Abstract: Methods and systems are disclosed to aid in the detection of cancer or lesion in a mammogram images. Two mammogram images are input into an application that aids in determining the probability of a cancer or lesion being present in one or both of the images. The images are divided into different nodes and labels are applied to the nodes. The first node is compared to different variants of corresponding nodes on the second image as well as neighboring nodes on the first image. Based upon the comparisons, a unary and binary potential is calculated for the label that is applied to the node. The process is repeated for every possible label and for every node. Once the unary and binary potentials have been calculated, the potentials are input into a Conditional Random Field model to determine the probability of cancer for each node of the images.

Abstract: Methods and systems are to facilitate the interaction between a user and a computer aided detection (“CAD”) system when identifying areas of interest in an image. The CAD system receives input from a user indicating areas of interest that the user perceives in the image. The input may include location information as well as additional information about the area of interest. The CAD system may then perform a voting process using the user provided areas of interest and one or more sets of areas of interest identified by at least one image recognition process to produce a final set of areas of interest in the image. The voting process is useful in eliminating false positives and false negatives that may have been identified by the user and/or the image recognition processes. Further embodiments disclosed herein provide methods and systems for training a voting process to complement a particular user.

Abstract: Methods and systems are disclosed to aid in the detection of cancer or lesion in a mammogram images. Two mammogram images are input into an application that aids in determining the probability of a cancer or lesion being present in one or both of the images. The images are divided into different nodes and labels are applied to the nodes. The first node is compared to different variants of corresponding nodes on the second image as well as neighboring nodes on the first image. Based upon the comparisons, a unary and binary potential is calculated for the label that is applied to the node. The process is repeated for every possible label and for every node. Once the unary and binary potentials have been calculated, the potentials are input into a Conditional Random Field model to determine the probability of cancer for each node of the images.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: Character model graphs are created, and the parameters of the model graphs are adjusted to optimize character recognition performed with the model graphs. In effect the character recognizer using the model graphs is trained. The model graphs are created in three stages. First, a vector quantization process is used on a set of raw samples of handwriting symbols to create a smaller set of generalized reference characters or symbols. Second, a character reference model graph structure is created by merging each generalized form model graph of the same character into a single character reference model graph. The merging is based on weighted Euclidian distance between parts of trajectory assigned to graph edges. As a last part of this second stage “type-similarity” vectors are assigned to model edges to describe similarities of given model edge to each shape and to each possible quantized value of other input graph edge parameters.

Abstract: A character recognizer recognizes a handwritten input character. A sequence of points in two dimensional space representative of a stroke trajectory forming the input character is gathered. An input Directed Acyclic Graph is built with nodes representative of singular points at the beginning, end, and along the trajectory of the input character and with edges between nodes representative of an edge trajectory formed by the sequence of points of the input character between the singular points. Each edge in the input graph is described based on the shape, orientation and pen lift of the edge trajectory that the edge represents. The input graph is evaluated against model graphs, which are also Directed Acyclic Graphs, for all possible characters to find a path through a model graph that produces a best path similarity score with a corresponding path through the input graph. The input character is identified as an answer character represented by the model graph producing the best path similarity score.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: Character model graphs are created, and the parameters of the model graphs are adjusted to optimize character recognition performed with the model graphs. In effect the character recognizer using the model graphs is trained. The model graphs are created in three stages. First, a vector quantization process is used on a set of raw samples of handwriting symbols to create a smaller set of generalized reference characters or symbols. Second, a character reference model graph structure is created by merging each generalized form model graph of the same character into a single character reference model graph. The merging is based on weighted Euclidian distance between parts of trajectory assigned to graph edges. As a last part of this second stage “type-similarity” vectors are assigned to model edges to describe similarities of given model edge to each shape and to each possible quantized value of other input graph edge parameters.

Abstract: A character recognizer recognizes a handwritten input character. A sequence of points in two dimensional space representative of a stroke trajectory forming the input character is gathered. An input Directed Acyclic Graph is built with nodes representative of singular points at the beginning, end, and along the trajectory of the input character and with edges between nodes representative of an edge trajectory formed by the sequence of points of the input character between the singular points. Each edge in the input graph is described based on the shape, orientation and pen lift of the edge trajectory that the edge represents. The input graph is evaluated against model graphs, which are also Directed Acyclic Graphs, for all possible characters to find a path through a model graph that produces a best path similarity score with a corresponding path through the input graph. The input character is identified as an answer character represented by the model graph producing the best path similarity score.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: The invention improves the appearance of freehand drawn lines and shapes in an electronic document by first recognizing freehand drawn lines and shapes and generating a line made up of sequential straight line segments for the freehand drawn line when the line does not form a closed line and generating a multiple straight-line-segment shape when the line forms a closed line. If a multiple segment shape is being reshaped, a basic shape is selected from reference ideal shapes as the basic shape of the multiple segment shape. The basic shape is adjusted to provide a specific shape as an improved shape for the freehand drawn shape. The recognition of the freehand drawn lines and shapes is accomplished by comparing source segments of a source freehand drawn line to a straight line and substituting a straight line segment for a source segment if the deviation between the source segment and the straight line is below a predetermined value.

Abstract: In an electronic-mail computing system having a display and a pointing device for entering geometric elements in a geometric document, additional writing space is inserted in an existing geometric document having original space with one or more original geometric elements. This is accomplished by creating a boundary line or split line and inserting open or free space at the boundary line in the document. Further, an insertion of a boundary line in the midst of a geometric elements of a document to split the elements and add free space is accomplished by grouping geometric elements split by the boundary line or split line and then locating grouped geometric elements on one side or the other of the split line before space is inserted at the split line. The split line on the geometric document is detected, and original geometric elements crossed by the split line are detected and identified as crossed geometric elements.

Abstract: A method and apparatus for cursive script recognition employs an intermediate cursive words description language constructed of elements in the form of metastrokes wherein each of the metastrokes is a member of a metric space, the metric space being expressible as a matrix of likelihood of matching between input metastrokes and predefined metastrokes forming a vocabulary. Typically, a minimum of twenty metastrokes is used to form the vocabulary or set of allowable stroke elemental shapes, although as many as seventy may be used, with a correlation factor or measurement of similarity being defined between strings of metastrokes. Techniques for interpreting the metastrokes as words include substituting, adding, and deleting metastrokes in an input sequence, measuring similarity and comparing with entries in a dictionary of words constructed of metastroke sequences and variants of metastroke sequences. Weighting of similarity may include penalties for lack of similarity.

Abstract: A method and apparatus for cursive script recognition employs an intermediate cursive words description language constructed of elements in the form of metastrokes wherein each of the metastrokes is a member of a metric space, the metric space being expressible as a matrix of likelihood of matching between input metastrokes and predefined metastrokes forming a vocabulary. Typically, a minimum of twenty metastrokes is used to form the vocabulary or set of allowable stroke elemental shapes, although as many as seventy may be used, with a correlation factor or measurement of similarity being defined between strings of metastrokes. Techniques for interpreting the metastrokes as words include substituting, adding, and deleting metastrokes in an input sequence, measuring similarity and comparing with entries in a dictionary of words constructed of metastroke sequences and variants of metastroke sequences. Weighting of similarity may include penalties for lack of similarity.