Extração de vias combinando métodos de detecção de regiões e linhas em imagens de intensidade de pulso laser

This paper aims at extracting street centerlines from previously isolated street regions by using the image of laser scanning intensity. In this image, streets are easily identified, since they manifest as dark, elongate ribbons contrasting with background objects. The intensity image is segmented by using the region growing technique, which generates regions representing the streets. From these regions, the street centerlines are extracted in two manners. The first one is through the Steger lines detection method combined with a line length thresholding by which lines being shorter than a minimum length are removed. The other manner is by combining the skeletonization method of regions based on the Medial Axis Transform and with a pruning process to eliminate as much as possible the ramifications. Experiments showed that the Steger-based method provided results better than the method based on skeletonization.

Método de detecção e classificação de lesões de pele em imagens digitais a partir do modelo Chan-Vese e máquina de vetor de suporte

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Jarina: o marfim das biojóias da Amazônia

A palmeira jarina (Phytelephas macrocarpa) é endêmica da Amazônia, onde se desenvolve sobre antigas planícies de inundação, cujos sedimentos são constituídos por quartzo, minerais de argila 2:1 e feldspatos, constituindo solos férteis e pouco ácidos a neutros. As sementes dessa palmeira são incluídas entre as gemas orgânicas raras. Devido a sua cor e brilho, as sementes são comparadas ao marfim animal, apesar da baixa dureza e baixa densidade, sendo empregadas na manufatura de biojóias e artefatos. Esses produtos são bem aceitos comercialmente devido às sementes serem susceptíveis a mudança de coloração e outros melhoramentos. Infelizmente, as jóias não apresentam vida longa, pois as sementes podem sofrer ataque de microorganismos entre 5 e 10 anos. Se houver uma política adequada para cadeia produtiva das sementes de jarina, a mesma poderá se tornar de grande importância para o desenvolvimento da região Amazônica, ao criar novas oportunidades de trabalho e agregação de valor aos produtos. No entanto faz-se necessário um especial cuidado para evitar exploração inadequada das sementes para assegurar a preservação da espécie.

A new information fusion method for land-use classification using high resolution satellite imagery

Satellite image classification involves designing and developing efficient image classifiers. With satellite image data and image analysis methods multiplying rapidly, selecting the right mix of data sources and data analysis approaches has become critical to the generation of quality land-use maps. In this study, a new postprocessing information fusion algorithm for the extraction and representation of land-use information based on high-resolution satellite imagery is presented. This approach can produce land-use maps with sharp interregional boundaries and homogeneous regions. The proposed approach is conducted in five steps. First, a GIS layer - ATKIS data - was used to generate two coarse homogeneous regions, i.e. urban and rural areas. Second, a thematic (class) map was generated by use of a hybrid spectral classifier combining Gaussian Maximum Likelihood algorithm (GML) and ISODATA classifier. Third, a probabilistic relaxation algorithm was performed on the thematic map, resulting in a smoothed thematic map. Fourth, edge detection and edge thinning techniques were used to generate a contour map with pixel-width interclass boundaries. Fifth, the contour map was superimposed on the thematic map by use of a region-growing algorithm with the contour map and the smoothed thematic map as two constraints. For the operation of the proposed method, a software package is developed using programming language C. This software package comprises the GML algorithm, a probabilistic relaxation algorithm, TBL edge detector, an edge thresholding algorithm, a fast parallel thinning algorithm, and a region-growing information fusion algorithm. The county of Landau of the State Rheinland-Pfalz, Germany was selected as a test site. The high-resolution IRS-1C imagery was used as the principal input data.

Distance measures for image segmentation evaluation

The task considered in this paper is performance evaluation of region segmentation algorithms in the ground-truth-based paradigm. Given a machine segmentation and a ground-truth segmentation, performance measures are needed. We propose to consider the image segmentation problem as one of data clustering and, as a consequence, to use measures for comparing clusterings developed in statistics and machine learning. By doing so, we obtain a variety of performance measures which have not been used before in image processing. In particular, some of these measures have the highly desired property of being a metric. Experimental results are reported on both synthetic and real data to validate the measures and compare them with others.

