prepared using a prescribed procedure and subsequently digitized using a commercially available off-the-shelf scanner. Shrinkage measurements in the lengthwise and widthwise directions are obtained by detecting and measuring the distance between two pairs of appropriately placed markers. In addition, these shrinkage markers help in producing estimates of the location of the center of the stain on the fabric image. Using this information, a customized adaptive statistical snake is initialized, which evolves based on region statistics to segment the stain. Once the stain is localized, appropriate measurements can be extracted from the stain and the background image that can help in objectively quantifying stain release. In addition, the statistical snakes algorithm has been parallelized on a graphical processing unit, which allows for rapid evolution of multiple snakes. This, in turn, translates to the fact that multiple stains can be detected and segmented in a computationally efficient fashion. Finally, the aforementioned scheme is validated on a sizeable set of fabric images and the promising nature of the results help in establishing the efficacy of the proposed approach.
Stain release is the degree to which a stained substrate approaches its original unsoiled appearance as a result of care
procedure. Stain release has a significant impact on the pricing of the fabric and, hence, needs to be quantified in an
objective manner. In this paper, an automatic approach for the objective assessment of fabric stain release that utilizes
region-based statistical snakes, is presented. This deformable contour approach employs a pressure energy term in the
parametric snake model in conjunction with statistical information (hence, statistical snakes) extracted from the image to
segment the stain and subsequently assign a stain release grade. This algorithm has been parallelized on a General
Purpose Graphical Processing Unit (GPGPU) for accelerated and simultaneous segmentation of multiple stains on a
fabric. The computational power of the GPGPU is attributed to its hardware and software architecture, which enables
multiple and identical snake kernels to be processed in parallel on several streaming processors. The detection and
segmentation results of this machine vision scheme are illustrated as part of the validation study. These results establish
the efficacy of the proposed approach in producing accurate results in a repeatable manner. In addition, this paper
presents a comparison between the benchmarking results for the algorithm on the CPU and the GPGPU.
We present the design and realization of an imaging system intended for use as a reference method for the accurate measurement of cotton fiber length. The prototype system is composed of an off-the-shelf scanner that generates a grayscale image of multiple individualized fibers, followed by customized image processing algorithms that compute the length of each fiber in the image. Although the system requires some degree of separation between the individual fibers at scan time, it is shown to produce highly accurate length measurements that are invariant to fiber orientation, shape, interfiber intersections, and intrafiber crimps and crossovers. Hence, in its present state, the proposed system serves as an excellent reference method for assessing the efficacy of commercially available length measurement systems.
We present a novel statistical approach to unsupervised detection and localization of a chromatic defect in a uniformly textured background. The test images are probabilistically modeled using Gaussian mixture models, and consequently defect detection is posed as a model-order selection problem. The statistical model is estimated using a modified Expectation-Maximization algorithm that aids in faster convergence of the scheme. A test image is segmented only if a defective region/blob has been declared to be present, and this improves the efficiency of the entire scheme. This work places equal emphasis on defect localization; hence, an elaborate statistical multiscale analysis is performed to accurately localize the defect in the image. The underlying idea behind the multiscale approach is that segmented structures should be stable across a wide range of scales. The efficacy of the proposed approach is successfully demonstrated on a large dataset of stained fabric images. The overall detection rate of the system is found to be 92% with a specificity of 95%. All of these factors make the proposed approach attractive for implementation in online industrial applications.
This paper presents the design and realization of an imaging system intended for use as a reference method for the
accurate measurement of cotton fiber length. The prototype system is composed of an off-the-shelf scanner that
generates a grayscale image of multiple individualized fibers, followed by customized image processing algorithms that
compute the length of each fiber in the image. Although the system requires some degree of separation between the
individual fibers at scan time, it is shown to produce highly accurate length measurements that are invariant to fiber
orientation, shape, inter-fiber intersections, and intra-fiber crimps and crossovers. Hence, in its present state, the
proposed system serves as an excellent reference method for assessing the efficacy of commercially available length
measurement systems.
This paper will describe a novel and automated system based on a computer vision approach, for objective evaluation of
stain release on cotton fabrics. Digitized color images of the stained fabrics are obtained, and the pixel values in the
color and intensity planes of these images are probabilistically modeled as a Gaussian Mixture Model (GMM). Stain
detection is posed as a decision theoretic problem, where the null hypothesis corresponds to absence of a stain. The null
hypothesis and the alternate hypothesis mathematically translate into a first order GMM and a second order GMM
respectively. The parameters of the GMM are estimated using a modified Expectation-Maximization (EM) algorithm.
Minimum Description Length (MDL) is then used as the test statistic to decide the verity of the null hypothesis. The
stain is then segmented by a decision rule based on the probability map generated by the EM algorithm. The proposed
approach was tested on a dataset of 48 fabric images soiled with stains of ketchup, corn oil, mustard, ragu sauce, revlon
makeup and grape juice. The decision theoretic part of the algorithm produced a correct detection rate (true positive) of
93% and a false alarm rate of 5% on these set of images.
This paper presents a novel approach for defect detection using a wavelet-domain Hidden Markov Tree (HMT)1 model and a level set segmentation technique. The background, which is assumed to contain homogeneous texture, is modeled off-line with HMT. Using this model, a region map of the defect image is produced on-line through likelihood calculations, accumulated in a coarse-to-fine manner in the wavelet domain. As expected, the region map is basically separated into two regions: 1) the defects, and 2) the background. A level-set segmentation technique is then applied to this region map to locate the defects. This approach is tested with images of defective fabric, as well as x-ray images of cotton with trash. The proposed method shows promising preliminary results, suggesting that it may be extended to a more general approach of defect detection.
