Um estudo sobre o osu do modelo de cor RGB no proceso de correspondência por correlação

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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.

A regularized nonlinear diffusion approach for texture image denoising

In this paper a new partial differential equation based method is presented with a view to denoising images having textures. The proposed model combines a nonlinear anisotropic diffusion filter with recent harmonic analysis techniques. A wave atom shrinkage allied to detection by gradient technique is used to guide the diffusion process so as to smooth and maintain essential image characteristics. Two forcing terms are used to maintain and improve edges, boundaries and oscillatory features of an image having irregular details and texture. Experimental results show the performance of our model for texture preserving denoising when compared to recent methods in literature. © 2009 IEEE.

ANN statistical image recognition method for computer vision in agricultural mobile robot navigation

The main application area in this project, is to deploy image processing and segmentation techniques in computer vision through an omnidirectional vision system to agricultural mobile robots (AMR) used for trajectory navigation problems, as well as localization matters. Thereby, 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 image recognition. Hence, it was possible to run simulations and carry out analyses of the performance of JSEG image segmentation technique through Matlab/Octave computational platforms, along with the application of customized Back-propagation Multilayer Perceptron (MLP) algorithm and statistical methods as structured heuristics 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 segmented images in which reasonably accurate results were obtained. © 2010 IEEE.

Image categorization through optimum path forest and visual words

Different from the first attempts to solve the image categorization problem (often based on global features), recently, several researchers have been tackling this research branch through a new vantage point - using features around locally invariant interest points and visual dictionaries. Although several advances have been done in the visual dictionaries literature in the past few years, a problem we still need to cope with is calculation of the number of representative words in the dictionary. Therefore, in this paper we introduce a new solution for automatically finding the number of visual words in an N-Way image categorization problem by means of supervised pattern classification based on optimum-path forest. © 2011 IEEE.

Improving image classification through descriptor combination

The efficiency in image classification tasks can be improved using combined information provided by several sources, such as shape, color, and texture visual properties. Although many works proposed to combine different feature vectors, we model the descriptor combination as an optimization problem to be addressed by evolutionary-based techniques, which compute distances between samples that maximize their separability in the feature space. The robustness of the proposed technique is assessed by the Optimum-Path Forest classifier. Experiments showed that the proposed methodology can outperform individual information provided by single descriptors in well-known public datasets. © 2012 IEEE.

Evaluation epidermal p53 immunostaining by digital image analysis

Digital techniques have been developed and validated to assess semiquantitatively immunohistochemical nuclear staining. Currently visual classification is the standard for qualitative nuclear evaluation. Analysis of pixels that represents the immunohistochemical labeling can be more sensitive, reproducible and objective than visual grading. This study compared two semiquantitative techniques of digital image analysis with three techniques of visual analysis imaging to estimate the p53 nuclear immunostaining. Methods: Sixty-three sun-exposed forearm-skin biopsies were photographed and submitted to three visual analyses of images: the qualitative visual evaluation method (0 to 4 +), the percentage of labeled nuclei and HSCORE. Digital image analysis was performed using ImageJ 1.45p; the density of nuclei was scored per ephitelial area (DensNU) and the pixel density was established in marked suprabasal epithelium (DensPSB). Results: Statistical significance was found in: the agreement and correlation among the visual estimates of evaluators, correlation among the median visual score of the evaluators, the HSCORE and the percentage of marked nuclei with the DensNU and DensPSB estimates. DensNU was strongly correlated to the percentage of p53-marked nuclei in the epidermis, and DensPSB with the HSCORE. Conclusion: The parameters presented herein can be applied in routine analysis of immunohistochemical nuclear staining of epidermis. © 2012 John Wiley & Sons A/S.

