AndrewBangham: Created page with " [t] ''A textured image sieved to five scales using a $\mathcal{M}$ -sieve. Resulting Channel images are Bi-polar. Red is used to denote +ve granules and blue -ve gr..."

2013-02-13T09:34:06Z

Created page with " [t] ''A textured image sieved to five scales using a <math>\mathcal{M}</math>-sieve. Resulting Channel images are Bi-polar. Red is used to denote +ve granules and blue -ve gr..."

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[t] ''A textured image sieved to five scales using a <math>\mathcal{M}</math>-sieve. Resulting Channel images are Bi-polar. Red is used to denote +ve granules and blue -ve granules.'' fe

Our algorithm uses sieve operators to sieve each texture image to scales, <math>\left[s_{1}\ldots s_{N}\right]</math> where <math>\log_{10} s_{n}</math> are equispaced between 0 and <math>\log_{10}S_{max}</math>, <math>S_{max}</math> is the maximum chosen scale and <math>N</math> the number of sieved images. Tex-Mex features are formed from statistics derived from channel images. Noting that the setting <math>S_{max} = 30</math> removes all the texture from the images and setting <math>N = 5</math> results in five images sieved to scales <math>\left[1,2,5,13,30\right]</math>. Five channel images are formed from these sieved images at scales 0 to 1, 1 to 2, 2 to 5, 5 to 13 and 13 to 30. Figure [fe] shows some example sieved images and resulting channel images. The intensity of the granule, or channel, images as a function of scale is an indicator of the scale-distribution of the texture features.

[[Image:fo2pixdoc/treessc2|image]]<br /> [fig:treesal]

For image retrieval, stereo-matching and object recognition the current interest is in finding regions in the image that are likely to remain unaffected by noise, projective transformations, compression and lighting change. In a comprehensive set of trialsÂ a type of region known as Maximally Stable Extremal Regions (MSERs) were found to be the best performing. It turns out that MSERs are generated by a variant of the sieve algorithm known as open/close-sieves. It is therefore possible to parse a sieve tree and to generate

==== Stable Salient Contours ====

(SSCs) which are carefully selected nodes from the sieve tree that have all the stability and robustness properties associated with MSERs. Thus, as in FigureÂ [fig:treesal] the sieve tree generates stable regions â€œfor freeâ€�.

==== Colour sieves ====

The sieve has recently been extended into the color domainÂ via the use convex hulls to define color extrema which are then merged to their nearest neighbours, found using a Euclidean distance measure. Figure [colPepdecomp] shows an example color sieve decomposition of a sample image.

[h]

<table>
<tbody>
<tr class="odd">
<td align="center">[[Image:fo2pixdoc/colpepperorj|image]]</td>
<td align="center">[[Image:fo2pixdoc/colpepper30|image]]</td>
</tr>
<tr class="even">
<td align="center">Original</td>
<td align="center">30</td>
</tr>
<tr class="odd">
<td align="center">[[Image:fo2pixdoc/colpepper201|image]]</td>
<td align="center">[[Image:fo2pixdoc/colpepper640|image]]</td>
</tr>
<tr class="even">
<td align="center">201</td>
<td align="center">640</td>
</tr>
<tr class="odd">
<td align="center">[[Image:fo2pixdoc/colpepper2311|image]]</td>
<td align="center">[[Image:fo2pixdoc/colpepper4421|image]]</td>
</tr>
<tr class="even">
<td align="center">2311</td>
<td align="center">4421</td>
</tr>
</tbody>
</table>

[colPepdecomp]

Art test page - Revision history

AndrewBangham: Created page with " [t] ''A textured image sieved to five scales using a \mathcal{M}-sieve. Resulting Channel images are Bi-polar. Red is used to denote +ve granules and blue -ve gr..."

AndrewBangham: Created page with " [t] ''A textured image sieved to five scales using a $\mathcal{M}$ -sieve. Resulting Channel images are Bi-polar. Red is used to denote +ve granules and blue -ve gr..."