Abstract: A method for determining an image quality verification (IQV) metric for false color artifacts in chromatic areas of digital images includes receiving a digital image that includes flat pure color patches and mixed color texture patches; processing the digital image in an image signal processing chain such that a red/green/blue image is produced; transforming the red/green/blue image into an LAB color space image; calculating, for each mixed color pixel in a mixed color texture patch, a chromatic distance in the LAB color space from the mixed color pixel in the mixed color texture patch to a center of each flat pure color patch; performing directional adjustments on each of the chromatic distances; and defining, for each mixed color patch, the IQV metric for the for the mixed color patch as a function of the distance metrics of each mixed color texture pixel in the mixed color patch.

Abstract: In a correction mode, a correction image is displayed on an extension display, and a specific region is displayed on the extension display. In the correction mode, a display style control unit performs at least one of changing a display style of the correction image or changing a display style of the specific region in accordance with reliability related to calculation of the oxygen saturation.

Abstract: An image processing apparatus includes a memory storing instructions, and a processor configured to execute the instructions to acquire information about an optical system included in an image pickup apparatus configured to capture an image, acquire a change amount in the information about the optical system in a peripheral area of a target pixel in the image based on the information about the optical system, and acquire, based on the change amount, information about recognition accuracy in the peripheral area by a recognition apparatus configured to perform image recognition.

Abstract: 1.

Abstract: There is provided a method for training a caption rephrasing model to allow edge-case data to be generated. The method includes steps of: (a) in response to acquiring at least one image caption, inputting, by a learning device, the image caption to the caption rephrasing model to generate a first rephrased caption to an n-th rephrased caption; (b) inputting, by the learning device, each of the first rephrased caption to the n-th rephrased caption to an image generating model, to thereby generate a first generated image and a first pseudo-label to an n-th generated image and an n-th pseudo-label, and generating, by the learning device, a first identification loss to an n-th identification; and (c) fine-tuning, by the learning device, the caption rephrasing model through a preference learning using one of (i) a preference data set and (ii) a group normalized reward.

Abstract: A computer device segments a first sample region to obtain a candidate image region set that includes a plurality of candidate image regions. For each of the candidate image regions, the device obtains a first relationship degree corresponding to each candidate image region and obtains a second relationship degree corresponding to the candidate image region. The device obtains a relationship degree change value based on the first relationship degree and the second relationship degree. The device selects, from the plurality of candidate image regions, a first candidate image region as a target image region in accordance with a determination that the first candidate image region satisfies a condition in the relationship degree change value. The device performs model training based on the target image region to obtain a target detection model.

Abstract: Generating intelligent drawings includes obtaining a current set of design drawings for a physical plant comprising a plurality of components, generating rasterized drawings from the current set of design drawings, and applying an object detection model to the rasterized drawings to extract a set of shapes corresponding to components in the drawings. A trained library is applied to the extracted shapes to obtain a classification of components in the drawings, and the classification is propagated to instances of the components in the current set of drawings.

Abstract: Various embodiments relate generally to computer vision and automation to autonomously survey an agricultural environment. In some examples, a system can receive sensor data of a geographic boundary from one or more sensors onboard of the agricultural observation system, detect a plurality of external dynamic objects, a plurality of external static objects, one or more agricultural objects, or a combination thereof from the sensor data, determine a position of a component of the agricultural observation system relative to the plurality of external dynamic objects, the plurality of external static objects, the one or more agricultural objects, or a combination thereof, and generate a local map, in real time, via one or more processing units.

Abstract: Apparatuses, systems, and techniques to scale textured images using a Fourier transform in conjunction with one or more neural networks. In at least one embodiment, a neural network generates an expanded image from an input image by applying a Fourier transform to one or more feature maps generated by said neural network and up-scaling one or more resulting frequency domain feature maps before generating an expanded output image based on up-scaled feature maps.

Abstract: An information processing apparatus comprises a first inference unit configured to infer an attribute of an object in an input image, and a weight relating to the attribute of each region in the input image, and a second inference unit configured to infer an attribute of the object, based on an attribute and a weight that are inferred by the first inference unit.

Abstract: The present disclosure relates to systems and methods for imaging. The method may include obtaining a first image and topology data of an object, wherein the topology data may include first topology data and second topology data, and the first topology data may correspond to the second topology data. The method may also include determining a base material density image corresponding to the first image, determining a difference in the topology data based on the first topology data and the second topology data, and determining a second image corresponding to the first image based on the first image, the base material density image, and the difference in the topology data.

