A ribbon of twins for extracting vessel boundaries

This paper presents an efficient model for automatic detection and extraction of blood vessels in ocular fundus images. The model is formed using a combination of the concept of ribbon snakes and twin snakes. On each edge, the twin concept is introduced by using two snakes, one inside and one outside the boundary. The ribbon concept integrates the pair of twins on the two vessel edges into a single ribbon. The twins maintain the consistency of the vessel width, particularly on very blurred, thin and noisy vessels. The model exhibits excellent performance in extracting the boundaries of vessels, with improved robustness compared to alternative models in the presence of occlusion, poor contrast or noise. Results are presented which demonstrate the performance of the discussed edge extraction method, and show a significant improvement compared to classical snake formulations

Automatic Contextual Thresholding of Color Images: Application in Road Seed Extraction

In this paper is a totally automatic strategy proposed to reduce the complexity of patterns (vegetation, building, soils etc.) that interact with the object ´road´ in color images, thus reducing the difficulty of the automatic extraction of this object. The proposed methodology consists of three sequential steps. In the first step the punctual operator is applied for artificiality index computationknown as NandA (Natural and Artificial). The result is an image whose the intensity attribute is the NandA response. The second step consists in automatically thresholding the image obtained in the previous step, resulting in a binary image. This image usually allows the separation between artificial and natural objects. The third step consists in applying a preexisting road seed extraction methodology to the previous generated binary image. Severalexperiments carried out with real images made the verification of the potential of the proposed methodology possible. The comparison of the obtained result to others obtained by a similar methodology for road seed extraction from gray level images, showed that the main benefit was the drastic reduction of the computational effort.Neste artigo é proposta uma estratégia totalmente automática para reduzir a complexidade de padrões (vegetação, edificações, solos etc.) que interagem com o objeto ´rodovia´ em imagens coloridas, reduzindo conseqüentemente a dificuldade na extração automática desse objeto. A metodologia proposta consiste em três etapas seqüências. Na primeira etapa é aplicado o operador pontual para o cálculo de índice de artificialidade denominado NandA (Natural and Artificial). O resultado é uma imagem cujo atributo de intensidade é a resposta do NandA. A segunda etapa consiste na limiarização automática da imagem obtida no passo anterior, resultando numa imagem binária. Esta imagem geralmente permite separar os objetos artificiais e naturais. A terceira etapa consiste em aplicar uma metodologia preexistentepara a extração de sementes de rodovia a partir da imagem binária gerada na segunda etapa. Vários experimentos realizados com imagens reais possibilitaram uma verificação experimental do potencial da metodologia proposta. A comparação dos resultados obtidos, com os correspondentes gerados por uma metodologia para extração de sementes de rodovia em imagens pancromáticas, possibilitou verificar que o principal benefício foi adrástica diminuição do esforço computacional

Spatio-temporal road detection from aerial imagery using CNNs

The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In order to train this neural network, we have put together a database containing videos of roads from the point of view of a small commercial drone. Additionally, we have developed an image annotation tool based on the watershed technique, in order to perform a semi-automatic labeling of the videos in this database. The experimental results using our modified version of SegNet show a big improvement on the performance of the neural network when using aerial imagery, obtaining over 90% accuracy.Postprint (published version

Extraction de réseaux de rues à partir d'images satellites à haute résolution spatiale

- Cet article traite le problème de l'extraction de réseaux de rues à partir des nouvelles images satellites à haute résolution spatiale. La méthode proposée se décompose en deux modules séquentiels: un graphe topologiquement correct du réseau de rues est tout d'abord extrait, puis les rues sont ensuite extraites en tant qu'éléments de surface. Le graphe topologique du réseau peut être extrait de manière automatisée (par algorithme de suivi minimisant une fonction de coût) ou provenir d'une base de données. L'algorithme d'extraction surfacique des rues fait intervenir des contours actifs combinés à une analyse multirésolution afin d'accélérer la convergence de l'algorithme et de minimiser le problème du bruit géométrique. Cette phase de reconstruction comprend deux étapes séquentielles: l'extraction des rues puis le traitement des intersections. Des résultats de l'extraction de réseaux de rues sont présentés afin d'illustrer les différentes phases de la méthode et les perspectives de recherche sont exposées

Constraint energies for the adaptation of 2d river borderlines to airborne laserscanning data using snakes

