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 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 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.