Algorithm for Handedness Detection of Fiddler Crabs

Members of the genus Uca are generally known as Fiddler crabs for the overgrown claw of the males. The following algorithm detects a male fiddler crabs handedness from a photograph. The algorithm was implemented using Matlab R2009a.

Generation of Mountain Ranges by Modifying a Controlled Terrain Generation Approach

A modification and extension of the parametrically controlled terrain generation approach produces mountain ranges of predefined shape with peaks at specified coordinates.



Like the original algorithm, the parameters can be tweaked to generate craters instead of peaks and other interesting geographical features.



The work was published in ICCIT 2008, Khulna bearing title Generation of Mountain Ranges by Modifying a Controlled Terrain Generation Approach

Automatic Species ID : Lepidochelys olivacea

The automatic species ID procedure I'm working on, follows these steps of image manipulation and decision making to detect Lepidochelys olivacea from a photo. For demonstration I'm using an image contributed by M.A. Hannan.

1. Convert RGB image to Lab color space
2. Enhance 'b' channel of the image


3. Complement 'b' channel of the image


4. Threshold complemented 'b' channel of the image with a predefined constant, 210 for this image


5. Dilate the resulting image


6. Fill the resulting image


7. Erode the resulting image


8. Mark the blob with maximum area


9. If area of the marked blob is not less than 30% of the image size then declare detection of LO

Automatic Species ID : Chelonia mydas

The automatic species ID procedure I'm working on goes through the following steps of image manipulation and decision making to detect Chelonia mydas from a photo. For demonstration I'm using an image contributed by Rachel Ruzgis to the image database of www.seaturtle.org

1. Convert RGB image to Lab color space

2. Threshold 'a' channel of the image with a predefined constant, 100 for this image

3. Fill holes

4. Erode image
5. Mark the blob with maximum area

6. If area of the marked blob is not less than 20% of the image size then declare detection of CM

Parametrically controlled terrain generation

The intent was to produce a single mountain while ensuring as much control as possible over the resulting terrain through algorithmic parameters. I ended up with this algorithm that allows you to specify desired coordinate of the mountain, height of the mountain peak and spread of the base region.

This algorithm is inspired by my previous work, the RMP algorithm and the famous Fault line algorithm by R. Krten. The algorithm takes three parameters as input. The number of fault lines l, probing depth r and number of iterations to perform n. The relationship between these parameters and physical properties of the mountain is a little complex. Height of the peak is directly proportional to n, spread of the mountain base is function of l,r and steepness of the mountain is inversly proportional to r.

These images bellow are result of tweaking with n parameter, notice the difference in height.



With a little modification introduced, the same procedure can produce craters and other unusual artifacts as well, as can be seen in these images.



The work was published in GRAPHITE 2007 bearing title Parametrically Controlled Terrain Generation.

Artificial Terrain Generation : RMP

These are results of Repeated Magnification and Probing algorithm for generating artificial terrain. The algorithm itself is pretty basic, and the results are pretty much basic height maps. But these can be used as base maps for erosion algorithms to produce nice realistic results.





The work was published as,

Repeated Magnification : a New Approach to Generating Artificial Mountain like Terrain
4th International Conference on Computer Graphics and Interactive Techniques in Australasia and Southeast Asia ( GRAPHITE 2006 ) Kuala Lumpur, Malaysia