Quantitative characterization of multi-phase materials by digital image processing

An automatic image processing and analysis technique has been developed for quantitative characterization of multi-phase materials. For the development of this technique is used the Khoros system that offers the basic morphological tools and a flexible, visual programming language. These techniques are implemented in a highly user oriented image processing environment that allows the user to adapt each step of the processing to his special requirements.To illustrate the implementation and performance of this technique, images of two different materials are processed for microstructure characterization. The result is presented through the determination of volume fraction of the different phases or precipitates.

Automatic segmentation and classification of human intestinal parasites from microscopy images

Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. © 2012 IEEE.

Extração automatica de segmentos lineares de rodovias em imagens de resoluções variadas

In this paper a methodology for automatic extraction of road segments from images with different resolutions (low, middle and high resolution) is presented. It is based on a generalized concept of lines in digital images, by which lines can be described by the centerlines of two parallel edges. In the specific case of low resolution images, where roads are manifested as entities of 1 or 2 pixels wide, the proposed methodology combines an automatic image enhancement operation with the following strategies: automatic selection of the hysteresis thresholds and the Gaussian scale factor; line length thresholding; and polygonization. In medium and high resolution images roads manifest as narrow and elongated ribbons and, consequently, the extraction goal becomes the road centerlines. In this case, it is not necessary to apply the previous enhancement step used to enhance roads in low resolution images. The results obtained in the experimental evaluation satisfied all criteria established for the efficient extraction of road segments from different resolution images, providing satisfactory results in a completely automatic way.

Automated diagnosis of human intestinal parasites using optical microscopy images

Intestinal parasitosis constitutes a serious health problem in most tropical countries. The diagnosis of enteroparasites in laboratory routine relies on the examination of stool samples using optical microscopy and the error rates usually range from moderate to high. Approaches based on automatic image analysis have been proposed, but the methods are usually specific for some species, some of them are computationally expensive, and image acquisition and focus are manual. We present a solution to automate the diagnosis of the 15 most common species of enteroparasites in Brazil, using a sensitive parasitological technique, a motorized microscope with digital camera for automatic image acquisition and focus, and fast image analysis methods. The results indicate that our solution is effective and suitable for laboratory routine, in which the exam must be concluded in a few minutes. © 2013 IEEE.

Detecção automática de rastros de Dust Devils na superfície de Marte

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

HTS and HTSn: New shape descriptors based on Hough transform statistics

With the widespread proliferation of computers, many human activities entail the use of automatic image analysis. The basic features used for image analysis include color, texture, and shape. In this paper, we propose a new shape description method, called Hough Transform Statistics (HTS), which uses statistics from the Hough space to characterize the shape of objects or regions in digital images. A modified version of this method, called Hough Transform Statistics neighborhood (HTSn), is also presented. Experiments carried out on three popular public image databases showed that the HTS and HTSn descriptors are robust, since they presented precision-recall results much better than several other well-known shape description methods. When compared to Beam Angle Statistics (BAS) method, a shape description method that inspired their development, both the HTS and the HTSn methods presented inferior results regarding the precision-recall criterion, but superior results in the processing time and multiscale separability criteria. The linear complexity of the HTS and the HTSn algorithms, in contrast to BAS, make them more appropriate for shape analysis in high-resolution image retrieval tasks when very large databases are used, which are very common nowadays. (C) 2014 Elsevier Inc. All rights reserved.

Comparing de novo and reference-based transcriptome assembly strategies by applying them to the blood-sucking bug Rhodnius prolixus

High Throughput Sequencing capabilities have made the process of assembling a transcriptome easier, whether or not there is a reference genome. But the quality of a transcriptome assembly must be good enough to capture the most comprehensive catalog of transcripts and their variations, and to carry out further experiments on transcriptomics. There is currently no consensus on which of the many sequencing technologies and assembly tools are the most effective. Many non-model organisms lack a reference genome to guide the transcriptome assembly. One question, therefore, is whether or not a reference-based genome assembly gives better results than de novo assembly. The blood-sucking insect Rhodnius prolixus-a vector for Chagas disease-has a reference genome. It is therefore a good model on which to compare reference-based and de novo transcriptome assemblies. In this study, we compared de novo and reference-based genome assembly strategies using three datasets (454, Illumina, 454 combined with Illumina) and various assembly software. We developed criteria to compare the resulting assemblies: the size distribution and number of transcripts, the proportion of potentially chimeric transcripts, how complete the assembly was (completeness evaluated both through CEGMA software and R. prolixus proteome fraction retrieved). Moreover, we looked for the presence of two chemosensory gene families (Odorant-Binding Proteins and Chemosensory Proteins) to validate the assembly quality. The reference-based assemblies after genome annotation were clearly better than those generated using de novo strategies alone. Reference-based strategies revealed new transcripts, including new isoforms unpredicted by automatic genome annotation. However, a combination of both de novo and reference-based strategies gave the best result, and allowed us to assemble fragmented transcripts.

Automatic classification of enzyme family in protein annotation

Most of the tasks in genome annotation can be at least partially automated. Since this annotation is time-consuming, facilitating some parts of the process - thus freeing the specialist to carry out more valuable tasks - has been the motivation of many tools and annotation environments. In particular, annotation of protein function can benefit from knowledge about enzymatic processes. The use of sequence homology alone is not a good approach to derive this knowledge when there are only a few homologues of the sequence to be annotated. The alternative is to use motifs. This paper uses a symbolic machine learning approach to derive rules for the classification of enzymes according to the Enzyme Commission (EC). Our results show that, for the top class, the average global classification error is 3.13%. Our technique also produces a set of rules relating structural to functional information, which is important to understand the protein tridimensional structure and determine its biological function. © 2009 Springer Berlin Heidelberg.

