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Many researchers have demonstrated that textural data increase the precision of image classification when they are combined with grey level information. However, textural parameters of order two take too long computation time. The problem is more complex when one must compute higher order textural parameters, which however can considerably improve the precision of a classification. In this work, we propose a new formulation for the calculation of statistical textural parameters. The principle consists in reducing the calculation of a n-summation of type $$ \sum^{L-1}_{i_{0}=0}\sum^{L-1}_{i_{1}=0}\sum^{L-1}_{i_{2}=0} \cdots\sum^{L-1}_{i_{n-1}=0}\psi[i_{0},i_{1},\cdots,i_{(n-1)},P_{i_{0},i_{1},\cdots i_{n-1}}] $$ generally used in the evaluation of textural parameters, to a double summation of type \( \sum\nolimits_{p=0}^{Wx}\sum\nolimits_{q=0}^{Wy}X(p,q)\) where L is the dynamic of grey levels (number of quantification levels) in the image, ( \( P_{i_{0},i_{1},\cdots i_{n-1}}\) ) is the occurrence frequency matrix (co-occurrence matrix in the case of order two parameters) and W x (respectively W y ) is the width (respectively the height) of the image window. This method produces the same results as the classical method, but it’s about L n-1 times faster than the classical method and a gain of L n of memory space is obtained, where n is the order of the textural parameter.

Digital imaging allows one to easily change not only the appearance of the image but also the underlying data used by the computer to form the image. Digital imaging is a very powerful technique for presenting data in an easily understood form, but can also result in misleading images that do not reflect the original data.

The images obtained from confocal microscopes are in a digital format. This greatly simplifies manipulation of the images for merging, colouring, cropping, printing etc. An understanding of what a digital image is will prove most valuable in not only getting the best out of your images, but also for interpreting your own results and for understanding the images obtained by others.

A morphological tool for accurate automatic texture classification is proposed and applied to a large set of plastering mortars. Starting from measurements of morphological data (covariance, speed of erosion and dilation, size distribution, watershed sizing) on grey level images, correspondence analysis is used in a learning procedure to visualize the surfaces in a reduced space, and to select the most pertinent measurements with respect to the classification of textures.

Until now different uses of computers are known in nematology, e.g. Boag (1981) described an electronic system for measuring nematodes and Fortuner (1983) designed a computer program (NEMAID) for identifying Helicotylenchus species, including a database with measurements.

The most common tasks of low level image processing are considered in order to define a set of primitives to be implemented in parallel image processing systems. In particular arithmetic primitives, global features (as grey level statistics), linear transforms and local computations are discussed taking care of the effectiveness of the implementations on array and pyramidal systems. The propagation capability supplied by flat arrays and pyramids is then presented in some extent with the purpose to highlight the performances of this kind of machines in well known low level tasks.

Several algorithms are tested to characterize order and disorder in digitised images of spatial patterns. As source of images, the BelousovZhabotinskii reaction coupled to convective motion in a thin liquid layer is used.

New structural applications of polymer matrix composites require the use of thick sections. The fabrication and use of such thick sections create new needs for flaw detection and characterization which cannot be met adequately by conventional nondestructive evaluation (NDE). These composites are susceptible to manufacturing defects due to thermal gradients and nonuniform resin bleeding during curing.

The progress of digital processing of satellite earth resources data is charted over the past four years. From the general trends, probable patterns for future processing are inferred. These lead to suggestions for a new approach to the provision of earth resources services involving satellite data. Mechanisms are proposed and discussed for implementing this approach, and basic questions that must be answered are set forth.