Abstract: A method for depth estimation performed by a depth estimation system associated with an agent includes determining a first depth of a first image and a second depth of a second image, the first image and the second image being captured by a sensor associated with the agent. The method also includes generating a first 3D image of the first image based on the first depth, a first pose associated with the sensor, and the second image. The method further includes generating a warped depth image based on transforming the first depth in accordance with the first pose. The method also includes updating the first pose based on a second pose associated with the warped depth image and the second depth, and updating the first 3D image based on the updated first pose. The method further includes controlling an action of the agent based on the updated first 3D image.

Abstract: Synthesizing training data for training a change detection model includes receiving a patched image comprising a background image with a patch pasted into the background image. It further includes synthesizing a harmonized patched image at least in part by harmonizing the patched image using a machine learning model trained on the background image. It further includes providing as output a synthetic training sample usable to train a change detection model. The synthetic training sample includes a reference image, at least a portion of the harmonized patched image, and a corresponding mask.

Abstract: There is provided a method and a system configured obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, wherein the PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.

Abstract: A method includes receiving a three-dimensional image dataset of a surgical site of a patient. The method also includes segmenting one or more anatomical features of the surgical site based on the three-dimensional image dataset. The method also includes receiving a two-dimensional image of the surgical site of the patient and registering the two-dimensional image to an image from the three-dimensional image dataset. The method also includes displaying a two-dimensional representation of the segmented one or more anatomical features based on the registered two-dimensional image and the image from the three-dimensional image dataset.

Abstract: The present technology relates to an information processing apparatus, an information processing method, a program, a mobile-object control apparatus, and a mobile object that make it possible to improve the accuracy in recognizing a target object. An information processing apparatus includes a geometric transformation section that transforms at least one of a captured image or a sensor image to match coordinate systems of the captured image and the sensor image, the captured image being obtained by an image sensor, the sensor image indicating a sensing result of a sensor of which a sensing range at least partially overlaps a sensing range of the image sensor; and an object recognition section that performs processing of recognizing a target object on the basis of the captured image and sensor image of which the coordinate systems have been matched to each other. The present technology is applicable to, for example, a system used to recognize a target object around a vehicle.

Abstract: A method and device for image storage control, and a storage medium are disclosed. The method may include: collecting biometric information of a user during capturing an image; and performing storage and clearing processing on image data obtained by capturing according to storage control information corresponding to the biometric information.

Abstract: A method, an apparatus, and a device for image processing and a training method thereof are provided. The training method includes obtaining a sample image set, the sample image set comprising a first number of sample images; constructing an image feature set based on the sample image set, the image feature set comprising an image feature extracted from each of the sample images in the sample image set; obtaining a training image set, the training image set comprising a second number of training images; constructing multiple training image pairs based on the training image set and the image feature set; and training the image processing model based on the multiple training image pairs.

Abstract: A method may process an electronic image corresponding to a medical sample associated with a patient. The method may include receiving a selection of one or more artificial intelligence (AI) algorithms, receiving one or more whole slide images of a medical sample associated with a patient, performing a task on the whole slide images, using the one or more selected AI algorithms, the whole slide images being stored in a first container, the whole slide images being originated from a first user, the task comprising determining a characteristic of the medical sample in the whole slide images, based on the characteristic of the whole slide image, generating metadata associated with the whole slide image, and storing the metadata in a second container.

Abstract: Various methods and systems are provided for increasing an efficiency of operations for improving digital image quality. In an embodiment, a method for processing a digital image includes generating a higher resolution histogram from the digital image, the higher resolution histogram generated from a lower resolution histogram of raw image data of the digital image, filtering the higher resolution histogram, downsampling the filtered, higher resolution histogram to form a downsampled histogram, generating one or more picture quality adjustments based on the downsampled histogram, applying the one or more picture quality adjustments to the raw image data of the digital image to generate an improved digital image, the improved digital image having a higher picture quality than the digital image, and displaying the improved digital image on a display device.

Abstract: An apparatus for enhancing an input phase distribution (I(xi)) is configured to retrieve the input phase distribution (I(xi)) and compute a baseline estimate (ƒ(xi)) as an estimate of a baseline (I2 (xi)) in the input phase distribution (I(xi)). The apparatus is further configured to obtain an output phase distribution (O(xi)) based on the baseline estimate (ƒ(xi)) and the input phase distribution (I(xi)).

