Діагностика морфометричних характеристик порошків надтвердих матеріалів засобами цифрової обробки зображень

Розглянуто питання діагностики розмірних, геометричних та морфологічних характеристик порошків надтвердих матеріалів засобами цифрової обробки зображень. Зроблено огляд сучасних методичних і технічних засобів діагностування. Проведено аналіз тенденцій їх розвитку, зазначено задачі, на розв'язання яких слід спрямувати подальші дослідження.The question of diagnostics of super hard powders dimensioning, geometry and morphological features by digital image processing technique is considered. Analysis of modern methodology and technical tools for diagnostics was done. Survey of the progress trends of these methods was done, tasks for the future investigation were defined

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

Radar remote sensing of a semi-arid environment: a case study in central tunisia.

This work examines the potential of spaceborne microwave remote sensing for the discrimination and analysis of morphological and surface cover-features in semi-arid Tunisia. The study area in central Tunisia comprises a region of overlap between two satellite radar passes: namely Seasat and SIR-A. This allows the influence of two different radar depression angles, look directions and resolutions upon image appearance to be determined. Botn these systems operated at a wavelength of 23cm and hence the characteristic responses of semi-arid surfaces at this wavelength are assessed. This is achieved through visual and digital image interpretation and discriminant analysis of image data. As SIR-A data is available only in optical format, the image had to be digitised before digital image processing could be undertaken. Several radiometric and geometric pre-processing procedures have to be accomplished. Despite the time-lag and calibration difficulties involved, the dominant ground controls on radar backscatter are identified through statistical analysis of information collected in the field. Surface relief, feature geometry and surface roughness are the most important parameters for both systems. The difference in depression angle causes relief information to dominate the Seasat image, while roughness information dominates the SIR-A image. The availability of Landsat Thematic Mapper data for part of the SIR-A swath west of the coastal study area provides a valuable opportunity to assess the complementary nature of data from the visible, infra-red and microwave parts of the electromagnetic spectrum. This is examined through digital and statistical analysis of image data. In the mountain environments, slope angle and aspect are found to be the dominant parameters influencing SIR-A backscatter through their effect on local radar incidence angle. The special quality of radar is its sensitivity to relief and roughness information. This is exploited in a number of applied studies that assess the contributions of radar to environmental management in semi-arid areas. Finally, recommendations are made for future research in the light of the multi-parameter radar systems due to be launched in the 1990s

Geological analysis of Martian rover-derived digital outcrop models using the 3D visualisation tool, Planetary Robotics 3D Viewer – PRo3D

Panoramic camera systems on robots exploring the surface of Mars are used to collect images of terrain and rock outcrops which they encounter along their traverse. Image mosaics from these cameras are essential in mapping the surface geology and selecting locations for analysis by other instruments on the rover’s payload. 2D images do not truly portray the depth of field of features within an image, nor their 3D geometry. This paper describes a new 3D visualization software tool for geological analysis of Martian rover-derived Digital Outcrop Models (DOMs) created using photogrammetric processing of stereo-images using the Planetary Robotics Vision Processing (ProViP) tool developed for 3D vision processing of ExoMars PanCam and Mars 2020 Mastcam-Z data. DOMs are rendered in real time in the Planetary Robotics 3D Viewer PRo3D, allowing scientists to roam outcrops as in a terrestrial field campaign. Digitisation of point, line and polyline features is used for measuring the physical dimensions of geological features, and communicating interpretations. Dip and strike of bedding and fractures is measured by digitising a polyline along the contact or fracture trace, through which a best fit plane is plotted. The attitude of this plane is calculated in the software. Here, we apply these tools to analysis of sedimentary rock outcrops and quantification of the geometry of fracture systems encountered by the science teams of NASA’s Mars Exploration Rover Opportunity and Mars Science Laboratory rover Curiosity. We show the benefits PRo3D allows for visualisation and collection of geological interpretations and analyses from rover-derived stereo-images

Computer-aided light sheet flow visualization using photogrammetry

A computer-aided flow visualization process has been developed to analyze video images acquired from rotating and translating light sheet visualization systems. The computer process integrates a mathematical model for image reconstruction, advanced computer graphics concepts, and digital image processing to provide a quantitative and a visual analysis capability. The image reconstruction model, based on photogrammetry, uses knowledge of the camera and light sheet locations and orientations to project two-dimensional light sheet video images into three-dimensional space. A sophisticated computer visualization package, commonly used to analyze computational fluid dynamics (CFD) results, was chosen to interactively display the reconstructed light sheet images with the numerical surface geometry for the model or aircraft under study. The photogrammetric reconstruction technique and the image processing and computer graphics techniques and equipment are described. Results of the computer-aided process applied to both a wind tunnel translating light sheet experiment and an in-flight rotating light sheet experiment are presented. The capability to compare reconstructed experimental light sheet images with CFD solutions in the same graphics environment is also demonstrated

