Abstract: The invention relates to methods and data processing systems for the automatic identification, processing, interpretation and evaluation of objects in the form of digital data, especially unknown objects. Said methods and systems are characterised in that objects which cannot be associated with any known model are entered into a case database as unknown objects. They are then available for automatic interpretation and evaluation by means of a similarity-based method. Said unknown objects can lead to new models. The model database is thereby continuously enlarged, existing models refined, and new models learned. The model data-base can be organised evenly or hierarchically in statistical models representing higher classes and lower classes.

Abstract: In a method for automatic substance determination based on spectroscopic examinations of unknown substances in comparison to known test substances, digital data sets are determined by a delta modulator from spectra of known test substances and saved as prototypes in a database. Digital data sets of the unknown substances are determined from spectra by the delta modulator and are sequentially compared with the digital data sets of the prototypes of the database by a comparator. For a positive result of comparing, the unknown substances are allocated to a respective prototype as a determined case in the database. For a negative result of comparing, the unknown substances are allocated to undetermined cases of the database. The delta modulator, the database, the comparator, and the allocator are components of a data processing system and are linked and interconnected in accordance with the sequence of the method steps.

Abstract: A device for detecting dynamic processes of cell samples has a camera and a data processing system with a first module recognizing the cells of a first image, a second module allocating an identity to the cells, a third module allocating a frame to cells of the first image with one cell in the frame. First, second and third modules are serially connected. A fourth module recognizes cells within frames and allocates identities of cells of the subsequent image and determines similarity of cells relative to cells detected in predecessor images; cells most similar to predecessor cells are assigned as successors; newly added cells are assigned new identities. A fifth module allocates frames for cells of the subsequent image with one cell in the frame. Fourth and fifth modules are a loop for images following the first image. Data memory and data display for images with recognized cells are provided.

Abstract: The invention relates to methods for acquiring shapes from images with representations of HEp-2 cell sections in the form of objects and for learning abstract shape models from representations of HEp-2 cell sections for a case database for a case-based recognition of HEp-2 cells in digital images. The invention also relates to methods for acquiring shapes from images with representations of HEp-2 cell sections in the form of cases and for the case-based recognition of HEp-2 cells in the form of objects in digital images, to computer program products having a program code for carrying out these methods, to computer program products on machine-readable carriers for carrying out these methods, and to digital storage media that can interact with a programmable computer system whereby carrying out these methods.

Abstract: Image segmentation is realized from the actual image based on image features and a most similar case. The characteristic of the actual image is compared to characteristics of images of a case database. The most similar image in the case database is determined by a similarity quantity, and the parameters correlated with the most similar case are the basis for image segmentation. The actual image is segmented with these parameters, preferably by watershed transformation. The segmented image is evaluated and according to the evaluation in the case database as a case with the parameters of the image segmentation device and the characteristics of the image is saved as a new case in the case database. The model creation is realized by incremental addition of new cases, by a refinement based on generalization of the cases of the case database, by learning similarity, or by at least one combination thereof.

Abstract: For automatic identification of microorganisms collected on a carrier, a color image of the carrier surface with collected microorganisms is recorded and digitalized. The digitalized image is converted into a grayscale image and optionally converted subsequently into a silhouette image. When microorganisms are present, an image is produced with full-surface labeled objects of a first grayscale and a background of a second grayscale. Objects are identified in the grayscale and/or silhouette image by a model-based comparative method. Contours of the objects are marked in the color or grayscale image. Features of the objects in the color image and/or grayscale image are determined. The objects are classified based on the features. The classified objects are indicated and/or saved as species, name and/or code. Non-classified objects are indicated and/or saved as color, grayscale and/or silhouette image. Non-classified objects are subsequently discarded or added as a new case to the classification system.

Abstract: For automatic detection and interpretation of the iris structure for determining a condition of a person, two-dimensional recording and digitization of an eyeball with iris is realized by a recording device connected to a data processing system; selection of the iris; generation of the topography of the iris by dividing the image of the iris into images of circular ring sections; determination of features for the circular ring sections and/or of objects in the images of the circular ring sections by combining image pixels of same color or intensity to individual objects by an object isolation method; feature determination of the objects determined by the object isolation method; comparison of the determined features of the objects with features saved as classification knowledge in the data processing system; and correlation of the determined objects to body regions in accordance with the topography of the iris are performed.

Abstract: The invention relates to methods for acquiring shapes from images with representations of HEp-2 cell sections in the form of objects and for learning abstract shape models from representations of HEp-2 cell sections for a case database for a case-based recognition of HEp-2 cells in digital images. The invention also relates to methods for acquiring shapes from images with representations of HEp-2 cell sections in the form of cases and for the case-based recognition of HEp-2 cells in the form of objects in digital images, to computer program products having a program code for carrying out these methods, to computer program products on machine-readable carriers for carrying out these methods, and to digital storage media that can interact with a programmable computer system whereby carrying out these methods.

Abstract: For automatic identification of microorganisms collected on a carrier, a color image of the carrier surface with collected microorganisms is recorded and digitalized. The digitalized image is converted into a grayscale image and optionally converted subsequently into a silhouette image. When microorganisms are present, an image is produced with full-surface labeled objects of a first grayscale and a background of a second grayscale. Objects are identified in the grayscale and/or silhouette image by a model-based comparative method. Contours of the objects are marked in the color or grayscale image. Features of the objects in the color image and/or grayscale image are determined. The objects are classified based on the features. The classified objects are indicated and/or saved as species, name and/or code. Non-classified objects are indicated and/or saved as color, grayscale and/or silhouette image. Non-classified objects are subsequently discarded or added as a new case to the classification system.