Traditional Snake Algorithm Research Articles

Improved Active Contour Snake Model for Hollow Filter Bar Roundness Inspection

Cigarette filter rods are a kind of easily deformed and irregularly shaped parts, and the quality of cigarette filter rods is often measured by the filter rod roundness index, although they are affected by factors such as environment and humidity, which can affect the detection of filter rod roundness and show blurred images. For the detection of blurred filter rod image roundness, an improved active contour snake model algorithm is proposed. The experimental results show that in the analysis of the algorithm iteration number on the model performance, the model performance gradually leveled off at the iteration number of 200. Comparing the PR of the improved algorithm with the traditional snake algorithm with the balloon force snake algorithm, the accuracy and recall rates obtained were 0.803 and 0.798, respectively, which were greater than the other two algorithms; the error of the value of the residual under the improved snake algorithm was always smaller than the other algorithms, which indicated that the convergence effect of the improved snake algorithm on the error of filter bar roundness was better than the other experimental algorithms. The abovementioned results indicate that the improved snake algorithm has high feasibility and effectiveness in the application of filter bar roundness error detection and has a significant value.

Adventitia segmentation in intravascular ultrasound images based on improved Snake algorithm

Segmentation of coronary artery intravascular ultrasound (IVUS) images is the first step in assessing blood vessel morphology and detecting possible atherosclerotic lesions. To overcome the disadvantage of manual acquisition of initial contour in traditional Snake algorithm and construction of external force only from gradient information, an improved Snake algorithm is proposed for automatic outer membrane segmentation in intravascular ultrasound images. Firstly, we design a basic segmentation algorithm to automatically extract the approximate contour of the first frame, and this is iterated as the initial contour of the Snake algorithm. Next, the extended structure tensor is combined with the Snake algorithm to make it part of the external force of the Snake algorithm, and then the curve is evolved to complete the segmentation. To verify the effectiveness of the proposed method, this paper not only shows the experimental results qualitatively, but also quantitatively measures the RMSE and RDD indices. Comparative experiments show that the proposed method not only has higher accuracy, but also has better timeliness.

Research and application of Improved snake algorithm in Boundary detection of Core CT image

With the continuous refinement of oilfield development, computed tomography (CT) scanning technology has gradually become an important means to study the core microstructure, through the analysis of core CT image to obtain the core structure information. Active contour model (snake algorithm model) has been widely used in medical image segmentation. Considering the great similarity between core CT image and medical CT image of petroleum geology department, this paper attempts to improve the traditional snake algorithm model. The experimental results show that the improved algorithm has achieved good results in the boundary extraction of core CT sequence image.

An Efficient Converging Snake Algorithm for Locating Object Boundaries

Active contour are now established as a technique for extracting salient contours from an image. A snake is an active contour for representing object contour. Traditional snake algorithms are often used to represent the contour of a single object. A different contour search algorithm is presented in this paper that provides an efficient convergence to the object contours than both the kass et al and greedy snake algorithm (GSA).Our proposed algorithm provides a straightforward approach for snake contour rapid splitting and connection, which usually cannot be gracefully handle by traditional snakes. This algorithm compares with other two conventional approaches is faster according to needed execution time. This paper tells us which one is better by comparing each other. The experimental results of various test sequence images with a single object shown good performance, which proved that the proposed algorithm is faster among those. DOI: http://dx.doi.org/10.11591/telkomnika.v14i1.7467

Automatic Construction of 3-D Building Model From Airborne LIDAR Data Through 2-D Snake Algorithm

The snake algorithm has been proposed to solve many remote sensing and computer vision problems such as object segmentation, surface reconstruction, and object tracking. This paper introduces a framework for 3-D building model construction from LIDAR data based on the snake algorithm. It consists of nonterrain object identification, building and tree separation, building topology extraction, and adjustment by the snake algorithm. The challenging task in applying the snake algorithm to building topology adjustment is to find the global minima of energy functions derived for 2-D building topology. The traditional snake algorithm uses dynamic programming for computing the global minima of energy functions which is limited to snake problems with 1-D topology (i.e., a contour) and cannot handle problems with 2-D topology. In this paper, we have extended the dynamic programming method to address the snake problems with a 2-D planar topology using a novel graph reduction technique. Given a planar snake, a set of reduction operations is defined and used to simplify the graph of the planar snake into a set of isolated vertices while retaining the minimal energy of the graph. Another challenging task for 3-D building model reconstruction is how to enforce different kinds of geometric constraints during building topology refinement. This framework proposed two energy functions, deviation and direction energy functions, to enforce multiple geometric constraints on 2-D topology refinement naturally and efficiently. To examine the effectiveness of the framework, the framework has been applied on different data sets to construct 3-D building models from airborne LIDAR data. The results demonstrate that the proposed snake algorithm successfully found the global optima in polynomial time for all of the building topologies and generated satisfactory 3-D models for most of the buildings in the study areas.

A Fast Snake Algorithm for Tracking Multiple Objects

A Snake is an active contour for representing object contours. Traditional snake algorithms are often used to represent the contour of a single object. However, if there is more than one object in the image, the snake model must be adaptive to determine the corresponding contour of each object. Also, the previous initialized snake contours risk getting the wrong results when tracking multiple objects in successive frames due to the weak topology changes. To overcome this problem, in this paper, we present a new snake method for efficiently tracking contours of multiple objects. Our proposed algorithm can provide a straightforward approach for snake contour rapid splitting and connection, which usually cannot be gracefully handled by traditional snakes. Experimental results of various test sequence images with multiple objects have shown good performance, which proves that the proposed method is both effective and accurate.

Improved Snake algorithm for complex target’s boundary detection

The traditional Snake algorithm cannot effectively detect the object edge of an image with non-convex shapes or low SNR. This paper studies the characteristics of this type of image with complex shape target or noise and presents an improved Snake algorithm. The traditional Snake function model and operation strategy are improved by increasing new control energy functions, and the influencing weight of these energy factors is discussed. At the same time, a dynamic arrangement for the Snake points is used to adapt different target shapes. The simulation results indicate that the new Snake model greatly decreases the dependence on the Snake point’s initial position and effectively overcomes noise influence. This method enhances the Snake algorithm’s ability of detecting object edge.