Carpet manufacturers certify their products for end-use applications by evaluating the wear behavior of their carpets in mechanical experiments. Currently, this process is performed by visual inspection, suffering from subjective gathers that limit reliability. To automate this process, we propose the use of image processing techniques, specifically of local binary pattern (LBP) statistics. Such statistics are tolerant against illumination changes, can be easily implemented, and perform well when combined with a symmetrized adaptation of the Kullback—Leibler divergence. As a main innovation, we extend the existing rotationally invariant LBPs by including ‘mirror’ and ‘complement’ invariants. We show an accurately improved and more reliable estimation of the degree of wear in worn carpets. The evaluation is performed on four digital reference scales, each containing eight pairs of images comparing transitional degrees of wear to the original appearance. Additionally, the texture changes due to distortions of the pile yarn tufts are enhanced by choosing a suitable scale factor per reference. We validate the findings using six physical reference scales, each containing four pairs of images. In both references, linear correlations of over 0.89 are demonstrated between the degrees of wear and extracted features from the images. These findings justify the use of the proposed LBP extensions in a first approach towards an automated low-cost inspection system for carpet wear at low computation cost.
In this paper we present a novel 3D scanner to capture the texture characteristics of worn carpets into images of the depth. We first compare our proposed scanner to a Metris scanner previously attempted for this application. Then, we scan the surface of samples from the standard EN1471 using our proposed scanner. We found that our proposed scanner offers additional benefits because it has been specifically designed for carpets, performing faster, cheaper, better and also a lot more suitable for darker carpets. The results of this approach give optimistic expectations in the automation of the label assessment dealing with multiple types of carpets.
SummaryA novel method for joint restoration and estimation of the degradation of confocal microscope images is presented. The observed images are degraded due to two sources: blurring due to the band-limited nature of the optical system [modelled by the point spread function (PSF)], and Poisson noise contaminates the observations due to the discrete nature of the photon detection process. The proposed method iterates noise reduction, blur estimation and deblurring, and applies these steps in two phases, i.e. a training phase and a restoration phase. In the first phase, these three steps are iterated until the blur estimation converges. Noise reduction and blur estimation are performed using steerable pyramids, and the deblurring is performed by the Richardson-Lucy algorithm. The second phase is the actual restoration. From then on, the blur estimation is used as a criterion to measure the image quality. The iterations are stopped when this measure converges, a result that is guaranteed. The integrated method is completely automatic, and no prior information on the image is required. The method has been given the name SPERRIL (Steerable Pyramidbased Estimation and Regularized Richardson-Lucy restoration). Compared with existing techniques by both objective measures and visual observation, in the SPERRIL-restored images noise is better suppressed.
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