Segmentation of color images based on the gravitational clustering concept

A new clustering algorithm derived from the Markovian model of the gravitational clustering concept is proposed that works in the RGB measurement space for color image. To enable the model to be applicable in image segmentation, the new algorithm imposes a clustering constraint at each clustering iteration to control and determine the formation of multiple clusters. Using such constraint to limit the attraction between clusters, a termination condition can be easily defined. The new clustering algorithm is evaluated objectively and subjectively on three different images against the K-means clustering algorithm, the recursive histogram clustering algorithm for color (also known as the multi-spectral thresholding), the Hedley-Yan algorithm, and the widely used seed-based region growing algorithm. From the evaluation, it is observed that the new algorithm exhibits the following characteristics: (1) its objective measurement figures are comparable with the best in this group of segmentation algorithms; (2) it generates smoother region boundaries; (3) the segmented boundaries align closely with the original boundaries; and (4) it forms a meaningful number of segmented regions. © 1998 Society of Photo-Optical Instrumentation Engineers.published_or_final_versio

Spatial Variability of Snow Chemistry of High Altitude Glaciers in the Peruvian Andes

Atmospheric contaminants become incorporated in glaciers through both wet and dry deposition. Some of this particulate matter can act as a source of contamination to glacial streams, leading to a concern for the chemical contamination to cause downstream toxicity to aquatic organisms and toxicity to people ingesting that water. Other portions of this particulate matter, including black carbon, can decrease the amount of light reflected off the snow, thereby contributing to increased rates of glacial melting. These issues are especially of concern to tropical glaciers, which are receding rapidly and are relied on heavily to provide drinking water in the dry season. A snow sampling campaign was conducted on the glaciers of seven mountains in the Cordillera Blanca mountain range in Peru during June-August, 2015 to determine concentrations of inorganic contaminants and black carbon in the upper layer of snow on high altitude glaciers (\u3e5000 m.a.s.l.). Elevation did not appear to be a factor in chemical concentrations, as there were no significant linear relationships with measured analytes and elevation, with the exception of Zn on one mountain sampled. Snow samples on two of the mountains had higher As and Pb concentrations than U.S. Environmental Protection Agency (USEPA) established water quality criteria for human health. Five metals (Al, Cd, Fe, Pb, and Zn) were found to exceed the USEPA aquatic life criteria in at least one sample. The highest concentrations of black carbon and metals were found closest to a local population center and lowest were found in areas furthest from anthropogenic influences. This study also provides supporting evidence that soil/dust is a contributing source of particulate matter but not the light absorbing fraction. An initial attempt at sourcing the particulate matter in these samples was made through an examination of analyte ratios, correlations, and principal components analysis. Multivariate analysis, including hierarchical clustering on principal components, could not explain categories based solely on concentrations of light absorbing particles or distance from the closest large city in the region. The sources of contaminants in the area appears to be complicated, and further studies would provide more insight into the source and spatial distribution of particulate matter on these tropical glaciers

Method and system for data clustering for very large databases

Multi-dimensional data contained in very large databases is efficiently and accurately clustered to determine patterns therein and extract useful information from such patterns. Conventional computer processors may be used which have limited memory capacity and conventional operating speed, allowing massive data sets to be processed in a reasonable time and with reasonable computer resources. The clustering process is organized using a clustering feature tree structure wherein each clustering feature comprises the number of data points in the cluster, the linear sum of the data points in the cluster, and the square sum of the data points in the cluster. A dense region of data points is treated collectively as a single cluster, and points in sparsely occupied regions can be treated as outliers and removed from the clustering feature tree. The clustering can be carried out continuously with new data points being received and processed, and with the clustering feature tree being restructured as necessary to accommodate the information from the newly received data points

Информационная технология обработки слабоконтрастных изображений на основе метода цифровой интерферометрии : монография

