This paper describes a .sen~i-aut~nnatic segmentationIrIetliod f.r application to reniral and luntbar X-ray inr- ages. TIE nietliod consists fjf a three stage, coarse 10 fine seginentafiou Iiwcess zitilizing one stage qf the Generalized Hough Transfima and W O stuges of Active Appearance Models. Cusroinizatiuirs to these algoritl~n~s are introduced, and segnienration results for 273 cervical x-ruy inlures and 262 lumbar X-ray images are pre-sented.

Soil release is an important attribute of fabrics that impacts their pricing on the marketplace. Currently, stain release is evaluated manually and hence its assessment tends to be subjective. This paper introduces a novel approach based on image analysis for objective evaluation of stain release in fabrics. This system has the capability to accurately detect, localize and grade stains with minimal human intervention. The grades assigned by the system are based on a digitized version of the AATCC soil release replica. The stain detection performance of the system was validated on a large dataset of 360 stain images. The system produced excellent results with a sensitivity (true positive rate) of around 93 percent and specificity (1 — false positive rate) of around 95 percent. Further, a correlation study and analysis of variance were performed using the system assigned grades and the technician assigned grades, and these initial results are very promising.

This report addresses the issues involved in developing a robust segmentation technique capable of finding the location and orientation of the cervical vertebrae in x-ray images. This technique should be invariant to rotation, scale, noise, occlusions and shape variability. A customized approach, based on the Generalized Hough transform (GHT), that captures shape variability and exploits shape information embedded in the accumulator structure to overcome noise and occlusions is proposed. This approach effectively finds estimates of the location and orientation of the cervical vertebrae boundaries in digitized x-ray images.

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.