Stochastic representation of the mechanical properties of irregular masonry structures

A procedure for the stochastic characterization of the elastic moduli of plane irregular masonry structures is presented in this paper. It works in the field of the random composite materials by considering the masonry as a mixture of stones (or bricks) and mortars. Once that the elastic properties of each constituent are known (deterministically or stochastically), the definition of the overall masonry elastic properties requires the knowledge of the random field describing the irregular geometry distribution. This last one is obtained by a software, implemented ad hoc, that, starting from a colour digital photo of the masonry and using the instruments of the digital image processing techniques, gives the random features of this field in both the space and frequency domain. The definition of the stochastic properties of masonry structures may be very useful both for the application of the stochastic homogenization techniques and for the direct stochastic analysis of the structures

Portal Imaging Using a CSI (TL) Scintillator Coupled to a Cooled CCD Camera

The purpose of this research was to design a high performance digital portal imaging system, using a transparent x-ray scintillator coupled to a cooled CCD camera. Theoretical analysis using Monte Carlo simulation was performed to calculate the QDE, SNR and DQE of the system. A prototype electronic portal imaging device (EPID) was built, using a 12.7 mm thick, 20.32 cm diameter, CsI (Tl) scintillator, coupled to an Astromed ® liquid nitrogen cooled CCD TV camera. The system geometry of the prototype EPID was optimized to achieve high spatial resolution. Experimental evaluation of the prototype EPID was performed, by determining its spatial resolution, contrast resolution, depth of focus and light scatter. Images of phantoms, animals and human subjects were acquired using the prototype EPID and were compared with those obtained using conventional and high contrast portal film and a commercial EPID. An image processing protocol was developed. The protocol was comprised of preprocessing, noise removal and image enhancement algorithms. An adaptive median filter algorithm for the removal of impulse noise was developed, analyzed and incorporated into the image processing protocol. Results from the theoretical analysis and experimental evaluation have indicated that the performance of the CsI (Tl) - CCD system is comparable or superior to that of current commercial and experimental portal imaging technologies, such as high contrast portal film, commercial TV camera based EPIDs, and amorphous silicon based flat panel EPIDs

Enhancment of dense urban digital surface models from VHR optical satellite stereo data by pre-segmentation and object detection

The generation of digital surface models (DSM) of urban areas from very high resolution (VHR) stereo satellite imagery requires advanced methods. In the classical approach of DSM generation from stereo satellite imagery, interest points are extracted and correlated between the stereo mates using an area based matching followed by a least-squares sub-pixel refinement step. After a region growing the 3D point list is triangulated to the resulting DSM. In urban areas this approach fails due to the size of the correlation window, which smoothes out the usual steep edges of buildings. Also missing correlations as for partly – in one or both of the images – occluded areas will simply be interpolated in the triangulation step. So an urban DSM generated with the classical approach results in a very smooth DSM with missing steep walls, narrow streets and courtyards. To overcome these problems algorithms from computer vision are introduced and adopted to satellite imagery. These algorithms do not work using local optimisation like the area-based matching but try to optimize a (semi-)global cost function. Analysis shows that dynamic programming approaches based on epipolar images like dynamic line warping or semiglobal matching yield the best results according to accuracy and processing time. These algorithms can also detect occlusions – areas not visible in one or both of the stereo images. Beside these also the time and memory consuming step of handling and triangulating large point lists can be omitted due to the direct operation on epipolar images and direct generation of a so called disparity image fitting exactly on the first of the stereo images. This disparity image – representing already a sort of a dense DSM – contains the distances measured in pixels in the epipolar direction (or a no-data value for a detected occlusion) for each pixel in the image. Despite the global optimization of the cost function many outliers, mismatches and erroneously detected occlusions remain, especially if only one stereo pair is available. To enhance these dense DSM – the disparity image – a pre-segmentation approach is presented in this paper. Since the disparity image is fitting exactly on the first of the two stereo partners (beforehand transformed to epipolar geometry) a direct correlation between image pixels and derived heights (the disparities) exist. This feature of the disparity image is exploited to integrate additional knowledge from the image into the DSM. This is done by segmenting the stereo image, transferring the segmentation information to the DSM and performing a statistical analysis on each of the created DSM segments. Based on this analysis and spectral information a coarse object detection and classification can be performed and in turn the DSM can be enhanced. After the description of the proposed method some results are shown and discussed