Trash content of raw cotton is a critical quality attribute. Therefore, accurate trash assessment is crucial for evaluating
cotton’s processing and market value. Current technologies, including gravimetric and surface scanning methods, suffer from various limitations. Furthermore, worldwide, the most commonly used method is still human grading. One of the best alternatives to the aforementioned approaches is 2D x-ray imaging since it allows a thorough analysis of contaminants in a very precise and quick manner. The segmentation of trash particles in 2D transmission images is
difficult since the background cotton is not uniform. Furthermore, there is considerable overlap between the gray levels of trash and cotton. We dealt with this problem by characterizing and identifying the background cotton via scale-space filtering, followed by a “background normalization” process that removes the background cotton, while leaving the trash particles intact. Furthermore, we have successfully employed stereo x-ray vision for recovering the depth information of the piled trash in controlled samples. Finally, the proposed technique was tested on 280 cotton radiographs-with
various trash levels-and the results compared favorably to the existing systems of cotton trash evaluation. Given that the approach described here provides the trash mass in real-time, when realized, it will have a wide-spread impact on the cotton industry.
The overall quality of a fabric is dependent on a number of factors. Among these is the fabric’s tendency to wrinkle after home laundering - referred to as smoothness. Wrinkle grading is a subjective process involving human graders who compare fabric samples to replicas, representing various degrees of wrinkling. This process is also operator dependent, expensive, and it lacks the ability to adequately describe the many subtle differences that exist between grades. Therefore, the textile industry needs an automated system that can describe wrinkles on a fabric surface in an objective and repeatable manner. In this paper, we describe a computer vision system developed in a previous work and examine the effectiveness of new features extracted from the wavelet domain independent mixture model and a landform classification technique. Shown to be useful in texture classification, features from the wavelet domain independent mixture model are measured based on the two-population characteristic of the wavelet domain. The second technique uses topographical analysis methods originally developed for geographical landform classification that have been successfully applied to digital elevation models of the Earth’s surface. These new measurements, representing quantitative descriptions of the surface of a fabric in both the frequency and spatial domains, are compared to the existing industry grading standard using a fuzzy classifier. Results show a good correlation with technicians’ grades.
A fabric's tendency to wrinkle is vitally important to the textile industry as it impacts the visual appeal of apparel. Current methods of grading this characteristic, called fabric smoothness, are very subjective and inadequate. As such, a quantitative method for assessing fabric smoothness is of the utmost importance to the textile community. To that end, we propose a laser-based surface-profiling system that utilizes a smart camera to sense the 3-D topography of fabric specimens. The system incorporates methods based on anisotropic diffusion and the facet model for characterizing edge information that ultimately relate to a specimen's degree of wrinkling. We detail the initial steps in a large-scale validation of this system. Using histograms of the extracted features, we compare the output of the system between two studies that total more than 200 fabric specimens. The results show that with the features used so far, this system is at least as good as the current American Association of Textile Chemists and Colorists (AATCC) smoothness grading system.
Technologies currently used for cotton contaminant assessment suffer from some fundamental limitations. These limitations result in the misassessment of cotton quality and may have a serious impact on the evaluation of the economic value of the cotton crop. This paper reports on the recent advances in the use of a 3D x-ray microtomographic system that employs image processing and pattern recognition techniques to accurately detect and classify trash present in cotton. The proposed method offers an attractive alternative to existing trash evaluation technologies, because of its ability to produce 3D representations of the samples, to robustly segment the trash from its background, and to accurately classify the contaminant types. This procedure could have a serious impact on the process control technologies (cotton lint cleaning), and indeed on the economic value of cotton.
A fabric's tendency to wrinkle is vitally important to the textile industry as it impacts the visual appeal of apparels. Current methods of grading this characteristic, called fabric smoothness, are very subjective and inadequate. As such, a quantitative method for assessing fabric smoothness is of the utmost importance to the textile community. To that end, we have proposed a laser-based surface profiling system that utilizes a smart camera to sense the 3-D topography of the fabric specimens. The system incorporates methods based on anisotropic diffusion and the facet model for characterizing edge information that ultimately relate to a specimen's degree of wrinkling. In this paper, we detail the initial steps in a large-scale validation of this system. Using histograms of the extracted features, we compare the output of the system among 78 swatches of various color, type, and texture. The results show consistency among repeated scans of the same swatch as well as among different swatches taken from the same fabric sample. Also, since swatches taken from the same piece of fabric typically wrinkle similarly, this adds to the feasibility of the system. In other words, it adequately identifies and measures appropriate features of the wrinkles found on a sample.
A vision system for the automatic quantification of fabric geometric distortion has been implemented and tested. The intended utility of this system is to replace the manual measurement of fabric shrinkage or growth as governed by the AATCC (American Association of Textile Chemists and Colorists) Test Method 135. In the near future, other capabilities, such as automatic quantification of fabric smoothness, will also be incorporated. The system uses commercial, off-the-shelf hardware components, together with a customized image processing algorithm to capture digital images of pre-marked fabric swatches and to accurately measure the distance between the benchmarks before and after laundering. The primary focus of this paper is a description of the algorithm that detects these benchmarks. This robust algorithm detects the marks without regard to: (1) changes in the texture or the color of the swatches, (2) inter-fabric changes in the benchmark colors, (3) changes in the fabric contrast due to scanning or laundering, (4) presence of noise, or (5) slight rotations of the swatches during scanning. The presented system has been under routine testing at the International Textile Center of Texas Tech University, as well as the laboratories of Cotton Inc., with the computed dimensional changes and the manual measurements possessing a nearly perfect linear correlation.
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