Automatic system for quality-based classification of marble textures

In this paper, we present an automatic system and algorithms for the classification of marble slabs into different groups in real time in production line, according to slabs quality. The application of the system is aimed at the marble industry, in order to automate and improve the manual classification process of marble slabs carried out at present. The system consists of a mechatronic prototype, which houses all the required physical components for the acquisition of marble slabs images in suitable light conditions, and computational algorithms, which are used to analyze the color texture of the marble surfaces and classify them into their corresponding group. In order to evaluate the color representation influence on the image analysis, four color spaces have been tested: RGB, XYZ, YIQ, and K-L. After the texture analysis performed with the sum and difference histograms algorithm, a feature extraction process has been implemented with principal component analysis. Finally, a multilayer perceptron neural network trained with the backpropagation algorithm with adaptive learning rate is used to classify the marble slabs in three categories, according to their quality. The results (successful classification rate of 98.9%) show very high performance compared with the traditional (manual) system

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

Marble Slabs Classification System Based on Image Processing (Ark Marble Mine in Birjand)

Marble is one of the semi-precious stones that has been used in decorating building façade and making decorative things. This stone is present in the nature in the form of rock or layered stone. Examining the kind of stone, extent of impurity and different streaks in white marble is a widely confronted subject by those who are involved in this industry. Obtaining the extent of impurity of white marble using methods of detecting and analyzing material is expensive and time-consuming. In this research carried out on while marbles of Arc Mine in Birjand, it has been attempted to present very fast method using Image Processing Techniques so that while preserving identity and appearance of stone and without any damage to it, we compute the impurity level and different streaks on white marble surface. The proposed method includes two stages; in the first stage applying image processing functions, it is attempted to segment the present impurities and streaks on marble surface from the stone background and in the second stage, the area of these impurities and streaks is computed. Results obtained in this paper (97.8%) in comparison with other researches and experimental methods indicate acceptability of this algorithm

Describing Textures in the Wild

Patterns and textures are defining characteristics of many natural objects: a shirt can be striped, the wings of a butterfly can be veined, and the skin of an animal can be scaly. Aiming at supporting this analytical dimension in image understanding, we address the challenging problem of describing textures with semantic attributes. We identify a rich vocabulary of forty-seven texture terms and use them to describe a large dataset of patterns collected in the wild.The resulting Describable Textures Dataset (DTD) is the basis to seek for the best texture representation for recognizing describable texture attributes in images. We port from object recognition to texture recognition the Improved Fisher Vector (IFV) and show that, surprisingly, it outperforms specialized texture descriptors not only on our problem, but also in established material recognition datasets. We also show that the describable attributes are excellent texture descriptors, transferring between datasets and tasks; in particular, combined with IFV, they significantly outperform the state-of-the-art by more than 8 percent on both FMD and KTHTIPS-2b benchmarks. We also demonstrate that they produce intuitive descriptions of materials and Internet images.Comment: 13 pages; 12 figures Fixed misplaced affiliatio

Image-based metric heritage modeling in the near-infrared spectrum

Digital photogrammetry and spectral imaging are widely used in heritage sciences towards the comprehensive recording, understanding, and protection of historical artifacts and artworks. The availability of consumer-grade modified cameras for spectral acquisition, as an alternative to expensive multispectral sensors and multi-sensor apparatuses, along with semi-automatic software implementations of Structure-from-Motion (SfM) and Multiple-View-Stereo (MVS) algorithms, has made more feasible than ever the combination of those techniques. In the research presented here, the authors assess image-based modeling from near-infrared (NIR) imagery acquired with modified consumergrade cameras, with applications on tangible heritage. Three-dimensional (3D) meshes, textured with the non-visible data, are produced and evaluated. Specifically, metric evaluations are conducted through extensive comparisons with models produced with image-based modeling from visible (VIS) imagery and with structured light scanning, to check the accuracy of results. Furthermore, the authors observe and discuss, how the implemented NIR modeling approach, affects the surface of the reconstructed models, and may counteract specific problems which arise from lighting conditions during VIS acquisition. The radiometric properties of the produced results are evaluated, in comparison to the respective results in the visible spectrum, on the capacity to enhance observation towards the characterization of the surface and under-surface state of preservation, and consequently, to support conservation interventions

