Light Detection and Ranging (LiDAR), a high-precision technique used for acquiring three-dimensional (3D) surface information, is widely used to study surface vegetation information. Moreover, the extraction of a vegetation point set from the LiDAR point cloud is a basic starting-point for vegetation information analysis, and an important part of its further processing. To extract the vegetatio...

We present two approaches for automatically segmenting the spinal cord/canal from native CT images of the thorax region containing the spine. Different strategies are included to handle images where only part of the spinal column is visible. The algorithms require one seed point given on a slice located in the middle region of the spine, and the rest is automatic. The spatial extent of the spin...

A framework of fuzzy information fusion is proposed in this paper to automatically segment tumor areas of human brain from multispectral magnetic resonance imaging (MRI) such as T1-weighted, T2-weighted and proton density (PD) images. A priori knowledge about tumors described by radiology experts for different types of MRI are very helpful to guide a automatic and a precise segmentation. Howeve...

In the two previous papers of this serie, we have created a library, called Population, dedicated to seeded region growing by pixels aggregation and we have proposed different growing processes to get a partition with or without a boundary region to divide the other regions or to get a partition invariant about the seeded region initialisation order. Using this work, we implement some algorithm...

We present a new and efficient algorithm for decomposition of arbitrary triangle meshes into connected subsets of meshes called regions. Our method, based on discrete curvature analysis decomposes the object into almost constant curvature surfaces and not only “cut” the object along its hard edges like traditional methods. This algorithm is an hybrid approach vertex-triangle, it is based on thr...

The segmentation of images with poor contrast characteristics is an important issue in Medical Computer Vision. Often image segmentation results are either oversegmented, with “objects” divided into parts, or incorrectly segmented, with two or more anatomies segmented as one single object. This problem occurs in all types of segmentation approaches, but is of particular importance in the field ...

Two-dimensional gel electrophoresis (2-DE) images show the expression levels of several hundred of proteins where each protein is represented as a blob shaped spot of grey level values. The spot detection, i.e. segmentation process has to be efficient as it is the first step in the gel processing. Such extraction of information is a very complex task. In this paper we propose a real time spot d...

Detecting moving pedestrians and vehicles with foreground segmentation algorithms is problematic during fluctuating lighting conditions. Edge-based approaches are more robust to lighting than the conventional intensity-based ones. The issue with edge-based approaches though is segmenting the internal foreground areas. In this work a strategy is developed to detect complete foreground areas. Fir...

The algorithm presented in this paper is comprised of three main stages: (1) classification of the image sequence and, in the case of a moving camera, parametric motion estimation, (2) change detection having as reference a fixed frame, an appropriately selected frame or a displaced frame, and (3) object localization using local colour features. The image sequence classification is based on sta...

A new slippage analysis method for recognizing basic primitive surfaces of CAD models is presented in this paper. Obtaining the exact normal and searching the appropriate local region of each point are found to be the key steps for determining the local slippage motion type. First, the tensor voting-based boundary point recognitionmethod is integrated to preprocess the original points. Then, th...