JSEG-based image segmentation in computer vision for agricultural mobile robot navigation

This project aims to apply image processing techniques in computer vision featuring an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. To carry through this task, computational methods based on the JSEG algorithm were used to provide the classification and the characterization of such problems, together with Artificial Neural Networks (ANN) for pattern recognition. Therefore, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave platforms, along with the application of customized Back-propagation algorithm and statistical methods in a Simulink environment. Having the aforementioned procedures been done, it was practicable to classify and also characterize the HSV space color segments, not to mention allow the recognition of patterns in which reasonably accurate results were obtained.

Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.

Automatic segmentation of digital images applied in cardiac medical images

The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature. © 2010 IEEE.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Sealing ability of retrograde obturation materials containing calcium hydroxide or MTA

Newly available materials for retrograde obturation should have their sealing properties evaluated. The goal of this study was to evaluate the sealing ability of Endo CPM sealer, an MTA-based endodontic cement. Single-rooted extracted human teeth were endodontically treated. After apical sectioning, retrograde cavities were prepared. Teeth were divided into five experimental groups (n=12), in which the following materials were used: Sealer 26 (S26), white Mineral Trioxide Aggregate (MTA), Endo CPM Sealer (CPM1), Endo CPM Sealer in thicker consistency (CPM 2), and zinc oxide and eugenol cement (ZOE), and two control groups (n=3). After retrograde obturation, the teeth were immersed in 0.2% rhodamine B dye for 48 hours in a vacuum chamber Marginal leakage data were subjected to ANOVA and Tukey tests at 5% significance level. S26 presented greater sealing ability (p<0.05) than ZOE, MTA, CPM1, and CPM2, all of which had similar results (p>0.05). We concluded that Sealer 26 has the greatest sealing ability. Endo CPM Sealer, with sealing ability similar to MTA, could be used as a retrograde obturation material.

Automatic segmentation of the secondary austenite-phase island precipitates in a superduplex stainless steel weld metal

Duplex and superduplex stainless steels are class of materials of a high importance for engineering purposes, since they have good mechanical properties combination and also are very resistant to corrosion. It is known as well that the chemical composition of such steels is very important to maintain some desired properties. In the past years, some works have reported that γ 2 precipitation improves the toughness of such steels, and its quantification may reveals some important information about steel quality. Thus, we propose in this work the automatic segmentation of γ 2 precipitation using two pattern recognition techniques: Optimum-Path Forest (OPF) and a Bayesian classifier. To the best of our knowledge, this if the first time that machine learning techniques are applied into this area. The experimental results showed that both techniques achieved similar and good recognition rates. © 2012 Taylor & Francis Group.

Semiautomatic dental recognition using a graph-based segmentation algorithm and teeth shapes features

Dental recognition is very important for forensic human identification, mainly regarding the mass disasters, which have frequently happened due to tsunamis, airplanes crashes, etc. Algorithms for automatic, precise, and robust teeth segmentation from radiograph images are crucial for dental recognition. In this work we propose the use of a graph-based algorithm to extract the teeth contours from panoramic dental radiographs that are used as dental features. In order to assess our proposal, we have carried out experiments using a database of 1126 tooth images, obtained from 40 panoramic dental radiograph images from 20 individuals. The results of the graph-based algorithm was qualitatively assessed by a human expert who reported excellent scores. For dental recognition we propose the use of the teeth shapes as biometric features, by the means of BAS (Bean Angle Statistics) and Shape Context descriptors. The BAS descriptors showed, on the same database, a better performance (EER 14%) than the Shape Context (EER 20%). © 2012 IEEE.

Automatic segmentation of latent fingerprints

Latent fingerprints are routinely found at crime scenes due to the inadvertent contact of the criminals' finger tips with various objects. As such, they have been used as crucial evidence for identifying and convicting criminals by law enforcement agencies. However, compared to plain and rolled prints, latent fingerprints usually have poor quality of ridge impressions with small fingerprint area, and contain large overlap between the foreground area (friction ridge pattern) and structured or random noise in the background. Accordingly, latent fingerprint segmentation is a difficult problem. In this paper, we propose a latent fingerprint segmentation algorithm whose goal is to separate the fingerprint region (region of interest) from background. Our algorithm utilizes both ridge orientation and frequency features. The orientation tensor is used to obtain the symmetric patterns of fingerprint ridge orientation, and local Fourier analysis method is used to estimate the local ridge frequency of the latent fingerprint. Candidate fingerprint (foreground) regions are obtained for each feature type; an intersection of regions from orientation and frequency features localizes the true latent fingerprint regions. To verify the viability of the proposed segmentation algorithm, we evaluated the segmentation results in two aspects: a comparison with the ground truth foreground and matching performance based on segmented region. © 2012 IEEE.