Classification of Rock Images using Textural Analysis

The classification of natural images is an useful task in current computer vision, pattern recognition applications etc. Rock images are a typical example of natural images, therefore their analysis is of major importance in the rock industry and in bedrock investigations. Rock image classification is based on specific textural descriptors which are extracted from the images. Using these descriptors, images are divided into various types. In the case of natural images, the feature distributions are often non-homogeneous and the image classes are also overlapping in the feature space. This can be problematic, if all the descriptors are combined into a single feature vector in the classification of an image. A method is presented for combining different visual descriptors in rock image classification. In this paper, k-nearest neighbor classification will be carried out for pair of descriptor separately. After that, the final decision is made by combining the results of each classification. The total numbers of the neighbors representing each class are used as votes in the final classification. The classification method will be tested using three types of rock. DOI: 10.17762/ijritcc2321-8169.15039

Modeling of evolving textures using granulometries

This chapter describes a statistical approach to classification of dynamic texture images, called parallel evolution functions (PEFs). Traditional classification methods predict texture class membership using comparisons with a finite set of predefined texture classes and identify the closest class. However, where texture images arise from a dynamic texture evolving over time, estimation of a time state in a continuous evolutionary process is required instead. The PEF approach does this using regression modeling techniques to predict time state. It is a flexible approach which may be based on any suitable image features. Many textures are well suited to a morphological analysis and the PEF approach uses image texture features derived from a granulometric analysis of the image. The method is illustrated using both simulated images of Boolean processes and real images of corrosion. The PEF approach has particular advantages for training sets containing limited numbers of observations, which is the case in many real world industrial inspection scenarios and for which other methods can fail or perform badly. [41] G.W. Horgan, Mathematical morphology for analysing soil structure from images, European Journal of Soil Science, vol. 49, pp. 161–173, 1998. [42] G.W. Horgan, C.A. Reid and C.A. Glasbey, Biological image processing and enhancement, Image Processing and Analysis, A Practical Approach, R. Baldock and J. Graham, eds., Oxford University Press, Oxford, UK, pp. 37–67, 2000. [43] B.B. Hubbard, The World According to Wavelets: The Story of a Mathematical Technique in the Making, A.K. Peters Ltd., Wellesley, MA, 1995. [44] H. Iversen and T. Lonnestad. An evaluation of stochastic models for analysis and synthesis of gray-scale texture, Pattern Recognition Letters, vol. 15, pp. 575–585, 1994. [45] A.K. Jain and F. Farrokhnia, Unsupervised texture segmentation using Gabor filters, Pattern Recognition, vol. 24(12), pp. 1167–1186, 1991. [46] T. Jossang and F. Feder, The fractal characterization of rough surfaces, Physica Scripta, vol. T44, pp. 9–14, 1992. [47] A.K. Katsaggelos and T. Chun-Jen, Iterative image restoration, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 208–209, 2000. [48] M. K¨oppen, C.H. Nowack and G. R¨osel, Pareto-morphology for color image processing, Proceedings of SCIA99, 11th Scandinavian Conference on Image Analysis 1, Kangerlussuaq, Greenland, pp. 195–202, 1999. [49] S. Krishnamachari and R. Chellappa, Multiresolution Gauss-Markov random field models for texture segmentation, IEEE Transactions on Image Processing, vol. 6(2), pp. 251–267, 1997. [50] T. Kurita and N. Otsu, Texture classification by higher order local autocorrelation features, Proceedings of ACCV93, Asian Conference on Computer Vision, Osaka, pp. 175–178, 1993. [51] S.T. Kyvelidis, L. Lykouropoulos and N. Kouloumbi, Digital system for detecting, classifying, and fast retrieving corrosion generated defects, Journal of Coatings Technology, vol. 73(915), pp. 67–73, 2001. [52] Y. Liu, T. Zhao and J. Zhang, Learning multispectral texture features for cervical cancer detection, Proceedings of 2002 IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 169–172, 2002. [53] G. McGunnigle and M.J. Chantler, Modeling deposition of surface texture, Electronics Letters, vol. 37(12), pp. 749–750, 2001. [54] J. McKenzie, S. Marshall, A.J. Gray and E.R. Dougherty, Morphological texture analysis using the texture evolution function, International Journal of Pattern Recognition and Artificial Intelligence, vol. 17(2), pp. 167–185, 2003. [55] J. McKenzie, Classification of dynamically evolving textures using evolution functions, Ph.D. Thesis, University of Strathclyde, UK, 2004. [56] S.G. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Transactions of the American Mathematical Society, vol. 315, pp. 69–87, 1989. [57] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, pp. 674–693, 1989. [58] B.S. Manjunath and W.Y. Ma, Texture features for browsing and retrieval of image data, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, pp. 837–842, 1996. [59] B.S. Manjunath, G.M. Haley and W.Y. Ma, Multiband techniques for texture classification and segmentation, Handbook of Image and Video Processing, A. Bovik, ed., Academic Press, London, pp. 367–381, 2000. [60] G. Matheron, Random Sets and Integral Geometry, Wiley Series in Probability and Mathematical Statistics, John Wiley and Sons, New York, 1975

