Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

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

Dynamic programming approach for road centerline extraction from digital images

This paper presents a dynamic programming approach for semi-automated road extraction from medium-and high-resolution images. This method is a modified version of a pre-existing dynamic programming method for road extraction from low-resolution images. The basic assumption of this pre-existing method is that roads manifest as lines in low-resolution images (pixel footprint> 2 m) and as such can be modeled and extracted as linear features. On the other hand, roads manifest as ribbon features in medium- and high-resolution images (pixel footprint ≤ 2 m) and, as a result, the focus of road extraction becomes the road centerlines. The original method can not accurately extract road centerlines from medium- and high- resolution images. In view of this, we propose a modification of the merit function of the original approach, which is carried out by a constraint function embedding road edge properties. Experimental results demonstrated the modified algorithm's potential in extracting road centerlines from medium- and high-resolution images.

Design of SPR systems with dynamic compensators and output variable structure control

This paper presents necessary and sufficient conditions for the following problem: given a linear time invariant plant G(s) = N(s)D(s)-1 = C(sI - A]-1B, with m inputs, p outputs, p > m, rank(C) = p, rank(B) = rank(CB) = m, £nd a tandem dynamic controller Gc(s) = D c(s)-1Nc(s) = Cc(sI - A c)-1Bc + Dc, with p inputs and m outputs and a constant output feedback matrix Ko ε ℝm×p such that the feedback system is Strictly Positive Real (SPR). It is shown that this problem has solution if and only if all transmission zeros of the plant have negative real parts. When there exists solution, the proposed method firstly obtains Gc(s) in order to all transmission zeros of Gc(s)G(s) present negative real parts and then Ko is found as the solution of some Linear Matrix Inequalities (LMIs). Then, taking into account this result, a new LMI based design for output Variable Structure Control (VSC) of uncertain dynamic plants is presented. The method can consider the following design specifications: matched disturbances or nonlinearities of the plant, output constraints, decay rate and matched and nonmatched plant uncertainties. © 2006 IEEE.

Object-Space Road Extraction in Rural Areas Using Stereoscopic Aerial Images

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

Extração semiautomática de contornos de telhado de edifícios com base em snakes e programação dinâmica

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

Extração semi-automática de rodovia baseada em um par estéreo de imagens aéreas e em otimização por programação dinâmica no espaço-objeto

This article proposes a method for 3D road extraction from a stereopair of aerial images. The dynamic programming (DP) algorithm is used to carry out the optimization process in the object-space, instead of usually doing it in the image-space such as the DP traditional methodologies. This means that road centerlines are directly traced in the object-space, implying that a mathematical relationship is necessary to connect road points in object and image-space. This allows the integration of radiometric information from images into the associate mathematical road model. As the approach depends on an initial approximation of each road, it is necessary a few seed points to coarsely describe the road. Usually, the proposed method allows good results to be obtained, but large anomalies along the road can disturb its performance. Therefore, the method can be used for practical application, although it is expected some kind of local manual edition of the extracted road centerline.

Optimal pest control problem in population dynamics

One of the main goals of the pest control is to maintain the density of the pest population in the equilibrium level below economic damages. For reaching this goal, the optimal pest control problem was divided in two parts. In the first part, the two optimal control functions were considered. These functions move the ecosystem pest-natural enemy at an equilibrium state below the economic injury level. In the second part, the one optimal control function stabilizes the ecosystem in this level, minimizing the functional that characterizes quadratic deviations of this level. The first problem was resolved through the application of the Maximum Principle of Pontryagin. The Dynamic Programming was used for the resolution of the second optimal pest control problem.

Extração semiautomática de rodovias no espaço-objeto: uso integrado de um estereopar de imagens aéreas e um MDT

Neste artigo é proposto um método semiautomático para extração de rodovias combinando um estereopar de imagens aéreas de baixa resolução com um poliedro gerado a partir de um modelo digital do terreno (MDT). O problema é formulado no espaço-objeto através de uma função objetivo que modela o objeto 'rodovia' como uma curva suave e pertencente a uma superfície poliédrica. A função objetivo proposta depende também de informações radiométricas, que são acessadas no espaço-imagem via relação de colinearidade entre pontos da rodovia no espaço-objeto e os correspondentes nos espaços imagem do estereopar. A linha poligonal que melhor modela a rodovia selecionada é obtida por otimização no espaço-objeto da função objetivo, tendo por base o algoritmo de programação dinâmica. O processo de otimização é iterativo e dependente do fornecimento por um operador de uma aproximação inicial para a rodovia selecionada. Os resultados obtidos mostraram que o método é robusto frente a anomalias existentes ao longo das rodovias, tais como obstruções causadas por sombras e árvores.

On the value function for nonautonomous optimal control problems with infinite horizon

In this paper we consider nonautonomous optimal control problems of infinite horizon type, whose control actions are given by L-1-functions. We verify that the value function is locally Lipschitz. The equivalence between dynamic programming inequalities and Hamilton-Jacobi-Bellman (HJB) inequalities for proximal sub (super) gradients is proven. Using this result we show that the value function is a Dini solution of the HJB equation. We obtain a verification result for the class of Dini sub-solutions of the HJB equation and also prove a minimax property of the value function with respect to the sets of Dini semi-solutions of the HJB equation. We introduce the concept of viscosity solutions of the HJB equation in infinite horizon and prove the equivalence between this and the concept of Dini solutions. In the Appendix we provide an existence theorem. (c) 2006 Elsevier B.V. All rights reserved.

Neural approach for solving several types of optimization problems

Neural networks consist of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of recurrent neural net-works that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its inter-nal parameters are computed explicitly using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points, which represent a solution of the problem considered. The problems that can be treated by the proposed approach include combinatorial optimiza-tion problems, dynamic programming problems, and nonlinear optimization problems.

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.

Projeto E análise de uma rede neural para resolver problemas de programação dinâmica

Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. Neural networks with feedback connections provide a computing model capable of solving a large class of optimization problems. This paper presents a novel approach for solving dynamic programming problems using artificial neural networks. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points which represent solutions (not necessarily optimal) for the dynamic programming problem. Simulated examples are presented and compared with other neural networks. The results demonstrate that proposed method gives a significant improvement.

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.

Optimal linear and nonlinear control design for chaotic systems

In this work, the linear and nonlinear feedback control techniques for chaotic systems were been considered. The optimal nonlinear control design problem has been resolved by using Dynamic Programming that reduced this problem to a solution of the Hamilton-Jacobi-Bellman equation. In present work the linear feedback control problem has been reformulated under optimal control theory viewpoint. The formulated Theorem expresses explicitly the form of minimized functional and gives the sufficient conditions that allow using the linear feedback control for nonlinear system. The numerical simulations for the Rössler system and the Duffing oscillator are provided to show the effectiveness of this method. Copyright © 2005 by ASME.

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.