Segmentation and Recognition of Multi-Food Meal Images for Carbohydrate Counting

In this paper, we propose novel methodologies for the automatic segmentation and recognition of multi-food images. The proposed methods implement the first modules of a carbohydrate counting and insulin advisory system for type 1 diabetic patients. Initially the plate is segmented using pyramidal mean-shift filtering and a region growing algorithm. Then each of the resulted segments is described by both color and texture features and classified by a support vector machine into one of six different major food classes. Finally, a modified version of the Huang and Dom evaluation index was proposed, addressing the particular needs of the food segmentation problem. The experimental results prove the effectiveness of the proposed method achieving a segmentation accuracy of 88.5% and recognition rate equal to 87%

Evaluación mediante imágenes estereoscópicas hemisféricas de tratamientos selvícolas en sistemas forestales restaurados. El caso del Parque Nacional de Sierra Nevada.

Los pinares de repoblación del Parque Nacional de Sierra Nevada presentan graves problemas ecológicos debido al exceso de densidad y a la falta de actuaciones selvícolas durante las primeras etapas de crecimiento de los árboles plantados. En este trabajo se evalúan los efectos de tratamientos de claras con diferentes intensidades en la estructura, composición y regeneración de especies de frondosas en parcelas experimentales de pino, principalmente de Pinus halepensis L. Para la caracterización de la estructura se ha desarrollado y testado una nueva metodología que permite obtener la posición y diámetro de los pies a partir de fotografías estereoscópicas hemisféricas tomadas con el sistema de medición MU2005-01738, el cual ha sido desarrollado en el INIA. La información obtenida a partir de estas imágenes permitió calcular índices de patrón espacial, diferenciación, diversidad diamétrica y cobertura de las masas forestales. La diversidad de especies y la regeneración se caracterizaron a partir de inventarios florísticos. Los resultados indican que, en el proceso de estimación de variables estructurales de los árboles mediante el análisis de las imágenes tomadas, la identificación de árboles homólogos basado en el método de “region growing” a partir de un punto seleccionado por el usuario, es más precisa que la obtenida mediante clasificación automática de todos los píxeles de la imagen. Por otro lado, los índices obtenidos muestran que los tratamientos con claras juegan un papel clave en la dinámica de la diversidad estructural, aumentando la heterogeneidad espacial y contribuyendo a restablecer la dinámica sucesional y la diversidad de las masas de repoblación estudiadas.

Land Cover Mapping by an Optimized Object-Oriented Approach. Case of Study Mediterranean Landscapes

Remote sensing information from spaceborne and airborne platforms continues to provide valuable data for different environmental monitoring applications. In this sense, high spatial resolution im-agery is an important source of information for land cover mapping. For the processing of high spa-tial resolution images, the object-based methodology is one of the most commonly used strategies. However, conventional pixel-based methods, which only use spectral information for land cover classification, are inadequate for classifying this type of images. This research presents a method-ology to characterise Mediterranean land covers in high resolution aerial images by means of an object-oriented approach. It uses a self-calibrating multi-band region growing approach optimised by pre-processing the image with a bilateral filtering. The obtained results show promise in terms of both segmentation quality and computational efficiency.