Mermer türlerinin makine öğrenmesi teknikleri kullanılarak sınıflandırılması

Doğal taşlar, insanların barınmadan silaha kadar vazgeçilmez unsurlarından bir tanesidir. Bu taş türleri içerisinde mermerler ve mermer türevli ürünler banyodan mutfağa, bahçe tasarımından küçük dekoratif ev süslerine kadar insanların sürekli tercih ettiği objelerdendir. Mermerler çıkarıldıkları bölgelere göre isimlendirilirken bu alanda uzman olarak nitelendirilen kişiler tarafından gözleme dayalı olarak türleri ve kaliteleri sınıflandırılmaktadır. Uzman kişilerin gözleme dayalı yaptığı bu sınıflandırma ekonomik anlamda risk taşımakta, iş yükünü arttırmakta ve hata oranı yüksek olabilen zorlu bir süreçtir. Bu süreçlerin hızlı, kolay ve doğruluk oranı yüksek bir dijital dönüşüme ihtiyacı bulunmaktadır. Bu çalışmada mermerlerin tür sınıflandırmasında derin öğrenme kullanılarak özellik çıkarımı yapılmıştır. Çıkarılan özellikler makine öğrenme teknikleri kullanılarak sınıflandırma uygulaması gerçekleştirilmiştir. 28 ayrı türe ait 3703 mermer ve mermer türevli doğal taş imgesinden oluşan veri seti ile yapılan uygulamanın test sonucunda DenseNet derin öğrenme modeli ve K-En Yakın Komşu metodu ile %99,7’lik sınıflandırma başarımı elde edilmiştir.Natural stones are one of the indispensable elements of people from shelter to weapons. Among these stone types, marbles and marble-derived products are among the objects that people always prefer, from bathroom to kitchen, from garden design to small decorative home decorations. While the marbles are named according to the regions where they are extracted, their types and qualities are classified based on observation by people who are qualified as experts in this field. This classification, which is made by experts based on observation, carries risks in economic terms, increases the workload and is a difficult process with a high error rate. These processes need a fast, easy and highly accurate digital transformation. In this study, feature extraction was done by using deep learning in the species classification of marbles. The extracted features were classified using machine learning techniques. As a result of the application made with the data set consisting of 3703 marble and marble-derived natural stone images belonging to 28 different species, a classification success of 99.7% was obtained with the DenseNet deep learning model and the K-Nearest Neighbor method