The current document is directed to methods and systems for identifying symbols corresponding to symbol images in a scanned-document image or other text-containing image, with the symbols corresponding to Chinese or Japanese characters, to Korean morpho-syllabic blocks, or to symbols of other languages that use a large number of symbols for writing and printing. In one implementation, the methods and systems to which the current document is directed create and store a decision tree, the nodes of which include classifiers that each recognizes the symbol that corresponds to a symbol image. Input of a symbol image to the decision tree and processing of the symbol image through one or more nodes of the decision tree returns a symbol corresponding to the symbol image.

Techniques are described to generate improved training data for pixel labeling. To generate training data, objects are displayed in a user interface by a computing device, e.g., iteratively. The objects are taken from a structured object representation associated with a respective one of a plurality of images. The structured object representation defines a hierarchical relationship of the objects within the respective image. Inputs are then received that are originated through user interaction with the user interface. The inputs label respective ones of the iteratively displayed objects, e.g., as text, a graphical element, background, foreground, and so forth. A model is trained by the computing device using machine learning.

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 are disclosed for optimizing a material graph for replicating a material of the target image. Embodiments include receiving a target image and a material graph to be optimized for replicating a material of the target image. Embodiments include identifying a non-differentiable node of the material graph, the non-differentiable node including a set of input parameters. Embodiments include selecting a differentiable proxy from a library of the selected differentiable proxy is trained to replicate an output of the identified non-differentiable node. Embodiments include generating an optimized input parameters for the identified non-differentiable node using the corresponding trained neural network and the target image. Embodiments include replacing the set of input parameters of the non-differentiable node of the material graph with the optimized input parameters. Embodiments include generating an output material by the material graph to represent the target image using the optimized input parameters for the non-differentiable node.

A computer-implemented method for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising determining a set of samples characterising a location in an image by sampling scale-space data representative of the image, the scale-space data comprising data representative of the image at a plurality of length scales; and generating a feature descriptor in dependence on the determined set of samples.

A computer-implemented method for generating a feature descriptor for a location in an image for use in performing descriptor matching in analysing the image, the method comprising determining a set of samples characterising a location in an image by sampling scale-space data representative of the image, the scale-space data comprising data representative of the image at a plurality of length scales; and generating a feature descriptor in dependence on the determined set of samples.

Some embodiments are directed to a computer implemented method for simplification of a point cloud including a set of points. The method comprises implementing recursive spatial partitioning of the set of points into a hierarchy of clusters, identifying representative points within each cluster in the hierarchy and, for each representative point, defining a point-pair that consists of or includes the representative point and a representative point of an immediate parent cluster. The method further includes calculating a contraction error metric for each point-pair, and iteratively contracting the point-pair with the lowest contraction error metric, updating remaining point-pairs as a result of the contraction, and revising the contraction error metric of the updated point-pairs accordingly.

According to an embodiment, a character recognition apparatus includes a character string image acquisition unit, a combination graph generation unit, a combination graph integration unit and an output unit. The character string image acquisition unit acquires a character string image. The combination graph generation unit performs a character recognition process on the character string image and generates a combination graph. The combination graph integration unit integrates a plurality of combination graphs generated from a plurality of character string images including an identical character string or integrates a plurality of combination graphs generated by performing a plurality of different character recognition processes on the single character string image. The output unit outputs the integrated combination graph or a recognition character string obtained based on the integrated combination graph.

Solutions for object tracking problems are presented by gathering images using one or more cameras, processing the gathered images to generate a directed acyclic graph, using the directed acyclic graph to determine a path cover that achieves maximum weight and satisfies one or more positive or negative constraints, and using the path cover to solve the object tracking problem. A first set of solutions utilizes trellis graphs, a second set of solutions employs a greedy approach, and a third set of solutions uses search algorithms.

A method for distinguishing between pulmonary arteries and pulmonary veins and a method for quantifying blood vessels are disclosed. The method for distinguishing between pulmonary arteries and pulmonary veins includes: forming a set of pulmonary vessels for points corresponding to pulmonary vessels, wherein each of the points of the set of pulmonary vessels has weight information; forming a tree from the points of the set of pulmonary vessels by using the weight information; and distinguishing between the pulmonary arteries and the pulmonary veins by separating the tree into a plurality of regions. The method for quantifying blood vessels includes: extracting blood vessels as a three-dimensional set of voxels based on medical images of an organ; finding the voxels of blood vessels included in a region of interest of the organ; and quantifying length information of the blood vessels by using the found voxels.