The Concave Contour Detection Based on GVFsnake

2010 ◽  
Vol 108-111 ◽  
pp. 1296-1301
Author(s):  
Jie Cao ◽  
Xiao Jun Liu ◽  
Zong Li Liu
Keyword(s):  
Active Contour ◽  
Field Experiments ◽  
Active Contour Model ◽  
Research Field ◽  
Contour Detection ◽  
Gradient Vector Flow ◽  
External Energy ◽  
Edge Information ◽  
Snake Model ◽  
Contour Model

Active contour model is an important research field in computer vision and many researchers studied the variational method in recent years. The traditional snake model is unable to converge to the concave area and it has a lower convergence. By improving the external energy, researchers introduced a gradient vector flow active contour model (GVFsnake). Several standard images are used to segmenting experiments, and the results show that GVF has obvious advantages compared with traditional snake model in the iteration number of force field. Experiments show that the method is faster and better to converge in the concave area. The edge information can be kept well and diffused more quickly.

Active Contour Model with Edge Flow Vector for Texture Segmentation

2015 ◽  
Vol 781 ◽  
pp. 511-514
Author(s):  
Tanunchai Boonnuk ◽  
Sanun Srisuk ◽  
Thanwa Sripramong
Keyword(s):  
Medical Imaging ◽  
Active Contour ◽  
Active Contour Model ◽  
Texture Segmentation ◽  
Gradient Vector Flow ◽  
Gradient Vector ◽  
Texture Image ◽  
Contour Model ◽  
Flow Vector ◽  
Edge Flow

In this paper, we propose effective method for texture segmentation using active contour model with edge flow vector. This technique was applied from previous active contour model that uses gradient vector flow as external force. It was observed that our method provided better results for texture segmentation while a traditional active contour model and active contour model with gradient vector flow were not capable to be used with texture image. Thus, texture image such as medical imaging can be identified using active contour model with edge flow vector.

EFFICIENT CONTOUR DETECTION BASED ON IMPROVED SNAKE MODEL

2004 ◽  
Vol 18 (02) ◽  
pp. 197-209 ◽  
Author(s):  
FRANK Y. SHIH ◽  
KAI ZHANG
Keyword(s):  
Force Field ◽  
Active Contour ◽  
Active Contour Model ◽  
Contour Detection ◽  
Initial Contour ◽  
Object Boundary ◽  
Edge Preserving ◽  
Snake Model ◽  
External Force Field ◽  
Gravitation Force

Active contour model, also called snake, adapts to edges in an image. A snake is defined as an energy minimizing spline – the snake's energy depends on its shape and location within the image. Problems associated with initialization and poor convergence to boundary concavities, however, have limited its utility. In this paper, we present a new external force field, named gravitation force field, for the snake model. We associate this force field with edge preserving smoothing to drive the snake for solving the problems. Our gravitation force field uses gradient values as particles to construct force field in the whole image. This force field will attract the active contour toward the edge boundary. The locations of the initial contour are very flexible, such that they can be very far away from the objects and can be inside, outside, or the mixture. The improved snake can converge toward the object boundary in a fast pace.

scholarly journals UNSUPERVISED AUTOMATIC BUILDING EXTRACTION USING ACTIVE CONTOUR MODEL ON UNREGISTERED OPTICAL IMAGERY AND AIRBORNE LIDAR DATA

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 181-188 ◽  
Author(s):  
T. H. Nguyen ◽  
S. Daniel ◽  
D. Guériot ◽  
C. Sintès ◽  
J.-M. Le Caillec
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Airborne Lidar ◽  
Training Data ◽  
Building Extraction ◽  
Snake Model ◽  
Urban Scenes ◽  
Contour Model ◽  
Optical Imagery ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> Automatic extraction of buildings in urban scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly with the emergence of LiDAR systems since mid-1990s. However, in reality, this task is still very challenging due to the complexity of building size and shape, as well as its surrounding environment. Active contour model, colloquially called snake model, which has been extensively used in many applications in computer vision and image processing, has also been applied to extract buildings from aerial/satellite imagery. Motivated by the limitations of existing snake models dedicated to the building extraction, this paper presents an unsupervised and automatic snake model to extract buildings using optical imagery and an unregistered airborne LiDAR dataset, without manual initial points or training data. The proposed method is shown to be capable of extracting buildings with varying color from complex environments, and yielding high overall accuracy.</p>

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