This paper presents a general framework to segment curvilinear objects in 2D images. A pre-processing step relies on mathematical morphology to obtain a connected line which encloses curvilinear objects. Then, a graph is constructed from this line and a Markovian Random Field is defined to perform objects segmentation. Applications of our framework are numerous: they go from simple surve segmen...

In order to improve the accuracy and real-time of structured road recognition algorithm, this paper puts forward a recognition algorithm of the structural road based on piecewise linear model. Firstly the original image was the initial region of interest division and pretreatment, using an improved Otsu algorithm and mathematical morphology processing image segmentation, eliminate noise and spl...

In this paper we propose a method for semantic segmentation of street-side images. Segmentation and classification is pixel based and results in classes of building facades, sections of sky, road and other areas present in general images taken in the urban environment. A segmentation method is suggested and detected segments are classified. Final classification is reinforced using context infor...

Dynamic segmentation is viewed as one of the most important functions of GIS for transportation applications. Although the road network and associated events (e.g. pavement material, traffic volume and incidents) can be referenced to both space and time, the spatial and temporal dimensions has not been well integrated. This paper explores how to model space-varying, time-varying and space-time-...

In this paper an approach to road extraction in open landscape regions from IKONOS multispectral imagery is presented which combines a line-based approach for road extraction with area-based colour segmentation. Existing road databases are used in two ways: firstly, to estimate scene dependent parameters of the line-based approach and secondly to exclude non road regions from the extraction, al...

An efficient shape-based recognition system of U.S. speed limit road signs is presented in this paper. The proposed system accomplishes speed sign detection and recognition processes using three main stages, namely, geometrical-based detection of rectangular road signs, shape-based segmentation and feature extraction, and pattern classification using a K-nearest neighbor classifier (KNN). Twent...

This paper addresses the problem of holistic road scene understanding based on the integration of visual and range data. To achieve the grand goal, we propose an approach that jointly tackles object-level image segmentation and semantic region labeling within a conditional random field (CRF) framework. Specifically, we first generate semantic object hypotheses by clustering 3D points, learning ...

Object segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images, has been applied to many application domains, especially in road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts at applying the deep convolutional neural network (DCNN) to extract roads from remote...

Object segmentation on remotely-sensed images: aerial (or very high resolution, VHS) images and satellite (or high resolution, HR) images, has been applied to many application domains, especially road extraction in which the segmented objects are served as a mandatory layer in geospatial databases. Several attempts in applying deep convolutional neural network (DCNN) to extract roads from remot...

Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...