Application of two segmentation protocols during the processing of virtual images in rapid prototyping: ex vivo study with human dry mandibles

The aim of this study was to evaluate the accuracy of virtual three-dimensional (3D) reconstructions of human dry mandibles, produced from two segmentation protocols (outline only and all-boundary lines).Twenty virtual three-dimensional (3D) images were built from computed tomography exam (CT) of 10 dry mandibles, in which linear measurements between anatomical landmarks were obtained and compared to an error probability of 5 %.The results showed no statistically significant difference among the dry mandibles and the virtual 3D reconstructions produced from segmentation protocols tested (p = 0,24).During the designing of a virtual 3D reconstruction, both outline only and all-boundary lines segmentation protocols can be used.Virtual processing of CT images is the most complex stage during the manufacture of the biomodel. Establishing a better protocol during this phase allows the construction of a biomodel with characteristics that are closer to the original anatomical structures. This is essential to ensure a correct preoperative planning and a suitable treatment.

Utilização de geotecnologias no monitoramento de processos erosivos e movimentos de massa em trecho da faixa de dutos Rio de Janeiro - Belo Horizonte

The transportation of oil through pipelines raises a concern related to safety and environmental impacts they may cause, especially when exposed to risks that affect their integrity. Among the natural phenomena that can affect the pipelines are erosion and landslides. Considering the large territory involving the pipelines, remote sensing tools have a great applicability for data acquisition. For this, visual analysis techniques were applied to perform change detection in order to monitor erosion features and landslides along a stretch of pipeline Rio de Janeiro – Belo Horizonte, in the state of Rio de Janeiro. The work involved the characterization of the study area as well as the erosion and landslide processes, through bibliographical data. The satellite image processing and the application of change detection techniques were developed in two scenes for the years 2002 and 2010. It was noted a small increase in the number of the identified features, however with regard to their area, a decrease of 21.7% was observed

Avaliação do método de classificação baseada no objeto em imagens de alta resolução espacial aplicado para o monitoramento de faixa de dutos: estudo de caso no entorno da refinaria de Capuava em Mauá - SP

The metropolitan region of São Paulo is the most populous of the country, this happens because of its great importance in the national economy and the job opportunities that are offered to the population. These factors result in intense population growth and urban expansion, reaching some non-habitable places of the metropolis, as areas of pipelines, which are very important for the transportation of natural gas, oil and its derivatives. Before the population growth of the region, these sites were unoccupied, do not presenting problems for the population. However, with the disorderly occupation is generated great anthropogenic pressure on the pipeline stitches, causing risks to people who are around them. Therefore it is extremely important to monitor the strip of pipelines through products and techniques of remote sensing and geoprocessing, enabling, through high spatial resolution images, identification of objects or phenomena that occur on Earth's surface that can alter the functioning and safety of pipelines. Therefore, this study aims to monitor a stretch of the area of the pipeline mesh GASPAL/OSVAT and Capuava Refinery (RECAP), located on the outskirts of the metropolitan area of São Paulo in the city of Mauá, who suffer great human pressure, proving thus the techniques of remote sensing and geographic information system (GIS) as effective tools for monitoring phenomena occurred in urban areas of great complexity. The monitoring was done by object-based classification applied in orbital images Ikonos II and RapidEye, of high spatial resolution and, image processing, detection of objects, segmentation, classification and editing were developed through the eCognition and ArcGis softwares. To determine the statistical accuracy of the mapping of the land cover of the stretch of pipeline in Maua, the results were analyzed by error matrix... (Complete abstract click electronic access below)

Effect of digitization parameters on periapical radiographic image quality with regard to anatomic landmarks

The aim of this study was to evaluate the influence of digitization parameters on periapical radiographic image quality, with regard to anatomic landmarks. Digitized images (n = 160) were obtained using a flatbed scanner with resolutions of 300, 600 and 2400 dpi. The radiographs of 2400 dpi were decreased to 300 and 600 dpi before storage. Digitizations were performed with and without black masking using 8-bit and 16-bit grayscale and saved in TIFF format. Four anatomic landmarks were classified by two observers (very good, good, moderate, regular, poor), in two random sessions. Intraobserver and interobserver agreements were evaluated by Kappa statistics. Inter and intraobserver agreements ranged according to the anatomic landmarks and resolution used. The results obtained demonstrated that the cement enamel junction was the anatomic landmark that presented the poorest concordance. The use of black masking provided better results in the digitized image. The use of a mask to cover radiographs during digitization is necessary. Therefore, the concordance ranged from regular to moderate for the intraobserver evaluation and concordance ranged from regular to poor for interobserver evaluation.

