Flexible shape extraction for micro/nano scale structured surfaces.

Surface feature is the one of the most important factors affecting the functionality and reliability of micro scale patterned surfaces. For micro scale patterned surface characterisation, it’s important to extract the surface feature effectively and accurately. The active contours, known as “snakes”, have been successfully used to segment, match and track the objects of interest. The active contours have been applied to facial boundary detection, medical image processing, motion correction, etc. In this paper, surface feature extraction techniques based on active contours have been investigated. Parametric active contour models and geometric active contour models have been presented. Also, a group of examples has been selected here to demonstrate the feasibility and applicability of the surface pattern extraction techniques based on active contours. At last, experimental results will be given and discussed

The Potential of Active Contour Models in Extracting Road Edges from Mobile Laser Scanning Data

Active contour models present a robust segmentation approach, which makes efficient use of specific information about objects in the input data rather than processing all of the data. They have been widely-used in many applications, including image segmentation, object boundary localisation, motion tracking, shape modelling, stereo matching and object reconstruction. In this paper, we investigate the potential of active contour models in extracting road edges from Mobile Laser Scanning (MLS) data. The categorisation of active contours based on their mathematical representation and implementation is discussed in detail. We discuss an integrated version in which active contour models are combined to overcome their limitations. We review various active contour-based methodologies, which have been developed to extract road features from LiDAR and digital imaging datasets. We present a case study in which an integrated version of active contour models is applied to extract road edges from MLS dataset. An accurate extraction of left and right edges from the tested road section validates the use of active contour models. The present study provides valuable insight into the potential of active contours for extracting roads from 3D LiDAR point cloud data

Microscopy image segmentation by active contour models

In this thesis a semi-automated cell analysis system is described through image processing. To achieve this, an image processing algorithm was studied in order to segment cells in a semi-automatic way. The main goal of this analysis is to increase the performance of cell image segmentation process, without affecting the results in a significant way. Even though, a totally manual system has the ability of producing the best results, it has the disadvantage of taking too long and being repetitive, when a large number of images need to be processed. An active contour algorithm was tested in a sequence of images taken by a microscope. This algorithm, more commonly known as snakes, allowed the user to define an initial region in which the cell was incorporated. Then, the algorithm would run several times, making the initial region contours to converge to the cell boundaries. With the final contour, it was possible to extract region properties and produce statistical data. This data allowed to say that this algorithm produces similar results to a purely manual system but at a faster rate. On the other hand, it is slower than a purely automatic way but it allows the user to adjust the contour, making it more versatile and tolerant to image variations

Surface reconstruction using active contour models

Variational methods have been frequently used for surface reconstruction and contour extraction (snakes). We present a surface reconstruction method where we assume the surface composed of two regions of different types of smoothness. One region of the surface models a "lake" (constant height region with uphill borders). It is surrounded by the other background region which is reconstructed using classic surface regularization. The boundary between the two regions, represented by a closed curve is determined with the help of an active contour model. Then the surface is reconstructed by minimizing the energy terms in each region. Minimizing a global energy defined on the couple of unknowns - boundary curve and surface - permits to introduce other forces on the curve. The surface reconstruction and contour extraction tasks are then made together. We have applied this model for segmenting a synthetic Digital Terrain Model (DTM) image which represents a noisy mountain and lake