Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

Embodiments disclose a method and system for a scene-aware audio-video representation of a scene. The scene-aware audio video representation corresponds to a graph of nodes connected by edges. A node in the graph is indicative of the video features of an object in the scene. An edge in the graph connecting two nodes indicates an interaction of the corresponding two objects in the scene. In the graph, at least one or more edges are associated with audio features of a sound generated by the interaction of the corresponding two objects. The graph of the audio-video representation of the scene may be used to perform a variety of different tasks. Examples of the tasks include one or a combination of an action recognition, an anomaly detection, a sound localization and enhancement, a noisy-background sound removal, and a system control.

Embodiments disclose a method and system for a scene-aware audio-video representation of a scene. The scene-aware audio video representation corresponds to a graph of nodes connected by edges. A node in the graph is indicative of the video features of an object in the scene. An edge in the graph connecting two nodes indicates an interaction of the corresponding two objects in the scene. In the graph, at least one or more edges are associated with audio features of a sound generated by the interaction of the corresponding two objects. The graph of the audio-video representation of the scene may be used to perform a variety of different tasks. Examples of the tasks include one or a combination of an action recognition, an anomaly detection, a sound localization and enhancement, a noisy-background sound removal, and a system control.

A method of determining a roadway map includes receiving an image from above a roadway. The method further includes generating a skeletonized map based on the received image, wherein the skeletonized map comprises a plurality of roads. The method includes identifying intersections based on joining of multiple roads of the plurality of roads in the skeletonized map. The method includes partitioning the skeletonized map based on the identified intersections, wherein partitioning the skeletonized map defines a roadway data set and an intersection data set. The method includes analyzing the roadway data set to determine a number of lanes in each roadway of the plurality of roads. The method further includes analyzing the intersection data set to lane connections in the identified intersections. The method further includes merging results of the analyzed road data set and the analyzed intersection data set to generate the roadway map.

A method and system is provided for generating a perception scene graph (PSG) having a focus region for a motor vehicle. Information is collected about a volume of space including surrounding areas adjacent a motor vehicle by a plurality of external sensors. The information is processed by a perception controller to generate the PSG having a virtual three-dimensional (3-D) model of the volume of space and area adjacent the motor vehicle. The perception controller is configured to allocate variable processing power to process selected portions of the collected sensor information. At least one focus region is defined. A focus region is a sub-set of the volume of space and/or area adjacent the motor vehicle. Processing power is increased by the perception controller to process the portions of the collected information relating to the focus region such that a high fidelity 3-D model of the focus region is generated.

Disclosed are a quantum system-based image pattern recognition computation apparatus and method for machine vision and a quantum system-based machine vision apparatus. The computation apparatus recognizes patterns between images in machine vision by using a quantum system. The computation apparatus includes a modeling unit and an interpretation unit. The modeling unit sets up an objective function based on the similarity between a first pattern derived from the relationships between points of interests of a first image and a second pattern derived from the relationships between points of interests of a second image. The interpretation unit finds an optimum first pattern and an optimum second pattern, in which the similarity between the first pattern and the second pattern is optimized, by interpreting a final quantum state obtained through an adiabatic evolution process of the quantum system in which the objective function is optimized.

In various example embodiments, a system and method for determining an item that has confirmed characteristics are described herein. An image that depicts an object is received from a client device. Structured data that corresponds to characteristics of one or more items are retrieved. A set of characteristics is determined, the set of characteristics being predicted to match with the object. An interface that includes a request for confirmation of the set of characteristics is generated. The interface is displayed on the client device. Confirmation that at least one characteristic from the set of characteristics matches with the object depicted in the image is received from the client device.

Improving visual quality of a raster image includes detecting connectivity components, detecting defects in each of the connectivity components based on a characteristic line width thereof, detecting segments in each of the connectivity components, detecting joints based on geometry of the connectivity components, creating a structural graph based on the segments and joints, and correcting the raster image according to the structural graph and detected ones of the defects. The joints may correspond to linear joints, T-joints, or X-joints. Detecting types of joints may include determining a configuration of adjacent segments in a proximity of each of the joints. A characteristic line width may be determined by determining co-boundaries on opposite sides of each of the segments and determining average distances between the co-boundaries. The raster image may be a binary black-and-white image of a line drawing obtained from a photograph or a scan of a handwritten document.

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.

The present invention correlates information, to be processed, about an organ and/or a disease, etc., obtained from a medical image and anatomical/functional medical knowledge information, and enables the information obtained from the medial image to be effectively utilized in medical examination and treatment processes. In a medical image information system (101), an image processing unit (103) processes an image, a graph model creation unit (104) creates a graph data model from the information obtained from the image, a graph data model processing unit (106) acquires a graph data model based on anatomical/functional medical knowledge, compares with each other and integrates the graph data models and stores an integrated graph data model, and a display processing unit (110) displays the integrated graph data model, whereby the effective use of information obtained from the image is made possible.