Abstract: This document describes an accelerator circuitry for facilitating acceleration of non-maximum suppression (NMS) for detection of objections within an image.

Abstract: Disclosed herein is a system and method for reducing false positives in object detection frameworks. A human reviews objects detected by the object detection framework and indicates whether the object is an object of a class for which the object detection framework is trained to detect. When an indication of a false positive been received, a feature representation of displayed object is stored in a gallery. During an inference or testing phase, the gallery is searched for a feature representation matching the feature representation of the detected objects, and, if a match is found, the detected object is deemed to be a false positive and is not identified as an object for which the object detection framework has been trained to detect.

Abstract: Systems and methods for machine-learning based cycle time tracking and reporting for vehicles are provided. A system includes a processor coupled with memory. The system identifies one or more models trained with machine learning relating to physical characteristics of vehicles and location designations associated with vehicle areas. The system receives, from one or more cameras, a video stream that captures a vehicle disposed in a vehicle area comprising a location designation. The system determines, based on an analysis of a plurality of frames of the video stream and via the one or more models, a type of the vehicle disposed in the vehicle area and a duration the vehicle is disposed in the vehicle area. The system performs, based on the type of the vehicle and a comparison of the duration of the vehicle with a threshold, an action to cause delivery of the vehicle from the vehicle area.

Abstract: Systems and methods for reconstructing field lines within video may include one or more processor configured by machine-readable instructions. Exemplary implementations may include identifying a plurality of candidate field lines in one or more frame of video; training a neural network to detect one or more field object; detecting, using the neural network, one or more field object in the one or more fame of video; assigning, using the detected one or more field object, a confidence value to each of the plurality of candidate field lines; removing one or more low confidence candidate field line from the plurality of candidate field lines; and retaining one or more high confidence candidate field line from the plurality of candidate field lines.

Abstract: A weed spot-spraying system is described for carrying out a spot-based weed spraying method based upon a classification value rendered from a sub-field image in accordance with a machine learning-based trained model applied by a processor to the sub-field image. The system includes a camera; a spray nozzle assembly including a spray nozzle; and a processor. The method carried out by the system includes acquiring, by the camera, a full field of view image of a crop floor. The method further includes extracting, from the full field of view image, a sub-field image corresponding to the spray nozzle positioned to provide a spray field extending over a part of the crop floor depicted in the sub-field image; rendering, by the processor in accordance with the machine learning-based trained model, a classification for the sub-field image; and selectively activating the spray nozzle in accordance with the classification for the sub-field image.

Abstract: Disclosed are a method for accelerating supervised training of a spiking neural network. The method includes measuring first and second membrane potentials for each time step during a training process, extracting distribution data of the first and second membrane potentials based on the first and second membrane potentials for the each time step, calculating a threshold value to be used in a subsequent training process based on the distribution data of the first and second membrane potentials, classifying images having no training contribution based on the threshold value calculated in a previous training process, and terminating the training at the time step based on determining that the image does not have the training contribution when a difference between the first and second membrane potentials in the time step is greater than the threshold value.

Abstract: A computer-implemented method for ascertaining an optimal architecture for a neural network that solves a given task in accordance with given boundary conditions and/or optimization goals. The method includes: providing a graph of the possible architectures of nodes and edges, wherein nodes correspond to data, edges correspond to parameterized operations to be carried out on the data, and a path which traverses the entire graph corresponds to an architecture; in a search phase, generating candidate architectures based on already known architectures, wherein the candidate architectures are similar but not identical to the known architectures in accordance with a predetermined criterion; evaluating the candidate architectures using the given boundary conditions and/or optimization goals; ascertaining a candidate architecture having the best rating as the sought optimal architecture.

Abstract: A system and methods are provided for generating photogrammetry data acquisition plans of target structures, including: establishing a location and orientation of the target structure; identifying on the target structure predetermined target objects designated for data acquisition; correlating shapes, locations, and orientations of the target objects with the shape, location, and orientation of the target structure; and applying the shapes, locations, and orientations of the target structure and of the target objects, to generate a data acquisition plan for drone photogrammetry of the target structure and of the target objects.

Abstract: A surgical instrument navigation system and method of use is provided that visually simulates a virtual volumetric scene of a body cavity of a patient from a point of view of a surgical instrument residing in the cavity of the patient, wherein the surgical instrument, as provided, may be a steerable surgical catheter with a biopsy device and/or a surgical catheter with a side-exiting medical instrument, among others. Additionally, systems, methods and devices are provided for forming a respiratory-gated point cloud of a patient's respiratory system and for placing a localization element in an organ of a patient.