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: An object analysis method performs object segmentation to generate segmentation confidence map and uses the segmentation results to generate robust object features. The robust object features are combined to create robust FOV summary features, robust sample summary features and robust assay summary features.

Abstract: A two pass Zone Of Influence (ZOI) creation method creates 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 allows distance lengths of the two pass adaptive ZOI creation method depend on their associated component labels.

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 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.

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.