Предложена информационная технология, основанная на методе виртуальной цифровой интерферометрии, которая обеспечивает повышение чувствительности, разрешающей способности и достоверности процедур сегментации и анализа как обычных, так и многопараметровых (мультиспектральных) изображений в условиях неопределенности системы их формирования, а также местоположение и вид объекта потенциального интереса. Для специалистов в области информационных технологий, аспирантов и студентов старших курсов направления «Компьютерные науки»

Design and Analysis of Multispecies Toxicity Tests for Pesticide Registration

The community conditioning hypothesis describes ecological structures as historical, nonequilibrial, and by definition complex. Indeed, the historical nature of ecological structures is seen as the primary difference between single-species toxicity tests and multispecies test systems. Given the complex properties of ecological structures, multispecies toxicity tests need to be designed accordingly with appropriate data analysis tools. Care must be taken to ensure that each replicate shares an identical history, or divergence will rapidly occur. Attempting to realize homogeneity by linear cross inoculation or waiting for an equilibrium state to occur assumes properties that ecological structures do not have. Data analysis must also incorporate the dynamic and hyperdimensional nature of ecological structures. Univariate analysis of individual variables denies the fundamental character of ecological structures as complex systems. A variety of methods, such as correspondence analysis, nonmetric multidimensional scaling, and nonmetric clustering and association analysis, are available to search for patterns and to test their relationships to experimental treatments. Visualization techniques including Space–Time Worms and redundancy analysis are also critical in attempting to understand the dynamic nature of these structures. Reliance upon the traditional analysis methods, such as ANOVA and the estimation of LOECs (lowest observable effects concentrations) or NOECs (no observable effects concentrations), comparable to those of single-species toxicity tests, is to be blind to the unique and complex nature of multispecies toxicity tests. Fundamental design criteria for multispecies toxicity tests, data analysis, and interpretation are presented

Changes in Water Chemistry and Biological Communities Associated with Metal Mining in Streams in the North Cascades

Hard rock and placer mining have been occurring throughout the mountains in the northern portion of Washington State since the late-1800s. As a result, aquatic ecosystems in this region are susceptible to the physical, chemical and biological changes that result from mining activities. These alterations, which include changes in water chemistry, habitat modifications, and reduction or contamination of food sources, can adversely impact aquatic communities of periphyton, benthic macroinvertebrates and fish. To evaluate changes in water chemistry and biological communities in two regions with extensive mining histories, the Ruby Creek watershed and Upper Skagit River watershed, I analyzed metals in grab samples of surface water, on Stabilized Liquid Membrane Devices (SLMDs) which passively sample metals in surface waters over time, and in periphyton. Metals were present in the water and benthos, and site-specific and temporal differences in the kinds and quantities of metals were linked to locations of hard rock and placer mining activities. Metal concentrations in surface waters differed between sites upstream and downstream of mining depending on different times when mining was or was not occurring. Metal concentrations in surface waters at some sites in the Ruby Creek watershed were high enough to be capable of adversely affecting aquatic organisms over time. Metals that were present in streams were not always detected in grab samples, but their presence was confirmed by SLMDs and periphyton. Clustering analyses of both SLMDs and periphyton each distinguished two different groups of samples, samples collected downstream of placer mining (SLMDs) and samples collected downstream of hard rock mining (periphyton). The accumulation of metals in periphyton indicated these communities could be a concentrated source of toxic metals to primary consumers, such as small aquatic insects, and may pass to other aquatic organisms at higher trophic levels through dietary exposures