Heterogeneous demand and market segmentation: the Argentine pension funds system

Includes bibliography

Evaluation of the anti-resorptive ability of an experimental acetazolamide paste for the treatment of late replanted teeth: A study in rats

When late replantation is performed, the root surface and root canal should be treated. Notwithstanding failures still occur, because of the high rates of root resorption, evidencing the need to search for substances that may inhibit root resorption. The acetazolamide is a known anti-resorptive agent, and its use as root canal dressing may increase the success rates in the treatment of root resorption. Therefore, this study evaluated the effect of an acetazolamide paste used as root canal dressing in late replanted teeth. The study was conducted on 24 maxillary right incisors of rats, which were avulsed and divided in two groups. In group I, the teeth were kept dry for 30min, had their root surfaces rubbed with a blade, and were treated with 2% sodium fluoride at pH 5.5 for 20min; the root canals were instrumented and filled with acetazolamide paste; and then the teeth were replanted. In group II, the treatment was similar to group I, except for the root canal dressing, with utilization of calcium hydroxide in group II. At 15 and 60days after replantation, the animals were killed and the specimens were processed in a histotechnical laboratory for microscopic and morphometric analysis. The results demonstrated the ability of both intracanal substances to limit root resorption, yet they were unable to completely inhibit the root resorption. Replacement resorption lacunae were present in greater proportion in group II, at 60days. It was concluded that the acetazolamide paste was effective to limit the root resorption, being more effective in limiting the replacement resorption compared with calcium hydroxide. © 2012 John Wiley & Sons A/S.

Sealing ability of MTA used as a root end filling material: Effect of the sonic and ultrasonic condensation

Despite the excellent properties of mineral trioxide aggregate (MTA), the condensation technique may have some influence in its sealing ability. The purpose of this study was to compare the sealing ability of sonic and ultrasonic setting of MTA. Thirty-four extracted human teeth had their canals prepared and filled with Sealapex sealer and gutta-percha using the active lateral condensation technique. The teeth were rendered waterproof and apicoectomy performed at 3 mm from the apex. Root-end cavities (3.0 mm deep and 1.4 mm diameter) were prepared with diamond ultrasonic tips. The root-end cavities were filled with Pro-Root MTA® with ultrasonic vibration, sonic vibration or no vibration. The positive control group did not receive any material while the negative control group was totally rendered waterproof. After material set, the specimens were immersed in Rodhamine B for 24 h, under vacuum in the first 15 min, then washed, dried and split longitudinally for evaluating the infiltration at the dentin/material interface. Data were analyzed using ANOVA and Tukey's tests at 5% significance level. Sonic vibration promoted the lowest infiltration values (p<0.05). It was concluded that sonic vibration could be considered an efficient aid to improve the sealing ability of MTA when used as root-end filling material.

Socio-demographic vulnerability: old and new risks for communities, households and individuals

Includes bibliography

Ablation of the ability to control the right-to-left cardiac shunt does not affect oxygen uptake, specific dynamic action or growth in the rattlesnake Crotalus durissus

The morphologically undivided ventricle of the heart in non-crocodilian reptiles permits the mixing of oxygen-rich blood returning from the lungs and oxygen-poor blood from the systemic circulation. A possible functional significance for this intra-cardiac shunt has been debated for almost a century. Unilateral left vagotomy rendered the single effective pulmonary artery of the South American rattlesnake, Crotalus durissus, unable to adjust the magnitude of blood flow to the lung. The higher constant perfusion of the lung circulation and the incapability of adjusting the right-left shunt in left-denervated snakes persisted over time, providing a unique model for investigation of the long-term consequences of cardiac shunting in a squamate. Oxygen uptake recorded at rest and during spontaneous and forced activity was not affected by removing control of the cardiac shunt. Furthermore, metabolic rate and energetic balance during the post-prandial metabolic increment, plus the food conversion efficiency and growth rate, were all similarly unaffected. These results show that control of cardiac shunting is not associated with a clear functional advantage in adjusting metabolic rate, effectiveness of digestion or growth rates. © 2013. Published by The Company of Biologists Ltd.

Unsupervised segmentation method for cuboidal cell nuclei in histological prostate images based on minimum cross entropy

This paper presents a novel segmentation method for cuboidal cell nuclei in images of prostate tissue stained with hematoxylin and eosin. The proposed method allows segmenting normal, hyperplastic and cancerous prostate images in three steps: pre-processing, segmentation of cuboidal cell nuclei and post-processing. The pre-processing step consists of applying contrast stretching to the red (R) channel to highlight the contrast of cuboidal cell nuclei. The aim of the second step is to apply global thresholding based on minimum cross entropy to generate a binary image with candidate regions for cuboidal cell nuclei. In the post-processing step, false positives are removed using the connected component method. The proposed segmentation method was applied to an image bank with 105 samples and measures of sensitivity, specificity and accuracy were compared with those provided by other segmentation approaches available in the specialized literature. The results are promising and demonstrate that the proposed method allows the segmentation of cuboidal cell nuclei with a mean accuracy of 97%. © 2013 Elsevier Ltd. All rights reserved.

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.