Abundance and biomass of phytoplankton in the western part of the Black Sea during the R/V Akademik cruise Ak082001 in August 2001

The dataset is composed of 41 samples from 10 stations. The phytoplankton samples were collected by 5l Niskin bottles attached to the CTD system. The sampling depths were selected according to the CTD profile and the in situ fluorometer readings: surface, temperature, salinity and fluorescence gradients and 1 m above the bottom. At some stations phytoplankton net samples (20 µm mesh-size) were collected to assist species biodiversity examination. The samples (1l sea water) were preserved in 4% buffered to pH 8-8.2 with disodiumtetraborate formaldehyde solution and stored in plastic containers. On board at each station few live samples were qualitatively examined under microscope for preliminary analysis of taxonomic composition and dominant species. The taxon-specific phytoplankton abundance samples were concentrated down to 50 cm**3 by slow decantation after storage for 20 days in a cool and dark place. The species identification was done under light microscope OLIMPUS-BS41 connected to a video-interactive image analysis system at magnification of the ocular 10X and objective - 40X. A Sedgwick-Rafter camera (1ml) was used for counting. 400 specimen were counted for each sample, while rare and large species were checked in the whole sample (Manual of phytoplankton, 2005). Species identification was mainly after Carmelo T. (1997) and Fukuyo, Y. (2000). Total phytoplankton abundance was calculated as sum of taxon-specific abundances. Total phytoplankton biomass was calculated as sum of taxon-specific biomasses. The cell biovolume was determined based on morpho-metric measurement of phytoplankton units and the corresponding geometric shapes as described in detail in (Edier, 1979).

Abundance and biomass of phytoplankton in the western part of the Black Sea during Branimir Ormanov cruise BO092000 in September 2000

The samples were concentrated down to 50 cm**3 by slow decantation after storage for 20 days in a cool and dark place. The species identification was done under light microscope OLIMPUS-BS41 connected to a video-interactive image analysis system at magnification of the ocular 10X and objective - 40X. A Sedgwick-Rafter camera (1ml) was used for counting. 400 specimen were counted for each sample, while rare and large species were checked in the whole sample (Manual of phytoplankton, 2005). Species identification was mainly after Carmelo T. (1997) and Fukuyo, Y. (2000). Taxon-specific phytoplankton abundance and biomass were analysed by Moncheva S., B. Parr, 2005. Manual for Phytoplankton Sampling and Analysis in the Black Sea. The cell biovolume was determined based on morpho-metric measurement of phytoplankton units and the corresponding geometric shapes as described in detail in (Edier, 1979).

Aplicación del análisis multi-espectral para el reconocimiento automatizado de menas metálicas

Traditional identification of ore minerals with reflected light microscopy relies heavily on the experience of the observer. Qualified observers have become a rarity, as ore microscopy is often neglected in today’s university training, but since it furnishes necessary and inexpensive information, innovative alternatives are needed, especially for quantification. Many of the diagnostic optical properties of ores defy quantification, but recent developments in electronics and optics allow new insights into the reflectance and colour properties of ores. Preliminary results for the development of an expert system aimed at the automatic identification of ores based on their reflectance properties are presented. The discriminatory capacity of the system is enhanced by near IR reflectance measures, while UV filters tested to date are unreliable. Interaction with image analysis software through a wholly automated microscope, to furnish quantitative and morphological information for geometallurgy, relies on automated identification of the ores based on the measured spectra. This methodology increases enormously the performance of the microscopist; nevertheless supervision by an expert is always needed.

Level Set Segmentation with Shape and Appearance Models Using Affine Moment Descriptors

We propose a level set based variational approach that incorporates shape priors into edge-based and region-based models. The evolution of the active contour depends on local and global information. It has been implemented using an efficient narrow band technique. For each boundary pixel we calculate its dynamic according to its gray level, the neighborhood and geometric properties established by training shapes. We also propose a criterion for shape aligning based on affine transformation using an image normalization procedure. Finally, we illustrate the benefits of the our approach on the liver segmentation from CT images.

Análisis de imágenes basado en correlación de imágenes

El objetivo principal del proyecto es la realización de una aplicación en el programa MATLAB. En primer lugar, realizaremos un estudio teórico relativo al tema de nuestro proyecto. En nuestro caso como el tema es Imagen y Televisión, explicaremos de forma teórica la información principal acerca del Tratamiento Digital de la Imagen. Una vez conocida las técnicas principales utilizadas en el tratamiento digital, realizaremos un estudio exhaustivo en las técnicas actuales que existen acerca del análisis de imágenes. Daremos una breve explicación mostrando en qué consiste esta técnica, los diferentes pasos que se llevan a cabo en una imagen para su análisis, explicando brevemente cada unos de ellos y enumerando algunas técnicas para la realización de cada una de ellas. Tras esta primera parte, nos centraremos en las técnicas de correlación de imágenes (DIC). Explicaremos como han surgido estas técnicas, cual son sus principales conceptos, sus inicios y las ventajas e inconvenientes que tienen. Dentro de las diferentes técnicas de correlación de imágenes, explicaremos de forma detallada la correspondencia por áreas, ya que es la técnica que vamos a utilizar para la realización del proyecto. Explicaremos en qué consiste, y desarrollaremos teóricamente cual son los pasos que se deben realizar en las imágenes para realizar esta técnica. Explicaremos cual es su terminología, y cuáles son los posibles defectos que puede tener esta técnica. Finalmente, una vez estudiada la teoría, realizaremos una sencilla aplicación que nos permita evaluar y encontrar las diferencias en una secuencia de imágenes. El programa utilizado para este proyecto es MATLAB, que es un programa matemático, utilizado enormemente en el ámbito de la ingeniería. Mediante esta aplicación obtendremos dos figuras, una de ellas donde veremos los vectores de movimiento que existen entre las dos imágenes y la segunda, donde obtendremos el factor de correlación que hay entre las dos imágenes. ABSTRACT OF MY PROJECT The main objective of the project is the development of an application in MATLAB program. Firstly carry out a theoretical study on the topic of our project. In our case as the theme is Picture and Television, we explain the main information about Digital Image Processing. Once known the main techniques used in digital images, we will make a study on current techniques that exist about image analysis. We will give a brief explanation showing what this technique is, the different steps that are performed on an image for analysis, briefly explaining each of them and listing some techniques for performing each. After this first part, we will focus on the techniques of image correlation (DIC). We explain how these techniques have emerged, which are the main concepts, the beginning and the advantages and disadvantages they have. There are different image correlation techniques. We will explain in detail the correspondence areas, as it is the technique that we will use for the project. Explain what it is, which is theoretically and we develop steps that must be performed on the images for this technique. We explain what their terminology is, and what are the possible defects that may have this technique. Finally, having explored the theory images, we will make a simple application that allows us to evaluate and find differences in a sequence of images. The program used for this project is MATLAB, a mathematical program, widely used in the field of engineering. Using this application will get two figures, one where we will see the motion vectors between the two images and the second where we get the correlation factor between the two images.