Abstract: A directed pattern enhancement method receives a learning image and pattern enhancement directive. Pattern enhancement learning is performed using the learning image and the pattern enhancement directive to generate pattern enhancement recipe. An application image is received and a pattern enhancement application is performed using the application image and the pattern enhancement recipe to generate pattern enhanced image. A recognition thresholding is performed using the pattern enhanced image to generate recognition result. The pattern enhancement directive consists of background directive, patterns to enhance directive, and patterns to suppress directive. A partitioned modeling method receives an image region and performs feature extraction on the image region to generate characterization feature. A hierarchical partitioning is performed using the characterization feature to generate hierarchical partitions. A model generation is performed using the hierarchical partitions to generate partition model.

Abstract: A keypad optimization method for handheld devices with optimal multi-language entry capability and handiness for both handheld and fingertip manipulation receives application requirements containing language specification and performs application mapping using the application requirements to generate versatile keypad map output. A versatile keypad is implemented using versatile keypad map wherein the versatile keypad receives user input and generates received data output. An interactive optimization is performed using the received data having online action output. The online action is used to generate keypad output and screen display by the versatile keypad. An application mapping method receives application requirements containing language specification and performs language entry order grouping using the language specification to generate entry order groups and valid combination output.

Abstract: An integrated human and computer interactive data mining method receives an input database. A learning, modeling, and analysis method uses the database to create an initial knowledge model. A query of the initial knowledge model is performed using a query request. The initial knowledge model is processed to create a knowledge presentation output for visualization. It further comprises a feedback and update request step that updates the initial knowledge model. A multiple level integrated human and computer interactive data mining method facilitates overview interactive data mining and dynamic learning and knowledge representation by using the initial knowledge model and the database to create and update a presentable knowledge model. It facilitates zoom and filter interactive data mining and dynamic learning and knowledge representation by using the presentable knowledge model and the database to create and update the presentable knowledge model.

Abstract: An object based boundary refinement method for object segmentation in digital images receives an image and a single initial object region of interest and performs refinement zone definition using the initial object regions of interest to generate refinement zones output. A directional edge enhancement is performed using the input image and the refinement zones to generate directional enhanced region of interest output. A radial detection is performed using the input image the refinement zones and the directional enhanced region of interest to generate radial detection mask output. In addition, a final shaping is performed using the radial detection mask having single object region output.

Abstract: An alignment template goodness qualification method receives a pattern image and a pattern based alignment template and performs template goodness measurement using the pattern image and the pattern based alignment template to generate template goodness result output. A template qualification is performed using the template goodness result to generate template qualification result output. If the template qualification result is acceptable, the pattern based alignment template is outputted as the qualified pattern based alignment template. Otherwise, an alternative template selection is performed using the pattern image, the pattern based alignment template and the template goodness result to generate alternative pattern based alignment template output. The template goodness measurements include signal content measurement, spatial discrimination measurement and pattern ambiguity measurement.

Abstract: A region-guided boundary refinement method for object segmentation in digital images receives an initial object regions of interest and performs directional boundary decomposition using the initial object regions of interest to generate a plurality of directional object boundaries output. A directional border search is performed using the plurality of directional object boundaries to generate base border points output. A base border integration is performed using the base border points to generate base borders output. In addition, a boundary completion is performed using the base borders having boundary refined object regions of interest output. A region-guided boundary completion method for object segmentation in digital images receives an initial object regions of interest and base borders. It performs boundary completion using the initial object regions of interest and the base borders to generate boundary refined object regions of interest output.

Abstract: A partition pattern template generation method for alignment receives a learning image and performs partition template generation using the learning image to generate a plurality of partition template result output. A partition template acceptance test is performed using the plurality of partition template results to generate partition templates or failure result. A partition template search method for alignment receives an alignment image and partition templates and performs a plurality of template search steps to generate a plurality of matching scores output. A partition integration method is performed using the plurality of matching scores to generate a partition template search result. A partition integration error self checking method receives a preliminary template search result position and a plurality of the matching scores. A matching score profile comparison is performed using the plurality of the matching scores and the expected matching score profile to generate the template search result.

Abstract: A robust object segmentation method for analysis of biological activity receives an input image and performs segmentation confidence mapping using the input image to generate segmentation confidence map output. A thresholding is performed using the object segmentation confidence map to generate a high confidence object mask output. An object segmentation confidence mapping method for analysis of biological activity receives an input image and performs segmentation decision to create segmentation decision result. A difference operation is performed to generate the segmentation decision result. A confidence mapping is performed using the difference result to generate segmentation confidence. An object level robust analysis method for biological activity receives an input image and performs object segmentation using the input image to create object segmentation result. A robust object feature measurement is performed to generate robust object feature result.

Abstract: A method for the detection and analysis of patterns receives an image containing object labels and performs relational feature development using the input image to create at least one pattern map. It then performs relational feature analysis using the at least one pattern map to create a relational feature analysis result. The pattern detection and analysis method further comprises a recipe for automation control and includes determination of a genetic anomaly. A relational feature development method receives an image containing object labels and performs core measurement table development using the input image to create at least one core measurement table. It then performs feature table production using the at least one core measurement table to create at least one feature table. It also performs PatternMap creation using the at least one feature table to create a PatternMap. The relational feature development method further comprises a PatternMap integration and update step to create an updated PatternMap.

Abstract: A fast image region partition method receives a component labeled image and performs a two pass Zone Of Influence (ZOI) creation method to create a Zone Of Influence (ZOI) image. The two pass ZOI creation method performs a first pass scan to create a first pass intermediate distance image and a shortest distance component label image. It then performs a second pass scan using the first pass intermediate distance image and the shortest distance component label image to create a background distance transform image and a updated shortest distance component label image. An adaptive image region partition method receives a component labeled image and performs an adaptive two pass ZOI creation method to create an adaptive ZOI image. The distance lengths of the two pass adaptive ZOI creation method depend on their associated component labels. An adaptive cell segmentation method receives a nuclei mask image and a cell mask image.

Abstract: A feature regulation application method for hierarchical decision learning systems receives a feature regulation training data. A feature regulation method uses the feature regulation training data and invokes a plurality of the hierarchical decision learning to create feature subset information output. The feature regulation application method also receives a learning data. A feature sampling method uses the feature subset information and the learning data to create a feature subset learning data output. A hierarchical decision learning method uses the feature subset learning data to create a hierarchical decision system output. The feature regulation method also outputs feature ranking information. A feature regulated hierarchical decision learning method uses the feature subset learning data and the feature ranking information to create a hierarchical decision system output.

Abstract: A fast high precision matching method receives an input image and a template. An initial search method uses the input image and the template to create an initial search result output. A high precision match uses the initial search result, the input image, and the template to create a high precision match result output. The high precision match method estimates high precision parameters by image interpolation and interpolation parameter optimization. The high precision match method also performs robust matching by limiting pixel contribution or pixel weighting. An invariant high precision match method estimates subpixel position and subsampling scale and rotation parameters by image interpolation and interpolation parameter optimization on the log-converted radial-angular transformation domain.