Other

Neopath, Inc.

Drvision Technologies LLC

Tripath Imaging, Inc.

Seho Oh

Shih-Jong James Lee

Chih-Chau L Kuan

Wendy R Bannister

Donglok Kim

Michael G Meyer

Paul S Wilhelm

Dayle G Ellison

Larry A Nelson

Keith L Frost

Chi-Chou Huang

Florence W Patten

Michael J Seo

Jon W Hayenga

Alan C Nelson

Shinichi Nakajima

Kim J Hansen

James K Riley

Larry Allan Nelson

Ryan Kempston Seghers

Hansang Cho

Yuji Kokumai

Stanley F Patten

Owsley Lane

A teachable object contour mapping method for region partition receives an object boundary and a teaching image. An object contour mapping recipe creation is performed using the object boundary and the teaching image to generate object contour mapping recipe output. An object contour mapping is applied to an application image using the object contour mapping recipe and the application image to generate object contour map output. An object region partition using the object contour map to generate object region partition output. An updateable object contour mapping method receives a contour mapping recipe and a validation image. An object contour mapping is performed using the object contour mapping recipe and the validation image to generate validation contour map output. An object region partition receives a region mask to generate validation object region partition output. A boundary correction is performed using the validation object region partition to generate corrected object boundary output. An update contour mapping is performed using the corrected object boundary, the validation image and the contour mapping recipe to generate updated contour mapping recipe output.

Teachable object contour mapping for biology image region partition

A computerized robust cell kinetic recognition method for moving cell detection from temporal image sequence receives an image sequence containing a current image. A dynamic spatial-temporal reference generation is performed to generate dynamic reference image output. A reference based object segmentation is performed to generate initial object segmentation output. An object matching and detection refinement is performed to generate kinetic recognition results output. The dynamic spatial-temporal reference generation step performs frame look ahead and the reference images contain a reference intensity image and at least one reference variation image.

Method for moving cell detection from temporal image sequence model estimation

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. The partitioned modeling further performs a partitioned matching step that matches an input point to the partition model to generate a matching score output. A partition model update method receives a partition model and input data for model update. A partition model update is performed using the partition model and the data to generate an updated partition model.

Method of directed pattern enhancement for flexible recognition

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. An update learning method performs pattern enhancement progressive update learning.

A computerized spatial-temporal regulation method for accurate spatial-temporal model estimation receives a spatial temporal sequence containing object confidence mask. A spatial-temporal weight regulation is performed to generate weight sequence output. A weighted model estimation is performed using the spatial temporal sequence and the weight sequence to generate at least one model parameter output. An iterative weight update is performed to generate weight sequence output. A weighted model estimation is performed to generate estimation result output. A stopping criteria is checked and the next iteration iterative weight update and weighted model estimation is performed until the stopping criteria is met. A model estimation is performed to generate model parameter output. An outlier data identification is performed to generate outlier data output. A spatial-temporal data integrity check is performed and the outlier data is disqualified.

Spatial-temporal regulation method for robust model estimation

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.

Partition pattern match and integration method for alignment

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. A directional edge enhancement method determining pixel specific edge contrast enhancement direction according to the object structure direction near the pixel consists receives an image and refinement zones and performs 1D horizontal distance transform and 1D vertical distance transform using the refinement zones to generate horizontal distance map and vertical distance map outputs. A neighboring direction determination is performed using the horizontal distance map and the vertical distance map to generate neighboring image output. In addition, a directional edge contrast calculation using the neighboring image and input image having directional enhanced region of interest output.

Object based boundary refinement method

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 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.