Fluorescence Level of Composites Assessed by Computer Processing of Digital Images - ScanWhite

The human dentition is naturally translucent, opalescent and fluorescent. Differences between the level of fluorescence of tooth structure and restorative materials may result in distinct metameric properties and consequently perceptible disparate esthetic behavior, which impairs the esthetic result of the restorations, frustrating both patients and staff. In this study, we evaluated the level of fluorescence of different composites (Durafill in tones A2 (Du), Charisma in tones A2 (Ch), Venus in tone A2 (Ve), Opallis enamel and dentin in tones A2 (OPD and OPE), Point 4 in tones A2 (P4), Z100 in tones A2 ( Z1), Z250 in tones A2 (Z2), Te-Econom in tones A2 (TE), Tetric Ceram in tones A2 (TC), Tetric Ceram N in tones A1, A2, A4 (TN1, TN2, TN4), Four seasons enamel and dentin in tones A2 (and 4SD 4SE), Empress Direct enamel and dentin in tones A2 (EDE and EDD) and Brilliant in tones A2 (Br)). Cylindrical specimens were prepared, coded and photographed in a standardized manner with a Canon EOS digital camera (400 ISO, 2.8 aperture and 1/ 30 speed), in a dark environment under the action of UV light (25 W). The images were analyzed with the software ScanWhite©-DMC/Darwin systems. The results showed statistical differences between the groups (p < 0.05), and between these same groups and the average fluorescence of the dentition of young (18 to 25 years) and adults (40 to 45 years) taken as control. It can be concluded that: Composites Z100, Z250 (3M ESPE) and Point 4 (Kerr) do not match with the fluorescence of human dentition and the fluorescence of the materials was found to be affected by their own tone.

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.

Image segmentation using morphological watershed applied to cartography

Image segmentation is a process frequently used in several different areas including Cartography. Feature extraction is a very troublesome task, and successful results require more complex techniques and good quality data. The aims of this paper is to study Digital Image Processing techniques, with emphasis in Mathematical Morphology, to use Remote Sensing imagery, making image segmentation, using morphological operators, mainly the multi-scale morphological gradient operator. In the segmentation process, pre-processing operators of Mathematical Morphology were used, and the multi-scales gradient was implemented to create one of the images used as marker image. Orbital image of the Landsat satellite, sensor TM was used. The MATLAB software was used in the implementation of the routines. With the accomplishment of tests, the performance of the implemented operators was verified and carried through the analysis of the results. The extration of linear feature, using mathematical morphology techniques, can contribute in cartographic applications, as cartographic products updating. The comparison to the best result obtained was performed by means of the morphology with conventional techniques of features extraction. © Springer-Verlag 2004.

Riverbed: A Novel User-Steered Image Segmentation Method Based on Optimum Boundary Tracking

This paper presents an optimum user-steered boundary tracking approach for image segmentation, which simulates the behavior of water flowing through a riverbed. The riverbed approach was devised using the image foresting transform with a never-exploited connectivity function. We analyze its properties in the derived image graphs and discuss its theoretical relation with other popular methods such as live wire and graph cuts. Several experiments show that riverbed can significantly reduce the number of user interactions (anchor points), as compared to live wire for objects with complex shapes. This paper also includes a discussion about how to combine different methods in order to take advantage of their complementary strengths.