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that can facilitate pedestrian detection via a boundary cylinder model are addressed. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer-executable components can comprise a bounding cylinder model component that determines numerical values for height and radius parameters of a three-dimensional bounding cylinder model representing a pedestrian, and that generates the three-dimensional bounding cylinder model representing the pedestrian based on the numerical values.

Abstract: A method for preparing a deck for a process is disclosed. The deck can be prepared with any necessary components, and then an imaging device can capture an image of the deck. This image can be compared with a reference image and any differences identified. The differences can be indicated in the image and shown to an operator, such that the operator can correct any errors associated with the differences.

Abstract: A computer implemented method of training a neural network configured to combine a set of coefficients with respective input data values. So as to train a test implementation of the neural network, sparsity is applied to one or more of the coefficients according to a sparsity parameter, the sparsity parameter indicating a level of sparsity to be applied to the set of coefficients; the test implementation of the neural network is operated on training input data using the coefficients so as to form training output data; in dependence on the training output data, assessing the accuracy of the neural network; the sparsity parameter is updated in dependence on the accuracy of the neural network; and a runtime implementation of the neural network is configured in dependence on the updated sparsity parameter.

Abstract: A computer-implemented method for training a machine learning system including: initializing parameters of the machine learning system and a metaparameter. Repeatedly carrying out the following as a loop: providing a batch of training data points and manipulating the provided training data points or a training method for optimizing the parameters of the machine learning system or a structure of the machine learning system based on the metaparameter. Ascertaining a cost function as a function of instantaneous parameters of the machine learning system and of the instantaneous metaparameters. Adapting the instantaneous parameters as a function of an ascertained first gradient, which has been ascertained with respect to the instantaneous parameters via the ascertained cost function for the training data points, and adapting the metaparameter as a function of a second gradient, which has been ascertained with respect to the metaparameter used in the preceding step via the ascertained cost function.

Abstract: Fiducial coordinates are obtained by aligning template with region of interest extracted from a workpiece image. Image values in the region of interest are projected along a template axis and the project values evaluated to establish a fiducial location which can be used as a reference location for locating workpiece areas for ion beam milling or other processing.

Abstract: A method and system of recognizing object edges and a computer-readable storage medium are provided. The method includes: obtaining an input image; recognizing the input image and obtaining a relative position of each object vertex and a corresponding image vertex thereof; determining a reference position of each object vertex in the input image according to the relative position of each object vertex and the corresponding image vertex thereof; performing corner point detection in a predetermined area where the reference position of the object vertex is located for each object vertex; determining an actual position of each object vertex in the input image according to a result of the corner point detection; and sequentially connecting adjacent object vertices to form edge lines to obtain the edges of the object with edges in the input image according to the actual position of each object vertex in the input image.

Abstract: Techniques for facilitating non-uniformity mitigation for imaging systems and methods are provided. In one example, a method includes cropping an image based on a defect in the image to obtain a cropped image. The method further includes cropping a supplemental flat field correction (SFFC) map based on the defect in the image to obtain a cropped SFFC map. The method further includes determining a scaling value of a scaling term based at least on a cost function. The method further includes scaling the SFFC map based on the scaling value to obtain a scaled SFFC map. Related devices and systems are also provided.

Abstract: A non-transitory computer-readable recording medium stores an information processing program for causing a computer to execute processing including: acquiring data that indicates a relationship between element actions for a plurality of element actions in an object period; acquiring an effective time that corresponds to an object action; and searching for a combination of two or more element actions that form the object action among the plurality of element actions for each divided section set by dividing the object period according to the acquired effective time on the basis of the acquired data.

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure identify a plurality of candidate concepts in a knowledge graph (KG) that correspond to an image tag of an image; generate an image embedding of the image using a multi-modal encoder; generate a concept embedding for each of the plurality of candidate concepts using the multi-modal encoder; select a matching concept from the plurality of candidate concepts based on the image embedding and the concept embedding; and generate association data between the image and the matching concept.

Abstract: A method for determining a semantic free space in an environment of a vehicle comprises capturing a two dimensional visual image from the environment of the vehicle via a camera and determining a limitation of a free space within the visual image. Via a sensor, distance data of objects are captured and assigned to the visual image, and the limitation of the free space is transferred to a bird's-eye view based on the assigned distance data. For objects identified in the visual image a respective bounding box and a respective classification are determined. Objects limiting the free space are selected, and their bounding box is assigned to the limitation of the free space in the bird's-eye view. Finally, segments of the limitation of the free space are classified according to the classification of each bounding box of the selected objects.