Fast high precision matching method

An intelligent spatial reasoning method receives a plurality of object sets. A spatial mapping feature learning method uses the plurality of object sets to create at least one salient spatial mapping feature output. It performs spatial reasoning rule learning using the at least one spatial mapping feature to create at least one spatial reasoning rule output. The spatial mapping feature learning method performs a spatial mapping feature set generation step followed by a feature learning step. The spatial mapping feature set is generated by repeated application of spatial correlation between two object sets. The feature learning method consists of a feature selection step and a feature transformation step and the spatial reasoning rule learning method uses the supervised learning method. The spatial reasoning approach of this invention automatically characterizes spatial relations of multiple sets of objects by comprehensive collections of spatial mapping features. Some of the features have clearly understandable physical, structural, or geometrical meanings. Others are statistical characterizations, which may not have clear physical, structural or geometrical meanings when considered individually. A combination of these features, however, could characterize subtle physical, structural or geometrical conditions under practical situations. One key advantage of this invention is the ability to characterize subtle differences numerically using a comprehensive feature set.

Intelligent spatial reasoning

A feature regulation application method for hierarchical decision learning systems receives feature regulation training data and invokes a plurality of hierarchical decision learning to create feature subset information output. The method receives learning data and uses the feature subset information and the learning data to create feature subset learning data output. The hierarchical decision learning method uses the feature subset learning data to create hierarchical decision output. The feature regulation method also outputs feature ranking information that can be used to create hierarchical decision output. The invention provides a computationally feasible method for feature selection that considers the hierarchical decision learning systems used for decision making.

Feature regulation for hierarchical decision learning

A segmentation method receives a learning image and an objects of interest specification. A segmentation learning method creates a segmentation recipe output. It performs a segmentation application using the second image and the segmentation recipe to create a segmentation result output. The segmentation learning method includes an object region of interest segmentation learning step and an object type specific segmentation learning step. The segmentation application method includes an object region of interest segmentation step and an object type specific segmentation step. The learnable object segmentation method further comprises an online learning and a feedback learning step that allows the update of the segmentation recipe automatically or under user direction.

Learnable object segmentation

Rotation and scale invariant profiles are generated for matching to a pattern template using a fast regular shaped pattern construction method. The invention achieves rotation invariant matching, rotation and scale invariant matching, and/or scale invariant matching. Invariant profiles are used to perform fast rotation, rotation and scale, or scale invariant search for initial detection of match candidates. The rotation invariant contours of this invention approximate circular contours through use of regular shaped patterns such as octagon or multiple rotated octagons. Rotation invariant search does not depend on rotation angles and is very fast.

Fast invariant pattern search

An accumulation transformation method for fast pattern search accurately locates general patterns of interest. The method can be used for fast invariant search to match patterns of interest in images where the searched pattern varies in size or orientation or aspect ratio, when pattern appearance is degraded, when the pattern is partially occluded, where the searched image is large, multidimensional, or very high resolution, or where the pattern size is large. The accumulation transformations of the input image are determined based upon the searched projection directions. Projection profile result images are derived from the accumulation transformed input image and used for fast matching with template pattern projection profiles.

Fast pattern searching

A fast matching method performs pattern decomposition and synthesis learning to create a pattern search recipe that is used by an invariant pattern search and synthesis method to generate the match result. The pattern search recipe includes template component invariant profiles, component weights, and allowable partial pattern configurations. The invariant matching method supports partial pattern match. This invention decomposes a template into multiple compact shaped components and performs search using separate rotation and scale invariant profiles for each component. It then synthesizes the search results for the complete template or partial template using the component search results.

Fast invariant matching using template decomposition and synthesis

A learning acceleration method is disclosed that can be applied to multiple types and stages of learning to enhance the learning efficiency and outcome. Artificially created training samples can improve representation of all classes in the training set, decrease the difficulty of obtaining sufficient training samples, and decrease the difficulty of unequal sample prevalence. Two specific embodiments of learning acceleration are disclosed: learning accelerated algorithm training and learning accelerated start-up learning. Three objects of interest implantation methods are disclosed: texture mapping of defects, parametric synthesis of negative samples, and manual image editing.

Growing community of inventors

Bellevue, WA, United States of America

Seho Oh