Projeto topológico de uma rede rodoviária

Mestrado em Engenharia Informática - Área de Especialização em Sistemas Gráficos e Multimédia

Extração de feições rodoviárias em imagens digitais: Metodologias desenvolvidas pelo GP-F&VC

Several kinds of research in road extraction have been carried out in the last 6 years by the Photogrammetry and Computer Vision Research Group (GPF&VC - Grupo de Pesquisa em Fotogrametria e Visão Computacional). Several semi-automatic road extraction methodologies have been developed, including sequential and optimizatin techniques. The GP-F&VC has also been developing fully automatic methodologies for road extraction. This paper presents an overview of the GP-F&VC research in road extraction from digital images, along with examples of results obtained by the developed methodologies.

Extração semi-automática de rodovias em um par de imagens aéreas digitais utilizando geometria epipolar e programação dinâmica

The purpose of this paper is to introduce a methodology for semi-automatic road extraction from aerial digital image pairs by using dynamic programming and epipolar geometry. The method uses both images from where each road feature pair is extracted. The operator identifies the corresponding road featuresand s/he selects sparse seed points along them. After all road pairs have been extracted, epipolar geometry is applied to determine the automatic point-to-point correspondence between each correspondent feature. Finally, each correspondent road pair is georeferenced by photogrammetric intersection. Experiments were made with rural aerial images. The results led to the conclusion that the methodology is robust and efficient, even in the presence of shadows of trees and buildings or other irregularities.

Predição de sombras de edifícios sobre vias urbanas com base em um modelo digital de elevações e dados de imagens aéreas de alta-resolução

Pós-graduação em Ciências Cartográficas - FCT

Metodologia automática para extração de cruzamentos de rodovias em imagens de alta resolução

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Uso de técnicas de otimização e agrupamento hierárquico para a complementação automática da malha viária em regiões rurais a partir de imagens aéreas de média e alta resolução

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Traffic sign detection and tracking using robust 3D analysis

In this paper we present an innovative technique to tackle the problem of automatic road sign detection and tracking using an on-board stereo camera. It involves a continuous 3D analysis of the road sign during the whole tracking process. Firstly, a color and appearance based model is applied to generate road sign candidates in both stereo images. A sparse disparity map between the left and right images is then created for each candidate by using contour-based and SURF-based matching in the far and short range, respectively. Once the map has been computed, the correspondences are back-projected to generate a cloud of 3D points, and the best-fit plane is computed through RANSAC, ensuring robustness to outliers. Temporal consistency is enforced by means of a Kalman filter, which exploits the intrinsic smoothness of the 3D camera motion in traffic environments. Additionally, the estimation of the plane allows to correct deformations due to perspective, thus easing further sign classification.

Automatic extraction of road seeds from high-resolution aerial images

This article presents an automatic methodology for extraction of road seeds from high-resolution aerial images. The method is based on a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each one of the road seeds is composed by a sequence of connected road objects, in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. Experiments carried out with high-resolution aerial images showed that the proposed methodology is very promising in extracting road seeds. This article presents the fundamentals of the method and the experimental results, as well.

A neural network based technique for automatic classification of road cracks

This paper presents a neural network based technique for the classification of segments of road images into cracks and normal images. The density and histogram features are extracted. The features are passed to a neural network for the classification of images into images with and without cracks. Once images are classified into cracks and non-cracks, they are passed to another neural network for the classification of a crack type after segmentation. Some experiments were conducted and promising results were obtained. The selected results and a comparative analysis are included in this paper.

Automatic visual detection of human behavior: a review from 2000 to 2014

Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.

One-step nucleic acid amplification (OSNA): a new road to better staging in colon cancer patients?