The German Authoritative Topographic Cartographic Information System (ATKIS) stores the height and the 2D position of the objects in a dual system. The digital terrain model (DTM), often acquired by airborne laser scanning (ALS), supplies the height information in a regular grid, whereas 2D vector data are provided in the digital landscape model (DLM). However, an increasing number of applications, such as flood risk modelling, require the combined processing and visualization of these two data sets. Due to different kinds of acquisition, processing, and modelling discrepancies exist between the DTM and DLM and thus a simple integration may lead to semantically incorrect 3D objects. For example, rivers may flow uphill. In this paper we propose an algorithm for the adaptation of 2D river borderlines to ALS data by means of snakes. Besides the two basic energy terms of the snake, the internal and image energy, 3D object knowledge is introduced in the constraint energy in order to guarantee the semantic correctness of the rivers in a combined data set. The image energy is based on ALS intensity and height information and derived products. Additionally, features of rivers in the DTM, such as the flow direction or the river profile, are formulated as constraints in order to fulfil the semantic properties of rivers and stabilize the adaptation process. Furthermore, the known concept of twin snakes exploits the width of the river and also supports the procedure. Some results are given to show the applicability of the algorithm

Using building and bridge information for adapting roads to ALS data by means of network snakes

In the German Authoritative Topographic Cartographic Information System (ATKIS), the 2D positions and the heights of objects such as roads are stored separately in the digital landscape model (DLM) and digital terrain model (DTM), which is often acquired by airborne laser scanning (ALS). However, an increasing number of applications require a combined processing and visualization of these two data sets. Due to different kinds of acquisition, processing, and modelling discrepancies exist between the DTM and DLM and thus a simple integration may lead to semantically incorrect 3D objects. For example, roads may be situated on strongly tilted DTM parts and rivers sometimes flow uphill. In this paper we propose an algorithm for the adaptation of 2D road centrelines to ALS data by means of network snakes. Generally, the image energy for the snakes is defined based on ALS intensity and height information and derived products. Additionally, buildings and bridges as strong features in height data are exploited in order to support the road adaptation process. Extracted buildings as priors modified by a distance transform are used to create a force of repulsion for the road vectors integrated in the image energy. In contrast, bridges give strong evidence for the correct road position in the height data. Therefore, the image energy is adapted for the bridge points. For that purpose bridge detection in the DTM is performed starting from an approximate position using template matching. Examples are given which apply the concept of network-snakes with new image energy for the adaptation of road networks to ALS data taking advantage of the prior known topology

ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES

Roads are significant objects of an infrastructure and the extraction of roads from aerial and satellite images are important for different applications such as automated map generation and change detection. Roads are also important to detect other structures such as buildings and urban areas. In this paper, the road extraction approach is based on Active Contour Models for 1-meter resolution gray level images. Active Contour Models contains Snake Approach. During applications, the road structure was separated as salient-roads, non-salient roads and crossings and extraction of these is provided by using Ribbon Snake and Ziplock Snake methods. These methods are derived from traditional snake model. Finally, various experimental results were presented. Ribbon and Ziplock Snake methods were compared for both salient and non-salient roads. Also these methods were used to extract roads in an image. While Ribbon snake is described for extraction of salient roads in an image, Ziplock snake is applied for extraction of non-salient roads. Beside these, some constant variables in literature were redefined and expressed in a formula as depending on snake approach and a new approach for extraction of crossroads were described and tried

Automatic shadows detection in high resolution images

This paper presents a development of filter for shadows detection in high resolution digital image. Shadows, many times, are very useful elements as information of contextual objects in digital images. Usually, in many image analysis and interpretation applications, shadows are critical to solve problems to automatic objects extraction and interpretation. For this reason, shadow segmentation is an important step in image analysis and many techniques have been proposed to solve the problem automatically. The proposed techniques identify automatically the shadows which are present in high resolution images. The results showed that the proposed filter is efficient in identifying shadows for a large class of scenes.Este artigo apresenta o desenvolvimento de um filtro de detecção automática de sombras presentes em imagens de alta resolução. As sombras são elementos que permitem a obtenção de informação de vizinhança a cerca de objetos presentes em imagens aéreas, bem como auxilia na análise de fluxo de tráfego entre outras aplicações. Por outro lado, são elementos indesejáveis na análise de imagens digitais, principalmente porque ofuscam a informação de cor ou intensidade do objeto sobre o qual é projetada. Várias técnicas de Processamento Digital de Imagens e tratamento radiométrico diferenciado estão sendo implementadas com a finalidade de solucionar o problema automaticamente. O objetivo deste trabalho é propor um filtro de detecção automática de sombras (Shadows Automatic Detection - SAD), que será testado em imagens de alta resolução tomadas por câmaras não métricas. Nos experimentos realizados, os resultados mostraram que o filtro proposto é eficiente e adequado na detecção automática de sombras em imagens de alta resolução