Geometric classification by stress polytopes. Performances and integration

We present in this paper a fast classification operator suitable for image processing, the performances of this operator as well as its implementation in the form of an ASIC. In image segmentation and classification in view of defect detection, it is often impossible to find a reduced set of pertinent characteristic parameters which allows to distinguish the classes. We propose herein a geometric classification method by stress polytop training which allows the use of a great number of parameters and ensures a high decision speed . The decision operator associated with the classification has been implemented in Standard Cell and Full Custom . Its ease of use, rapidity, and robustness in classification are the major qualities which enable it to compete with neural operators .Nous présentons dans cet article un opérateur de classification rapide adapté au traitement d'images, ses performances en classification, ainsi que son intégration dans un circuit ASIC. Pour effectuer une segmentation ou un classement d'images en vue de la détection de défauts, il est souvent impossible de trouver un nombre réduit de paramètres caractéristiques pertinents qui permettent de discriminer les classes. Nous proposons une méthode de classification géométrique par apprentissage de polytopes de contraintes, qui autorise l'utilisation d'un grand nombre de paramètres et assure une vitesse de décision élevée. L'opérateur de décision associé à cette classification a été intégré sous forme de circuit précaractérisé dont la simplicité de mise en œuvre, la rapidité et la robustesse en classification sont des qualités qui lui permettent de rivaliser avec les opérateurs neuronau

An exploratory statistical analysis of the ground water in the Abbotsford-Sumas aquifer

The Abbotsford-Sumas aquifer is a primarily unconfined aquifer, located in Whatcom County, WA, with a history of nitrate contamination. Whatcom County is a large producer of raspberries and contains numerous dairy farms. Both of these agricultural practices involve large quantities of nitrates being produced or used for fertilization. A two-year ground water monitoring program was conducted in 1997 and 1998 by Western Washington University in order to determine the spatial and temporal extent of the nitrate contamination. Possible trends in nitrate concentrations may be associated with ground water movement, chemical and biological nitrate reduction processes, seasonality and land use. Exploratory univariate, bivariate and multivariate statistical analyses were utilized to determine the dominant processes affecting nitrate concentrations in the study area. Nitrate concentrations in shallow wells were associated with local agricultural practices and nitrate concentrations in deeper wells were associated with agricultural practices occurring up-gradient in Canada. Differentiating land use based on nitrate concentrations was determined to be inconclusive. Denitrification was occurring in over half of the wells in the study area. Several types of nitrate concentration trends were observed: higher nitrate concentrations in the fall and winter due to nitrification in the spring and summer; higher nitrate concentrations in the spring and summer due to nitrogen inputs; a steady increase or decrease in nitrate concentrations; no detectable nitrate concentration. Multivariate statistical analyses confirmed that there was not one dominant process affecting nitrate concentrations in the Abbotsford-Sumas study area; therefore, nitrate concentration trends are due to a combination of processes

Cluster Analysis as a Means of Examining Topographically-mediated Bristlecone Pine (Pinus longaeva) Growth in the American Southwest

Tree-ring analysis can provide information about the surrounding environment of trees, as ring widths often reflect the variability of the factors that limit their growth. Great Basin bristlecone pine (Pinus longaeva D. K. Bailey) provides crucial tree-ring data to understand paleoclimate, but the growth signals can be difficult to interpret. The bristle- cone record could present a potentially confounding narrative because of its mixed growth signals; in many cases, not all trees at one site are limited by the same environmental variable. Trees that are sensitive to patterns in temperature tend to grow in the alpine upper treeline ecotone, and trees limited by moisture availability have the tendency to grow downslope. At four sites in the Great Basin region, USA, this study uses cluster analysis to find dual-signal patterns in tree growth, and uses topoclimate modeling to better understand bristlecone growth. I found two-cluster patterns at two of those sites; both of these sites included a cluster that correlated well with temperature data and one cluster that correlated with reconstructed drought data. Temperature-limited clusters contained trees growing in colder areas at higher elevations, and moisture-limited clusters contained trees at lower elevation in warmer areas. This study presents models to predict the primary limiting factor of an individual tree based on topoclimate variables in hopes of furthering understanding of mixed-signal growth patterns and improving the accuracy of climate reconstructions using bristlecone pine

