Automatic detection of cell nuclei in fluorescence micrographs

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Blur estimation for document images

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Analysing wear in carpets by detecting varying local binary patterns

Currently, carpet companies assess the quality of their products based on their appearance retention capabilities. For this, carpet samples with different degrees of wear after a traffic exposure simulation process are rated with wear labels by human experts. Experts compare changes in appearance in the worn samples to samples with original appearance. This process is subjective and humans can make mistakes up to 10% in rating. In search of an objective assessment, research using texture analysis has been conducted to automate the process. Particularly, Local Binary Pattern (LBP) technique combined with a Symmetric adaptation of the Kullback-Leibler divergence (SKL) are successful for extracting texture features related to the wear labels either from intensity and range images. We present in this paper a novel extension of the LBP techniques that improves the representation of the distinct wear labels. The technique consists in detecting those patters that monotonically change with the wear labels while grouping the others. Computing the SKL from these patters considerably increases the discrimination between the consecutive groups even for carpet types where other LBP variations fail. We present results for carpet types representing 72% of the existing references for the EN1471:1996 European standard

Texture wear analysis in textile floor coverings by using depth information

Considerable industrial and academic interest is addressed to automate the quality inspection of textile floor coverings, mostly using intensity images. Recently, the use of depth information has been explored to better capture the 3D structure of the surface. In this paper, we present a comparison of features extracted from three texture analysis techniques. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show that the use of Local Binary Patterns techniques result in a better ranking of the wear labels as well as in a higher amount of discrimination between features related to consecutive degrees of wear

Feature extraction of the wear label of carpets by using a novel 3D scanner

In the textile industry, the quality of carpets is still determined through visual assessment by human experts. Human assessment is somewhat subjective, so there is a need for a more objective assessment which yields to automated systems. However, existing computer models are at this moment not yet capable of matching the human expertise. Most attempts at automated assessment have focused on image analysis of two dimensional images of worn carpet. These do not adequately capture the three dimensional structure of the carpet that is also evaluated by the experts and the image processing is very dependent on the lighting conditions. One previous attempt however used a laser scanner to obtain three dimensional images of the carpet and process them for carpet assessment. This paper describes the development of a new scanner to acquire wear label characteristics in three dimensions based on a structured light pattern. Now an appropriate technique based on the local binary patterns (LBP) and the Kullback-Leibler divergence has been developed. We show that the new laser scanning system is less dependent on the lighting conditions and color of the carpet and obtains data points on a structured grid instead of sparse points. The new system is also more than five times cheaper, scans more than seven times faster and is specifically designed for scanning carpets instead of 3D objects. Previous attempts to classify the carpet wear were based on several extracted features. Only one of them - the height difference between worn and unworn part - showed a good correlation of 0.70 with the carpet wear label. However, experiments demonstrate that our approach - using the LBP technique - gives rise to promising results, with correlation factors from 0.89 to 0.99 between the Kullback-Leibler divergence and quality labels. This new laser scanner system is a significant step forward in the automated assessment of carpet wear using 3D images

Optimizing feature extraction in image analysis using experimented designs, a case study evaluating texture algorithms for describing appearance retention in carpets

When performing image analysis, one of the most critical steps is the selection of appropriate techniques. A huge amount of features can be extracted from several techniques and the selection is commonly performed based on expert knowledge. In this paper we present the theory of experimental designs as a tool for an objective selection of techniques in image analysis domain. We present a study case for evaluating appearance retention in textile floor coverings using texture features. The use of experimental design theory permitted to select an optimal set of techniques for describing the texture changes due to degradation