Abstract: Systems and methods for classifying historical images. A feature extractor may create feature vectors corresponding to a plurality of images. A first classification of the plurality of images may be performed based on the plurality of feature vectors, which may include assigning a label to each of the plurality of images and assigning a probability for each of the assigned labels. The assigned probability for each of the assigned labels may be related to a statistical confidence that a particular assigned label is correctly assigned to a particular image. A subset of the plurality of images may be displayed to a display device. An input corresponding to replacement of an incorrect label with a corrected label for a certain image may be received from a user. A second classification of the plurality of images based on the input from the user may be performed.

Abstract: A tree building system that accesses data from a social networking site and, in some embodiments, other data sites having information that may pertain to the relationships between users of the social networking site. A rules engine within the system implements logic for analyzing the data from the social networking site. The system provides a candidate list of possible relatives for selection by a user and solicits information from the user through the use of guided questions.

Abstract: Described are techniques for determining population structure from identity-by-descent (IBD) of individuals. The techniques may be used to predict that an individual belongs to zero, one or more of a number of communities identified within an IBD network. Additional data may be used to annotate the communities with birth location, surname, and ethnicity information. In turn, these data may be used to provide to an individual a prediction of membership to zero, one or more communities, accompanied by a summary of the information annotated to those communities.

Abstract: A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.

Abstract: Described are computational methods to reconstruct the chromosomes (and genomes) of ancestors given genetic data, IBD information, and full or partial pedigree information of some number of their descendants.

Abstract: A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.

Abstract: A graph database system is used to determine relationships between persons. The graph database system manages relationship data records having a compact data structure stored at a relationship database and accessed to find links or common relationships. For determining relationships between two people, the relationship records in the relationship paths for those two people are alternatively accessed and compared it to each other. Relationship records may include an identifier for a person having a relationship to a given individual, a relationship type, and a weight reflecting the confidence level or quality of the relationship.

Abstract: A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.

Abstract: A sample collection device having a sample tube, funnel, and cap having a capsule and a piercing insert, the capsule having a stabilization solution. After depositing the sample into the tube via the funnel, the cap is screwed onto the tube, piercing the capsule and releasing the stabilization fluid into the tube. The device can be used at home without clinicial supervision for collecting a saliva sample and transporting the sample to an analysis location for DNA analysis.

Abstract: A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.

Abstract: Methods, devices and systems are described for implementing interactive modification of spacing constraints of genealogical charts. The method includes displaying, on a display device of a computer system, a user interface for displaying one or more genealogical charts including one or more nodes. The user interface includes a horizontal slider and a vertical slider for manipulating the one or more geological charts' size. The one or more geological charts include page breaks representing a number of pages. The method further includes receiving, from an input device of the computer system, manipulation input for one or more of the vertical slider or the horizontal slider. Then, in response to receiving the manipulation input, providing real-time graphical feedback representing the manipulation in the display of the one or more genealogical charts.

Abstract: A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.

Abstract: A method of creating a blending mode for blending base colors and blend colors to produce resultant colors for one or more pixels of a digital image includes displaying by a display device a relationship between a first independent variable representing the base colors and a dependent variable representing the resultant colors for a specific value of a second independent variable representing blend colors. The method also includes receiving user input altering the relationship, displaying the altered relationship, and storing the relationship as a blending mode.

Abstract: According to one embodiment, a method of linking a document image with indexed data is provided. The method may be performed by providing a document image, which is a digitized document having various information. Indexed data is also provided, which is a record that includes information extracted from the document image or a different document image. The process is further performed by identifying a feature of the indexed data and analyzing the document image to determine whether the feature is present within the information of the digitized document. The feature may be information or a characteristic defined by the information extracted from the document image or the different document image. If the feature is present within the information of the digitized document, a determination is made that the indexed data corresponds with the document image and the indexed data is linked with the document image.

Abstract: A tree building system that accesses data from a social networking site and, in some embodiments, other data sites having information that may pertain to the relationships between users of the social networking site. A rules engine within the system implements logic for analyzing the data from the social networking site. The system provides a candidate list of possible relatives for selection by a user and solicits information from the user through the use of guided questions.

Abstract: Undated photos are organized by estimating the date of each photo. The date is estimated by building a model based on a set of reference photos having established dates, and comparing image characteristics of the undated photo to the image characteristics of the reference photos. The photo characteristics can include hues, saturation, intensity, contrast, sharpness and graininess as represented by image pixel data. Once the date of a photo is estimated, it can be tagged with identifying information, such as by using the estimated date to associate the photo with a node in a family tree.

Abstract: Described embodiments enable identification of family networks using combinations of DNA analysis and genealogical information. Genealogical data is provided by users of a genealogical research service or collected from other sources and used to create family trees for each user. DNA samples are also received from the users. By analyzing the DNA samples, potential genetic relationships can be identified between some users. Once these DNA-suggested relationships have been identified, common ancestors can be sought in the respective trees of the potentially related users. Where these common ancestors exist, an inference is drawn that the DNA-suggested relationship accurately represents a familial overlap between the individuals in question. People descended from the same common ancestor are each members of a family network. Members of a family network not in a user's tree may be identified for the user, enabling the user to discover additional ancestors that might otherwise have remained unknown.

Abstract: Undated photos are organized by estimating the date of each photo. The date is estimated by building a model based on a set of reference photos having established dates, and comparing image characteristics of the undated photo to the image characteristics of the reference photos. The photo characteristics can include hues, saturation, intensity, contrast, sharpness and graininess as represented by image pixel data. Once the date of a photo is estimated, it can be tagged with identifying information, such as by using the estimated date to associate the photo with a node in a family tree.

Abstract: A tree building system that accesses data from a social networking site and, in some embodiments, other data sites having information that may pertain to the relationships between users of the social networking site. A rules engine within the system implements logic for analyzing the data from the social networking site. The system provides a candidate list of possible relatives for selection by a user and solicits information from the user through the use of guided questions.

Abstract: A handwriting recognition system converts word images on documents, such as document images of historical records, into computer searchable text. Word images (snippets) on the document are located, and have multiple word features identified. For each word image, a word feature vector is created representing multiple word features. Based on the similarity of word features (e.g., the distance between feature vectors), similar words are grouped together in clusters, and a centroid that has features most representative of words in the cluster is selected. A digitized text word is selected for each cluster based on review of a centroid in the cluster, and is assigned to all words in that cluster and is used as computer searchable text for those word images where they appear in documents. An analyst may review clusters to permit refinement of the parameters used for grouping words in clusters, including the adjustment of weights and other factors used for determining the distance between feature vectors.