Difference between revisions of "Team:Cambridge-JIC/MicroMaps"
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<center><p><i><b>Figure 1</b>: First successful stitching of two images (Nigerian liane). <b>Figure 2</b>: Stitching implemented on macroscopic images of Marchantia polymorpha as part of our <a href="//2015.igem.org/Team:Cambridge-JIC/Stretch_Goals" class="blue">Stretch Goals</a>. Note the accuracy of the stitching. <b>Figure 3</b>: Pretend stitching (performed manually) - shows how MicroMaps is ultimately intended to work.</i></p></center> | <center><p><i><b>Figure 1</b>: First successful stitching of two images (Nigerian liane). <b>Figure 2</b>: Stitching implemented on macroscopic images of Marchantia polymorpha as part of our <a href="//2015.igem.org/Team:Cambridge-JIC/Stretch_Goals" class="blue">Stretch Goals</a>. Note the accuracy of the stitching. <b>Figure 3</b>: Pretend stitching (performed manually) - shows how MicroMaps is ultimately intended to work.</i></p></center> | ||
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<p><b>How it works:</b> More concretely, MicroMaps keeps a collection of images it has taken along with the corresponding expected physical coordinates. MicroMaps will request small regions (tiles) of the slide one-by-one to fill up its field of view. When a tile is requested the software will look through its collection to see if it has already captured that region, and will join any seams it finds if multiple images match that tile. If no images match that tile, it will take a series of overlapping images between a nearby (in terms of expected coordinates) image and the desired tile. For each image, it will use the stitching algorithm to determine accurate coordinates representing the image and compare them to the expected coordinates. This is essential to correct for hardware noise and inaccuracies, and will allow a seamless image to be constructed from these small tiles. The accuracy obtained, combined with calibration data, will then allow for precise measurements to be made. The accurate positioning information will also allow pins to be dropped so interesting features can be returned to later.</p> | <p><b>How it works:</b> More concretely, MicroMaps keeps a collection of images it has taken along with the corresponding expected physical coordinates. MicroMaps will request small regions (tiles) of the slide one-by-one to fill up its field of view. When a tile is requested the software will look through its collection to see if it has already captured that region, and will join any seams it finds if multiple images match that tile. If no images match that tile, it will take a series of overlapping images between a nearby (in terms of expected coordinates) image and the desired tile. For each image, it will use the stitching algorithm to determine accurate coordinates representing the image and compare them to the expected coordinates. This is essential to correct for hardware noise and inaccuracies, and will allow a seamless image to be constructed from these small tiles. The accuracy obtained, combined with calibration data, will then allow for precise measurements to be made. The accurate positioning information will also allow pins to be dropped so interesting features can be returned to later.</p> | ||
Revision as of 12:01, 18 September 2015