Abstract: Disclosed is an electronic apparatus which includes a memory storing a first neural network model and a second neural network model; and a processor connected to the memory configured to control the electronic apparatus, and the processor may obtain context information of a user, operation information, and environment information of a washing machine, identify an active time and an inactive time of the user by inputting the context information into the first neural network model, obtain one or more freezing probabilities by time zones of the washing machine by inputting the operation information and the environment information into the second neural network model based on a current point in time being within the active time, and identify a freezing probability greater than or equal to a threshold freezing probability during the active time and the inactive time based on the obtained one or more freezing probabilities by time zones.

Abstract: Disclosed is an electronic apparatus which includes a memory storing a first neural network model and a second neural network model; and a processor connected to the memory configured to control the electronic apparatus, and the processor may obtain context information of a user, operation information, and environment information of a washing machine, identify an active time and an inactive time of the user by inputting the context information into the first neural network model, obtain one or more freezing probabilities by time zones of the washing machine by inputting the operation information and the environment information into the second neural network model based on a current point in time being within the active time, and identify a freezing probability greater than or equal to a threshold freezing probability during the active time and the inactive time based on the obtained one or more freezing probabilities by time zones.

Abstract: Disclosed is an electronic apparatus which includes a memory storing a first neural network model and a second neural network model; and a processor connected to the memory configured to control the electronic apparatus, and the processor may obtain context information of a user, operation information, and environment information of a washing machine, identify an active time and an inactive time of the user by inputting the context information into the first neural network model, obtain one or more freezing probabilities by time zones of the washing machine by inputting the operation information and the environment information into the second neural network model based on a current point in time being within the active time, and identify a freezing probability greater than or equal to a threshold freezing probability during the active time and the inactive time based on the obtained one or more freezing probabilities by time zones.

Abstract: 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.

Abstract: 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.

Abstract: A computerized derivable kinetic characterization measurement method for live cell kinetic characterization inputs kinetic recognition data for a plurality of time frames. A single cell measurement step is performed using the kinetic recognition data for a plurality of time frames to generate single cell feature for a plurality of time frames output. The single cell feature includes cell morphological profiling feature. A kinetic measurement step uses the single cell feature for a plurality of time frames to generate kinetic feature output. A trajectory measurement step uses the single cell feature for a plurality of time frames and the kinetic feature to generate trajectory feature output. An interval measurement step uses the kinetic feature to generate interval feature output. A cell state classifier step uses the interval feature to generate cell state output. A state based measurement uses the single cell feature, the kinetic feature and the cell state to generate state based feature output.

Abstract: 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.

Abstract: 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.

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 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.

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

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 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.

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 computerized derivable kinetic characterization measurement method for live cell kinetic characterization inputs kinetic recognition data for a plurality of time frames. A single cell measurement step is performed using the kinetic recognition data for a plurality of time frames to generate single cell feature for a plurality of time frames output. The single cell feature includes cell morphological profiling feature. A kinetic measurement step uses the single cell feature for a plurality of time frames to generate kinetic feature output. A trajectory measurement step uses the single cell feature for a plurality of time frames and the kinetic feature to generate trajectory feature output. An interval measurement step uses the kinetic feature to generate interval feature output. A cell state classifier step uses the interval feature to generate cell state output. A state based measurement uses the single cell feature, the kinetic feature and the cell state to generate state based feature output.

Abstract: 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.

Abstract: 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.

Abstract: A computerized directed feature development method receives an initial feature list, a learning image and object masks. Interactive feature enhancement is performed by human to generate feature recipe. The Interactive feature enhancement includes a visual profiling selection method and a contrast boosting method. A visual profiling selection method for computerized directed feature development receives initial feature list, initial features, learning image and object masks. Information measurement is performed to generate information scores. Ranking of the initial feature list is performed to generate a ranked feature list. Human selection is performed through a user interface to generate a profiling feature. A contrast boosting feature optimization method performs extreme example specification by human to generate updated montage. Extreme directed feature ranking is performed to generate extreme ranked features.

Abstract: 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.