A method for event predictions is provided. The method includes receiving input data. The method further includes identifying an object in the input data with the identified object associated with a first node in a knowledge graph. The method further includes determining a second node of a first object event with the second node related to the first node in the knowledge graph. The method further includes contextualizing the identified input object with the first object event.

A method for event predictions is provided. The method includes receiving input data. The method further includes identifying an object in the input data with the identified object associated with a first node in a knowledge graph. The method further includes determining a second node of a first object event with the second node related to the first node in the knowledge graph. The method further includes contextualizing the identified input object with the first object event.

Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method rely on characterizing local spatial statistics in the images. Also, a method for quantifying intratumor spatial heterogeneity that can work with single biomarker, multiplexed, or hyperplexed immunofluorescence (IF) data. The method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. The method generates a two-dimensional heterogeneity map to explicitly elucidate spatial associations of both major and minor sub-populations.

Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method rely on characterizing local spatial statistics in the images. Also, a method for quantifying intratumor spatial heterogeneity that can work with single biomarker, multiplexed, or hyperplexed immunofluorescence (IF) data. The method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. The method generates a two-dimensional heterogeneity map to explicitly elucidate spatial associations of both major and minor sub-populations.

Disclosed are techniques for detecting and mitigating pest infestations within a grow operation. In some embodiments, such techniques comprise receiving, from a visual observer device, image data associated with a location within a grow operation. The image data is then used to determine at least one pest classification and a count associated with the pest classification. Distribution data is generated based on the at least one pest classification, count, and location. A level of risk can then be determined based on the distribution data. In some embodiments, if the level of risk is greater than a threshold level of risk, a recommendation may be generated based at least in part on the distribution data.

This disclosure relates to a method and system for extracting information from images of one or more templatized documents. A knowledge graph with a fixed schema based on background knowledge is used to capture spatial and semantic relationships of entities present in scanned document. An adaptive lattice-based approach based on formal concepts analysis (FCA) is used to determine a similarity metric that utilizes both spatial and semantic information to determine if the structure of the scanned document image adheres to any of the known document templates, If known document template whose structure is closely matching the structure of the scanned document is detected, then an inductive rule learning based approach is used to learn symbolic rules to extract information present in scanned document image. If a new document template is detected, then any future scanned document images belonging to new document template are automatically processed using the learnt rules.

Systems and methods for segmenting scan data are disclosed. The methods include receiving scan data representing a plurality of points in an environment associated with a ground surface and one or more objects, and creating a graph from the scan data. The graph includes a plurality of vertices corresponding to the plurality of points. The method further includes assigning a unary potential to each of the plurality of vertices that is a cost of assigning that vertex to a ground label or a non-ground label, and assigning a pairwise potential to each pair of neighboring vertices in the graph that is the cost of assigning different labels to neighboring vertices. The methods include using the unary potentials and the pairwise potentials to identify labels for each of the plurality of points, and segmenting the scan data to identify points associated with the ground based on the identified labels.

An activity recognition system is disclosed. A plurality of temporal features is generated from a digital representation of an observed activity using a feature detection algorithm. An observed activity graph comprising one or more clusters of temporal features generated from the digital representation is established, wherein each one of the one or more clusters of temporal features defines a node of the observed activity graph. At least one contextually relevant scoring technique is selected from similarity scoring techniques for known activity graphs, the at least one contextually relevant scoring technique being associated with activity ingestion metadata that satisfies device context criteria defined based on device contextual attributes of the digital representation, and a similarity activity score is calculated for the observed activity graph as a function of the at least one contextually relevant scoring technique, the similarity activity score being relative to at least one known activity graph.

Embodiments of methods and apparatuses for object detection and of computer devices are disclosed. The method for object detection includes: acquiring an image to be detected that is captured by an image capturing means; inputting the image to be detected into a fully convolutional neural network obtained by training to generate an object upper-vertex confidence distribution diagram, an object lower-vertex confidence distribution diagram, and an object upper-and-lower-vertex correlation diagram for the image to be detected; for the object upper-vertex confidence distribution diagram and the object lower-vertex confidence distribution diagram respectively, determining upper-vertex objects and lower-vertex objects in the image to be detected by using a preset object determination method; for each first vertex, calculating a correlation value of a connection line connecting the first vertex object and each of second vertex object respectively by mapping the upper-vertex objects and the lower-vertex object onto the object upper-and-lower-vertex correlation diagram; and based on the correlation values, determining a connection line having a maximum correlation value as a specified object by matching the upper-vertex objects and lower-vertex objects. The accuracy of object detection can be improved through the present solution.

Biometric health monitoring of a specific user or population is performed during biometric authentication for granting access to physical or digital assets. If biometric authentication, biometric verification and biometric health monitoring is acceptable, access to the physical or digital assets is allowed. Likewise, if a health anomaly is detected in a specific user or if an outbreak is detected in a specific community, an electronic notification can be sent to the individual, a health administrator, or to a government official, and access may be denied to the specific user.