Naturally occurring aqueous arsenic and seawater intrusion on Lummi Island, WA

Two different types of groundwater contamination may be present in the aquifers on northern Lummi Island, Washington: naturally occurring arsenic and seawater intrusion. Freshwater on northern Lummi Island is stored in bedrock and unconsolidated glacial sediments. The naturally occurring arsenic, sourced from an undetermined stratigraphic layer, varies spatially throughout the island. Additionally, seawater may be intruding into the groundwater supply, which is the primary source of drinking water for the residents of the island. The process of mobilization of the naturally occurring arsenic and the extent of the seawater intrusion has not been fully explored. The purpose of my study was to determine the geochemical, physical, and seasonal influences on concentrations of arsenic and major ions on Lummi Island. I collected water samples and made in situ measurements from wells distributed throughout Lummi Island for geochemical analysis. Statistical analysis was used to test for a relationship between arsenic concentrations and geochemical factors or season. The speciation of arsenic in the groundwater was determined by plotting pH and redox potential measurements on an arsenic species stability diagram. Whole-rock chemical analysis was used to investigate the bedrock source of the arsenic. The extent of the seawater intrusion was determined using major ion analysis, and the source of the ions was interpreted using Piper diagrams. The relationship between aquifers, major ions, and seasonality was explored using multivariate statistical analysis. Whole rock analysis indicated that the highest arsenic concentration was in the sample taken from the Chuckanut conglomerate. When Eh and pH field measurements were plotted on an arsenic stability diagram, arsenate was revealed as the dominant species in the groundwater. Speciation calculations in PHREEQC supported the conclusion that arsenate was the dominant species in most water samples. No wells indicated seawater intrusion and some plotted in the freshening region of the Piper diagram. Wells that plotted in the freshening area of the Piper diagram were more likely to have higher arsenic concentrations. Bivariate analysis, principal component analysis, non-metric clustering and Piper plots failed to show a difference in the measured variables between the April and August samples. A positive correlation was found between specific conductance, Na+, Cl- and total alkalinity and dissolved arsenic, and a negative correlation was found between Ca2+ and Mg2+ and dissolved arsenic. No correlation was observed between dissolved arsenic and Fe or Mn. Multivariate statistics indicated a correlation between the presence of major ions and the dissolved arsenic concentrations. The positive correlation between alkalinity and dissolved arsenic, negative correlations between Ca2+ and Mg2+ and dissolved arsenic, and no correlations with Fe or Mn is consistent with an arsenic release through a desorption process. The presence of dissolved carbonate and bicarbonate is indicative of a chemical weathering process, which could lead to arsenic desorption, and the charge on Ca2+ and Mg2+ ions can facilitate the adsorption and desorption of dissolved arsenic. Since the Chuckanut sandstone had the highest dissolved arsenic concentrations, a chemical weathering process is most likely occurring within this stratigraphic layer. No wells in this study exceeded the SMCL (Secondary Maximum Contaminant Level), nor did any wells experience a statistically significant fluctuation in chlorides between the April and August sampling seasons. When the major ions were plotted on a Piper diagram, all of the wells plotted in either the fresh or the freshening part of the diagram; none of the samples plotted in the intruding or intruded area. Because there was no evidence that the wells in my study were experiencing seawater intrusion, the salts must be released from another source. This relationship between major ions and dissolved arsenic was supported by the multivariate statistical tests principal component analysis and linear discriminant analysis. The principal component analysis successfully classified arsenic into high and low groups, and once trained with a subset of the data, the linear discriminant analysis divided arsenic into high or low categories. The relationship between the major ions and dissolved arsenic can be interpreted from a Piper diagram when the high dissolved arsenic concentrations ([As] \u3e0.07 mg/L) is color coded. These water samples all plotted in the freshening region of the Piper diagram. Because chlorides and dissolved arsenic were positively related, specific conductance, used as a proxy for chlorides, could be used as a rough indicator for arsenic