Active Snake (level Set model)

Frontier: Recently, due to the experimental reasons of large papers, several snake methods need to be collated to compare the effect of road extraction. So this evening, some of the lbf snake code in the computer is defined as a classification. and give the effect. For comparison.

1. Effect of the original lbf snake method

The original LBF algorithm is implemented as follows;

The Code of the experiment, Download Link. Then locate the directory in the network disk and locate the file shown.

However, the initial test is not available because the source files of the LSE program cannot be compiled and the source files cannot be found. The reference cited in this code is [1]. If you need more detailed study, please direct Baidu to download the academic.

Here are some instructions from the original author in the Code:

% This Matlab code demomstrates a improved algorithm based on the local binary fitting (LBF) model% in chunming Li et al '  S paper:% "Implicit Active contours driven by Local Binary Fitting Energy" in Proceedings of CVPR ' 07%% author:chunming Li, All rights reserved% e-mail: [email protected]% url:http://vuiis.vanderbilt.edu/~licm/% Http://www.engr. uconn.edu/~cmli/%% notes:% 1. Some parameters is set to default values for the demos. They need to be% modified for different types of images. % 2. The current version of does not work for images with multiple junctions, due to its two-phase% formulation (i.e. using Only one level set function). For example, an image have 3 objects/regions,% and each object/region are directly contiguous to all the other Jects/regions.    This code would be is% extended to multiphase on the future version, which'll be is available at the author ' s webpage.% 3. The image intensities need to Be rescaled to the range of [0, 255], if the intensities is much lower% or much higher than 255. Alternatively, you can change the parameters lambda1 and Lambda2, and Nu (the% coefficient of lenght term) Accordin Gly.

Algorithmic effects of the 2008 article

Original image

Experimental results of the test (reference [2])

The Code of the experiment, Download Link. Then find this directory in the network disk.

With some of the original author's own words, the following

% This Matlab file demomstrates A level set method in Chunming Li et al ' s paper%    "minimization of region-scalable Fitt  ING Energy for image segmentation ",%    IEEE Trans. Image Processing, vol. pp.1940-1949, 2008.% author:chunming Li, All rights reserved% e-mail:li_chunming@hotmail.com% URL:  http://www.engr.uconn.edu/~cmli/%% Note 1:the origin  Al model (LBF) with a small scale parameter sigma, such as Sigma = 3, was sensitive to% the initialization of the level         Set function. Appropriate initial level set functions is given in% this         code for different test images.% Note 2:there is several Ways to improve the original LBF model to make it robust to initialization.% one of the improved LBF algorithms are         imp Lemented by the code in the following link:%                Http://www.engr.uconn.edu/~cmli/code/LBF_v0.1.rar

2 CV Model

The program lacks the appropriate documentation and only finds the following in the demo program. This code should be the implementation of this article. References [3].

%   Matlab code implementing Chan-vese model in the paper ' Active contours without Edges ' percent this   method works well fo R bimodal images, for example the image ' Three.bmp '

When initializing, you need to draw a line. The results of the operation are as follows:

Program code also at the first mention of the download connection, Download link, find this folder

Reference documents

[1] Li C, Kao c Y, Gore J C, et al implicit Active contours driven by Local Binary Fitting energy[c]//IEEE Conferen Ce on computer Vision and Pattern recognition. IEEE Computer Society, 2007:1-7.

[2] chunming L, Chiu-yen K, Gore J C, et al minimization of region-scalable fitting energy for image segmentation. [J]. IEEE Transactions on Image processing A Publication of the IEEE Signal Processing Society, 2008, 17 (10): 1940-1949.

[3] Chan T F, Vese L A. Active contours without edges. [J]. IEEE Transactions on Image processing A Publication of the IEEE Signal Processing Society, 2001, 10 (2): 266-277.

Active Snake (level Set model)