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- All Subjects: Imaging
- Creators: Barber, Anne Christine
Description
The Arizona State University Herbarium began in 1896 when Professor Fredrick Irish collected the first recorded Arizona specimen for what was then called the Tempe Normal School - a Parkinsonia microphylla. Since then, the collection has grown to approximately 400,000 specimens of vascular plants and lichens. The most recent project includes the digitization - both the imaging and databasing - of approximately 55,000 vascular plant specimens from Latin America. To accomplish this efficiently, possibilities in non-traditional methods, including both new and existing technologies, were explored. SALIX (semi-automatic label information extraction) was developed as the central tool to handle automatic parsing, along with BarcodeRenamer (BCR) to automate image file renaming by barcode. These two developments, combined with existing technologies, make up the SALIX Method. The SALIX Method provides a way to digitize herbarium specimens more efficiently than the traditional approach of entering data solely through keystroking. Using digital imaging, optical character recognition, and automatic parsing, I found that the SALIX Method processes data at an average rate that is 30% faster than typing. Data entry speed is dependent on user proficiency, label quality, and to a lesser degree, label length. This method is used to capture full specimen records, including close-up images where applicable. Access to biodiversity data is limited by the time and resources required to digitize, but I have found that it is possible to do so at a rate that is faster than typing. Finally, I experiment with the use of digital field guides in advancing access to biodiversity data, to stimulate public engagement in natural history collections.
ContributorsBarber, Anne Christine (Author) / Landrum, Leslie R. (Thesis advisor) / Wojciechowski, Martin F. (Thesis advisor) / Gilbert, Edward (Committee member) / Lafferty, Daryl (Committee member) / Arizona State University (Publisher)
Created2012
Description
The detection and characterization of transients in signals is important in many wide-ranging applications from computer vision to audio processing. Edge detection on images is typically realized using small, local, discrete convolution kernels, but this is not possible when samples are measured directly in the frequency domain. The concentration factor edge detection method was therefore developed to realize an edge detector directly from spectral data. This thesis explores the possibilities of detecting edges from the phase of the spectral data, that is, without the magnitude of the sampled spectral data. Prior work has demonstrated that the spectral phase contains particularly important information about underlying features in a signal. Furthermore, the concentration factor method yields some insight into the detection of edges in spectral phase data. An iterative design approach was taken to realize an edge detector using only the spectral phase data, also allowing for the design of an edge detector when phase data are intermittent or corrupted. Problem formulations showing the power of the design approach are given throughout. A post-processing scheme relying on the difference of multiple edge approximations yields a strong edge detector which is shown to be resilient under noisy, intermittent phase data. Lastly, a thresholding technique is applied to give an explicit enhanced edge detector ready to be used. Examples throughout are demonstrate both on signals and images.
ContributorsReynolds, Alexander Bryce (Author) / Gelb, Anne (Thesis director) / Cochran, Douglas (Committee member) / Viswanathan, Adityavikram (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05