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Automatic Classification (automatic + classification)

Distribution by Scientific Domains
Medical Sciences40%

Selected Abstracts

Automatic classification of protein crystallization images using a curve-tracking algorithm

JOURNAL OF APPLIED CRYSTALLOGRAPHY, Issue 2 2004
Marshall Bern
An algorithm for automatic classification of protein crystallization images acquired from a high-throughput vapor-diffusion system is described. The classifier uses edge detection followed by dynamic-programming curve tracking to determine the drop boundary; this technique optimizes a scoring function that incorporates roundness, smoothness and gradient intensity. The classifier focuses on the most promising region in the drop and computes a number of statistical features, including some derived from the Hough transform and from curve tracking. The five classes of images are `Empty', `Clear', `Precipitate', `Microcrystal Hit' and `Crystal'. On test data, the classifier gives about 12% false negatives (true crystals called `Empty', `Clear' or `Precipitate') and about 14% false positives (true clears or precipitates called `Crystal' or `Microcrystal Hit'). [source]

Automatic analysis of aqueous specimens for phytoplankton structure recognition and population estimation

MICROSCOPY RESEARCH AND TECHNIQUE, Issue 9 2006
Karsten Rodenacker
Abstract An automatic microscope image acquisition, evaluation, and recognition system was developed for the analysis of Utermöhl plankton chambers in terms of taxonomic algae recognition. The system called PLASA (Plankton Structure Analysis) comprises (1) fully automatic archiving (optical fixation) of aqueous specimens as digital bright field and fluorescence images, (2) phytoplankton analysis and recognition, and (3) training facilities for new taxa. It enables characterization of aqueous specimens by their populations. The system is described in detail with emphasis on image analytical aspects. Plankton chambers are scanned by sizable grids, divers objective(s), and up to four fluorescence spectral bands. Acquisition positions are focused and digitized by a TV camera and archived on disk. The image data sets are evaluated by a large set of quantitative features. Automatic classifications for a number of organisms are developed and embedded in the program. Interactive programs for the design of training sets were additionally implemented. A long-term sampling period of 23 weeks from two ponds at two different locations each was performed to generate a reliable data set for training and testing purposes. These data were used to present this system's results for phytoplankton structure characterization. PLASA represents an automatic system, comprising all steps from specimen processing to algae identification up to species level and quantification. Microsc. Res. Tech., 2006. © 2006 Wiley-Liss, Inc. [source]

Automatic classification of protein crystallization images using a curve-tracking algorithm

JOURNAL OF APPLIED CRYSTALLOGRAPHY, Issue 2 2004
Marshall Bern
An algorithm for automatic classification of protein crystallization images acquired from a high-throughput vapor-diffusion system is described. The classifier uses edge detection followed by dynamic-programming curve tracking to determine the drop boundary; this technique optimizes a scoring function that incorporates roundness, smoothness and gradient intensity. The classifier focuses on the most promising region in the drop and computes a number of statistical features, including some derived from the Hough transform and from curve tracking. The five classes of images are `Empty', `Clear', `Precipitate', `Microcrystal Hit' and `Crystal'. On test data, the classifier gives about 12% false negatives (true crystals called `Empty', `Clear' or `Precipitate') and about 14% false positives (true clears or precipitates called `Crystal' or `Microcrystal Hit'). [source]

The use of Artificial Neural Networks to classify primate vocalizations: a pilot study on black lemurs

AMERICAN JOURNAL OF PRIMATOLOGY, Issue 4 2010
Luca Pozzi
Abstract The identification of the vocal repertoire of a species represents a crucial prerequisite for a correct interpretation of animal behavior. Artificial Neural Networks (ANNs) have been widely used in behavioral sciences, and today are considered a valuable classification tool for reducing the level of subjectivity and allowing replicable results across different studies. However, to date, no studies have applied this tool to nonhuman primate vocalizations. Here, we apply for the first time ANNs, to discriminate the vocal repertoire in a primate species, Eulemur macaco macaco. We designed an automatic procedure to extract both spectral and temporal features from signals, and performed a comparative analysis between a supervised Multilayer Perceptron and two statistical approaches commonly used in primatology (Discriminant Function Analysis and Cluster Analysis), in order to explore pros and cons of these methods in bioacoustic classification. Our results show that ANNs were able to recognize all seven vocal categories previously described (92.5,95.6%) and perform better than either statistical analysis (76.1,88.4%). The results show that ANNs can provide an effective and robust method for automatic classification also in primates, suggesting that neural models can represent a valuable tool to contribute to a better understanding of primate vocal communication. The use of neural networks to identify primate vocalizations and the further development of this approach in studying primate communication are discussed. Am. J. Primatol. 72:337,348, 2010. © 2009 Wiley-Liss, Inc. [source]

Acquired loss of chromatic sensitivity

ACTA OPHTHALMOLOGICA, Issue 2009
J BARBUR
Purpose A range of ophthalmic and neurological conditions cause diminished visual performance, even when the subject is often unaware of any problems and the loss of vision remains undetected in conventional perimetry and visual acuity tests. The extent to which detection of acquired colour vision loss can revealed in subclinical cases and distinguished from congenital loss has been investigated. Methods Over 400 subjects with congenital and acquired colour vision loss have been examined using conventional colour screening methods. In addition, the loss of yellow / blue and red / green chromatic sensitivity has been quantified using the CAD test (http://www.caa.co.uk/docs/33/200904.pdf). Those investigated included subjects with diseases of the retina and / or the optic nerve as well as patients with selective damage to central visual pathways. Patients with various stages of glaucoma, photoreceptor dystrophies, diabetes, optic neuritis, age-related macular degeneration as well as tobacco and alcohol toxicity have been examined. Results Algorithms developed for analysis of colour vision loss and automatic classification of congenital and / or acquired colour deficiency will be described. In acquired deficiency, the loss of chromatic sensitivity tends to affect both the rg and the yb channels. Significant differential effects have, however, been observed in relation to stimulus size, retinal location and state of light adaptation. Conclusion The findings from these studies show that in the majority of these conditions, the loss of chromatic sensitivity is the most sensitive measure of early changes in diseases of the eye. [source]