Introduction: Approximately one fifth of stage I and II colon cancer patients will suffer from recurrent disease. This is partly due to the presence of small nodal tumour infiltrates, which are undetected by standard histopathology using Haematoxylin & Eosin (H&E) staining on one slice and thus may not receive beneficial adjuvant therapy. A new diagnostic, semi-automatic system, called one-step nucleic acid amplification (OSNA), was recently designed for the detection of cytokeratin 19 (CK19) mRNA as a surrogate for lymph node metastases. The objective of the present investigation was to compare the performance of OSNA with both standard H&E as well as intensive histopathologic analyses in the detection of colon cancer lymph node micro- and macro-metastases.Methods: In this prospective study 313 lymph nodes from 22 consecutive stage I - III colon cancer patients were assessed. Half of each lymph node was analysed initially based on one slice of H&E followed by an intensive histologic work-up (5 levels of H&E and immuno-histochemistry staining for each slice), the other half was analysed using OSNA.Results: All OSNA results were available after less than 40 minutes. Fifty-one lymph nodes were positive and 246 lymph nodes negative with both OSNA and standard H&E. OSNA was more sensitive to detect small nodal tumor infiltrates compared to H&E (11 OSNA pos. /H&E neg.). Compared to intensive histopathologic analyses, OSNA had a sensitivity of 94.5% and a specificity of 97.6% to detect lymph node micro- and macro-metastases with a concordance rate of 97.1%. An upstaging due to OSNA was found in 2/13 (15.3%) initially node negative colon cancer patients.Conclusion: OSNA appears to be a powerful and promising molecular tool for the detection of lymph node macro- and micro-metastases in colon cancer patients. OSNA has a similar performance in the detection of micro- and macro-metastases compared to intensive histopathologic investigations and appears to be superior to standard histology with H&E. Since the use of OSNA allows the analysis of the whole lymph node, the problem of sampling bias and undetected tumor deposits due to uninvestigated material will be overcome in the future and OSNA may thus improve staging in colon cancer patients. It is hoped that this improved staging will lead to better patient selection for adjuvant therapy and consecutively improved local and distant control as well as better overall survival.

Automatic traffic sign inventory- and condition analysis

This thesis researches automatic traffic sign inventory and condition analysis using machine vision and pattern recognition methods. Automatic traffic sign inventory and condition analysis can be used to more efficient road maintenance, improving the maintenance processes, and to enable intelligent driving systems. Automatic traffic sign detection and classification has been researched before from the viewpoint of self-driving vehicles, driver assistance systems, and the use of signs in mapping services. Machine vision based inventory of traffic signs consists of detection, classification, localization, and condition analysis of traffic signs. The produced machine vision system performance is estimated with three datasets, from which two of have been been collected for this thesis. Based on the experiments almost all traffic signs can be detected, classified, and located and their condition analysed. In future, the inventory system performance has to be verified in challenging conditions and the system has to be pilot tested.

Georeferenced road extraction and formulation of hypotheses for new road segments

The acquisition and update of Geographic Information System (GIS) data are typically carried out using aerial or satellite imagery. Since new roads are usually linked to georeferenced pre-existing road network, the extraction of pre-existing road segments may provide good hypotheses for the updating process. This paper addresses the problem of extracting georeferenced roads from images and formulating hypotheses for the presence of new road segments. Our approach proceeds in three steps. First, salient points are identified and measured along roads from a map or GIS database by an operator or an automatic tool. These salient points are then projected onto the image-space and errors inherent in this process are calculated. In the second step, the georeferenced roads are extracted from the image using a dynamic programming (DP) algorithm. The projected salient points and corresponding error estimates are used as input for this extraction process. Finally, the road center axes extracted in the previous step are analyzed to identify potential new segments attached to the extracted, pre-existing one. This analysis is performed using a combination of edge-based and correlation-based algorithms. In this paper we present our approach and early implementation results.

Automated extraction of road network from medium-and high-resolution images

This paper presents an automatic methodology for road network extraction from medium-and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road segments) are extracted using a set of four road objects and another set of connection rules among road objects. Each road object is a local representation of an approximately straight road fragment and its construction is based on a combination of polygons describing all relevant image edges, according to some rules embodying road knowledge. Each road seed is composed by a sequence of connected road objects in which each sequence of this type can be geometrically structured as a chain of contiguous quadrilaterals. In the second step, two strategies for road completion are applied in order to generate the complete road network. The first strategy is based on two basic perceptual grouping rules, i.e., proximity and collinearity rules, which allow the sequential reconstruction of gaps between every pair of disconnected road segments. This strategy does not allow the reconstruction of road crossings, but it allows the extraction of road centerlines from the contiguous quadrilaterals representing connected road segments. The second strategy for road completion aims at reconstructing road crossings. Firstly, the road centerlines are used to find reference points for road crossings, which are their approximate positions. Then these points are used to extract polygons representing the contours of road crossings. This paper presents the proposed methodology and experimental results. © Pleiades Publishing, Inc. 2006.

Experiences on the specification of algorithms for fire and smoke control in road tunnels

The main objective of ventilation systems in case of fire is the reduction of the possible consequences by achieving the best possible conditions for the evacuation of the users and the intervention of the emergency services. The required immediate transition, from normal to emergency functioning of the ventilation equipments, is being strengthened by the use of automatic and semi-automatic control systems, what reduces the response times through the help to the operators, and the use of pre-defined strategies. A further step consists on the use of closed-loop algorithms, which takes into account not only the initial conditions but their development (air velocity, traffic situation, etc.), optimizing smoke control capacity.