The use of context in the classification of urban aerial imagery

Urban regions present some of the most challenging areas for the remote sensing community. Many different types of land cover have similar spectral responses, making them difficult to distinguish from one another. Traditional per-pixel classification techniques suffer particularly badly because they only use these spectral properties to determine a class, and no other properties of the image, such as context. This project presents the results of the classification of a deeply urban area of Dudley, West Midlands, using 4 methods: Supervised Maximum Likelihood, SMAP, ECHO and Unsupervised Maximum Likelihood. An accuracy assessment method is then developed to allow a fair representation of each procedure and a direct comparison between them. Subsequently, a classification procedure is developed that makes use of the context in the image, though a per-polygon classification. The imagery is broken up into a series of polygons extracted from the Marr-Hildreth zero-crossing edge detector. These polygons are then refined using a region-growing algorithm, and then classified according to the mean class of the fine polygons. The imagery produced by this technique is shown to be of better quality and of a higher accuracy than that of other conventional methods. Further refinements are suggested and examined to improve the aesthetic appearance of the imagery. Finally a comparison with the results produced from a previous study of the James Bridge catchment, in Darleston, West Midlands, is made, showing that the Polygon classified ATM imagery performs significantly better than the Maximum Likelihood classified videography used in the initial study, despite the presence of geometric correction errors.

LCD-OpenPACS: sistema integrado de telerradiologia com auxílio ao diagnóstico de nódulos pulmonares em exames de tomografia computadorizada

Lung cancer is one of the most common types of cancer and has the highest mortality rate. Patient survival is highly correlated with early detection. Computed Tomography technology services the early detection of lung cancer tremendously by offering aminimally invasive medical diagnostic tool. However, the large amount of data per examination makes the interpretation difficult. This leads to omission of nodules by human radiologist. This thesis presents a development of a computer-aided diagnosis system (CADe) tool for the detection of lung nodules in Computed Tomography study. The system, called LCD-OpenPACS (Lung Cancer Detection - OpenPACS) should be integrated into the OpenPACS system and have all the requirements for use in the workflow of health facilities belonging to the SUS (Brazilian health system). The LCD-OpenPACS made use of image processing techniques (Region Growing and Watershed), feature extraction (Histogram of Gradient Oriented), dimensionality reduction (Principal Component Analysis) and classifier (Support Vector Machine). System was tested on 220 cases, totaling 296 pulmonary nodules, with sensitivity of 94.4% and 7.04 false positives per case. The total time for processing was approximately 10 minutes per case. The system has detected pulmonary nodules (solitary, juxtavascular, ground-glass opacity and juxtapleural) between 3 mm and 30 mm.

Impact of Rapid Transit on the Residential Market in Hudson County, New Jersey

The New York Metropolitan region is one of the most populous urban agglomerations in the world, and the single largest in North America.[1] It is also one of the most prominent economic centers, with New York City at the epicenter of its growth. With the entire region growing rapidly over the last decade, it is essential to analyze the socio-economic changes in order to understand the impact it has on commercial real estate. With its focus on housing rentals, this study aims to highlight housing costs as a function of rapid transit over time.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

Insects associated with tropical foliage produced in the coffee growing region of Colombia

We conducted a survey of insects and pest management practices on 34 farms growing ornamental tropical foliage plants in the central coffee region of Colombia over two years. Tropical foliage provided habitat for a diverse range of insects. In total, phytophagous or detritivorous insects from six orders, 40 families and 62 genera were collected. The most common were Hemiptera (29 genera from 16 families), followed by Coleoptera (17 genera from 4 families), Diptera (5 genera from 5 families), Lepidoptera (5 genera from 4 families), Hymenoptera (3 genera from 2 families) and Orthoptera (2 genera from 2 families). The most common phytophagous species were leaf cutting ants (Atta and Acromyrmex spp.), leaf beetles (Chrysomelidae), leafhoppers (Cicadellidae), stinkbugs (Pentatomidae), squash bugs (Coreidae), tree hoppers (Membracidae) and plant hoppers (Fulgoridae). Beneficial insects identified from tropical foliage included predators and parasitoids amongst 5 orders, 12 families and 22 genera. The most abundant were predators among the Coccinellidae, Chrysopidae, Reduviidae, Lycidae and Formicidae but only low numbers of parasitoids (Ichneumonidae, Braconidae and Tachinidae) were collected. A pest management questionnaire given to growers revealed a preponderance of reliance on broad spectrum insecticides with a smaller number of growers (approximately one third) also using some biological control methods. Our survey contributes basic information regarding diversity of Neotropical insects associated with ornamental foliage plants.

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach