Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method relies 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.

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.

A method for analyzing one or more conditions of a transportation pathway includes obtaining, using an imaging device of an inspection system, image data reproducible as a plurality of images of the transportation pathway, each of the plurality of images being reproducible as an image of a portion of the transportation pathway, each portion of the transportation pathway having an associated location along a length of the transportation pathway, analyzing, using one or more processors of the inspection system, the image data to determine a first plurality of metrics indicative of a condition of the transportation pathway at each of the associated locations, and generating a first graph, using the determined first plurality of metrics, that is indicative of the condition of the transportation pathway at each of the associated locations.

A method and an apparatus for encoding and/or decoding digital images, wherein the encoding apparatus includes a processor configured for determining weights of a graph related to an image by minimizing a cost function, transforming the weights through a graph Fourier transform, quantizing the transformed weights, computing transformed coefficients through a graph Fourier transform of a graph having the transformed weights as weights, de-quantizing the quantized transformed weights, computing a reconstructed image through an inverse graph Fourier transform on the basis of the de-quantized transformed weights, computing a distortion cost on the basis of the reconstructed image and the original image, generating a final encoded image on the basis of the distortion cost.

Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from graphical representations. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from graphical representations and creating a centralized database for providing graphical attributes, image skeletons, and other metadata information integrated with a graphical representation classification training layer. The invention is designed to receive a graphical representation for analysis, intelligently identify and extract objects and data in the graphical representation, and store the data attributes of the graphical representation in an accessible format in an automated fashion.

Similarity acquisition means calculates a similarity in each combination of an examination cross-sectional image and a reference cross-sectional image between examination volume data and reference volume data. Adjustment value acquisition means acquires an adjustment value of the similarity based on a relationship between the cross-sectional positions of examination cross-sectional images in two combinations and a relationship between the cross-sectional positions of reference cross-sectional images in the two combinations. Association means associates the examination cross-sectional image and the reference cross-sectional image with each other based on a sum of all the similarities and all the adjustment values.

In one embodiment, a method includes extracting a document object model (DOM) for a content page, wherein the DOM comprises a hierarchical tree-based data structure. The method also includes identifying candidate nodes in the DOM based on a context of the nodes, wherein the candidate nodes may correspond to listing items. The method additionally includes for each of the candidate nodes, locating its parent and child nodes by traversing the DOM from the candidate node, extracting information from the candidate node and its parent and child nodes, and assessing whether the candidate node qualifies as a listing item based on whether the extracted information fulfills a required set of characteristics for a listing item.

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.

Provided are methods and apparatuses for determining a target object in an image based on an interactive input. A target object determining method acquires first feature information corresponding to an image and second feature information corresponding to an interactive input; and determines a target object corresponding to the interactive input from among objects in the image based on the first feature information and the second feature information.

Systems and methods are described for generating a machine learning model for multi-modal feature extraction. The method may include receiving a document in a digital format, where the digital format comprises text information and image information, performing a text extraction function on a first portion of the document to produce a set of text features, performing an image extraction function on a second portion of the document to produce a set of image features, generating a feature tree, wherein a plurality of nodes of the feature tree correspond to the set of text features and the set of image features, and generating an input vector for a machine learning model based on the feature tree. In some cases, the feature tree may be generated synthetically, or modified by a user prior to being converted into the input vector.