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Code Listing by Su Dongcai

Code 1-10 of 11   Pages: Go to  1  2  Next >>  page  

An implementation of [1], it works in follows:
1. Compute the Jmap.
2. Compute the valleys of Jmap.
3. Valleys growing as the oversegmentation.
4. Region merging.
Example usage:
%labels = colorImgSeg('imagessflowg.jpg', 12, 5, 10,0.35, 0.02, 0.88);
To run the code, you have to firstly generate the necessary dlls in
"local_min", "Jmap", "seededRegionGrowing" and...

%the function work in 4 steps:
%1. compute the inverse eucilidean distance trasform ("inv_distance") of "bw"
%2. compute the ridges(watersheds) of "inv_distance" by watershed algorithm
%3. compute the...

This file is a fast computation of Jmap, which introduce in [1]. Jmap is an edge indicator which insensitive to noise and texture.
sample usage:
mex scale_sum.cpp
2. read an image:
img = imread('tiger.jpg');

To duplicate results of the screenshot, please run:

This script is an implementation of Reference [1], including 2 parts:
1. 1D signal denoising (page 9~11)
2. 1D random process(RP) classification (page 12)

This file adapt a state of art structure 'disjoint set' to label objects in an binary
2D image. Which have similar effect as the matlab function 'bwlabel' in case of 8-connected
%please run the following commands to...

This is an implementation of [1]. The result is slightly different from website[1], because we apply adjacent neighborhood rather than K nearest neighborhood.

The following is an example usage:

%complie:(only once)

Counting the number of nuts as shown in the photo:
count 3 by 3, reminder: 2
count 5 by 5, reminder: 3
count 7 by 7, reminder: 2
then the requested total number of nuts X can be calculated as follow command:

Let r_scale be the neighborhood radius scale, a brute search method would require N*r_scale to detect the valleys with neighborhood size of r_scale in 1d signal or N*r_scale*r_scale complexity in 2d image. Which is too slow in large data size. In...

This file implement the modified hausdorf distance[2], and apply it to solve a simple application--tile images classification with shape variance. See the 'description.pdf' for more details.
[1] D. P. Huttenlocher, G. A. Klanderman, and W....

Implementation of the level set method proposed in[1](ACWE) while add the regularity term[2] to avoid re-initialization.
The core function to implement ACWE is 'acwe.m',while 'demo_acwe.m' is for demonstration purpose.