Image processing algorithm for the identification of Martian dust devil tracks in MOC and HiRISE images

This paper presents a method for automatic identification of dust devils tracks in MOC NA and HiRISE images of Mars. The method is based on Mathematical Morphology and is able to successfully process those images despite their difference in spatial resolution or size of the scene. A dataset of 200 images from the surface of Mars representative of the diversity of those track features was considered for developing, testing and evaluating our method, confronting the outputs with reference images made manually. Analysis showed a mean accuracy of about 92%. We also give some examples on how to use the results to get information about dust devils, namelly mean width, main direction of movement and coverage per scene. (c) 2012 Elsevier Ltd. All rights reserved.

CIE L*a*b*: comparison of digital images obtained photographically by manual and automatic modes

The aim of this study was to analyze the color alterations performed by the CIE L*a*b* system in the digital imaging of shade guide tabs, which were obtained photographically according to the automatic and manual modes. This study also sought to examine the observers' agreement in quantifying the coordinates. Four Vita Lumin Vaccum shade guide tabs were used: A3.5, B1, B3 and C4. An EOS Canon digital camera was used to record the digital images of the shade tabs, and the images were processed using Adobe Photoshop software. A total of 80 observations (five replicates of each shade according to two observers in two modes, specifically, automatic and manual) were obtained, leading to color values of L*, a* and b*. The color difference (AE) between the modes was calculated and classified as either clinically acceptable or unacceptable. The results indicated that there was agreement between the two observers in obtaining the L*, a* and b* values related to all guides. However, the B1, B3, and C4 shade tabs had AE values classified as clinically acceptable (Delta E = 0.44, Delta E = 2.04 and Delta E = 2.69, respectively). The A3.5 shade tab had a AE value classified as clinically unacceptable (Delta E = 4.17), as it presented higher values for luminosity in the automatic mode (L* = 54.0) than in the manual mode (L* = 50.6). It was concluded that the B1, B3 and C4 shade tabs can be used at any of the modes in digital camera (manual or automatic), which was a different finding from that observed for the A3.5 shade tab.

Edge detection and noise removal by use of a partial differential equation with automatic selection of parameters

This work deals with noise removal by the use of an edge preserving method whose parameters are automatically estimated, for any application, by simply providing information about the standard deviation noise level we wish to eliminate. The desired noiseless image u(x), in a Partial Differential Equation based model, can be viewed as the solution of an evolutionary differential equation u t(x) = F(u xx, u x, u, x, t) which means that the true solution will be reached when t ® ¥. In practical applications we should stop the time ''t'' at some moment during this evolutionary process. This work presents a sufficient condition, related to time t and to the standard deviation s of the noise we desire to remove, which gives a constant T such that u(x, T) is a good approximation of u(x). The approach here focused on edge preservation during the noise elimination process as its main characteristic. The balance between edge points and interior points is carried out by a function g which depends on the initial noisy image u(x, t0), the standard deviation of the noise we want to eliminate and a constant k. The k parameter estimation is also presented in this work therefore making, the proposed model automatic. The model's feasibility and the choice of the optimal time scale is evident through out the various experimental results.

Automatic estimation of crowd density using texture

This paper considers the role of automatic estimation of crowd density and its importance for the automatic monitoring of areas where crowds are expected to be present. A new technique is proposed which is able to estimate densities ranging from very low to very high concentration of people, which is a difficult problem because in a crowd only parts of people's body appear. The new technique is based on the differences of texture patterns of the images of crowds. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The image pixels are classified in different texture classes and statistics of such classes are used to estimate the number of people. The texture classification and the estimation of people density are carried out by means of self organising neural networks. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented. (C) 1998 Elsevier B.V. Ltd. All rights reserved.

Automatic absolute orientation of scanned aerial photographs

Image orientation is a basic problem in Digital Photogrammetry. While interior and relative orientations were succesfully automated, the same can not be said about absolute orientation. This process can be automated by using an approach based on relational matching and a heuristic that uses the analytical relation between straight features in the object space and its homologous in the image space. A build-in self-diagnosis is also used in this method, that is based on the implementation of data snooping statistic test in the process of spatial resection, using the Iterated Extended Kalman Filtering (IEKF). The aim of this paper is to present the basic principles of the proposed approach and results based on real data.

Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. This paper describes a new technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey-level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.

Three-dimensional reconstruction of fracture surfaces: Area matching algorithms for automatic parallax measurements

This paper describes two solutions for systematic measurement of surface elevation that can be used for both profile and surface reconstructions for quantitative fractography case studies. The first one is developed under Khoros graphical interface environment. It consists of an adaption of the almost classical area matching algorithm, that is based on cross-correlation operations, to the well-known method of parallax measurements from stereo pairs. A normalization function was created to avoid false cross-correlation peaks, driving to the true window best matching solution at each region analyzed on both stereo projections. Some limitations to the use of scanning electron microscopy and the types of surface patterns are also discussed. The second algorithm is based on a spatial correlation function. This solution is implemented under the NIH Image macro programming, combining a good representation for low contrast regions and many improvements on overall user interface and performance. Its advantages and limitations are also presented.