Abstract: Methods and systems for presenting raw image data with a composite image are provided. For example, a method comprises displaying a composite image on a user interface; identifying an area of interest on the composite image; and displaying at least one source image on the user interface simultaneously with the composite image, the at least one source image containing at least one pixel in the area of interest. As another example, a method comprises projecting a composite image onto a representation of a three-dimensional (3D) surface displayed on a user interface to produce a 3D composite image and changing an orientation of the 3D composite image in response to a user input prior to identifying an area of interest on the 3D composite image. An image display system comprising a user interface is configured to simultaneously display the composite image and a plurality of source images.

Abstract: Techniques for improving image processing related to item deliveries are described. In an example, a computer system receives an image showing a drop-off of an item, the item associated with a delivery to a delivery location. The computer system inputs the image to a first artificial intelligence (AI) model. The computer system receives first data comprising an indication of whether the drop-off is correct from the first AI model. The computer system causes a presentation of the indication at a device associated with the delivery of the item to the delivery location.

Abstract: A system and method are provided for identifying a multimodal biomarker of a prognostic prediction, using a deep learning framework trained to analyze different modality data, including radiomic image data, pathology image data, and molecular image data to obtain unimodal embedding predictions from those modality data and generate multimodal embedding predictions, through application of a loss minimization and attention-based fusion processes.

Abstract: The method for configuring a wristwatch strap for a given wearer includes: firstly, acquiring (E1) at least one morphological data item (1; 2) of the wearer of the bracelet; intermediately, automatically determining (E2) at least one optimal bracelet configuration (c*) adapted to the wearer, including determining the adjustment of the length of the bracelet on the basis of the at least one morphological data item (1; 2), the intermediate determining (E2) of the at least one optimal configuration of the bracelet including determining configuration data; and secondly, preparing (E3) the bracelet of the wearer according to an optimal configuration determined in the preceding step.

Abstract: The present application provides an image detection method performed by a server. The method includes: intercepting a first image and a second image at a preset time interval from a video stream; performing pixel matching on the first image and the second image to obtain a value of total matching pixels between the first image and the second image; performing picture content detection on the second image in response to determining that the value of total matching pixels between the first image and the second image satisfies a preset matching condition based on the value of total matching pixels; and determining that the video stream is abnormal in response to determining that no picture content is in the second image by the picture content detection. In this way, an image recognition manner can be used to perform detection on image pictures of the video stream at the preset time interval.

Abstract: This invention provides a system and method for using an area scan sensor of a vision system, in conjunction with an encoder or other knowledge of motion, to capture an accurate measurement of an object larger than a single field of view (FOV) of the sensor. It identifies features/edges of the object, which are tracked from image to image, thereby providing a lightweight way to process the overall extents of the object for dimensioning purposes. Logic automatically determines if the object is longer than the FOV, and thereby causes a sequence of image acquisition snapshots to occur while the moving/conveyed object remains within the FOV until the object is no longer present in the FOV. At that point, acquisition ceases and the individual images are combined as segments in an overall image. These images can be processed to derive overall dimensions of the object based on input application details.

Abstract: The present application provides methods, devices, systems and non-transitory computer readable storage media for image processing. In an aspect, there is provided a computer-implemented method of image processing, the method comprising: receiving a low resolution image of an object; generating an edge map of the low resolution image by an edge extractor; and inputting the edge map and the low resolution image to a neural network to reconstruct a super resolution image of the object, wherein the neural network is trained using a multicomponent loss function.

Abstract: An example method includes obtaining a workpiece image of a semiconductor workpiece. The example method includes providing the workpiece image as input to a machine-learned encoding model. The example method includes obtaining an output from the machine-learned encoding model, the output includes an encoding corresponding to the semiconductor workpiece. The example method includes determining one or more characteristics of the semiconductor workpiece based at least in part on the encoding or modifying a semiconductor manufacturing process based at least in part on the encoding.

Abstract: Methods, systems and devices for use in semiconductor package manufacture of a package having a die and die substrate are provided. A method for use in semiconductor package manufacture includes steps of forming one or more wirebond interconnections between the die and the die substrate, capturing input data representative of wirebond interconnection features during inspection of the formed wirebond interconnections, and passing the captured input data to a machine learning (ML) engine. The method further includes processing the captured input data with the machine learning engine using a trained model to obtain an output array of data, evaluating the output array of data to determine a predicted radio-frequency (RF) performance rating, and outputting the predicted RF performance rating. Training data set processing may include applying image data and parameters to a multi-layer neural network to obtain a set of candidate ML models, and selecting an optimal ML model.

Abstract: A method for identifying a type of choroidal neovascularization in a retina of a human eye is disclosed, the method comprising receiving, in a processor, optical coherence tomography data of the eye; generating, in the processor, volume segments of the eye using the optical coherence tomography data and a neural network; and identifying, in the processor, the type of choroidal neovascularization in the eye, using the volume segments, the type of choroidal neovascularization comprising one or more of the following: classic choroidal neovascularization and occult choroidal neovascularization.

Abstract: One example provides a computing system comprising a depth sensor comprising a plurality of pixels, and a storage machine holding instructions executable by a logic machine to, for each pixel, make K phase measurements to form a set of noisy phase measurements, determine a location at which a projection line that passes through the set of noisy phase measurements in a K-dimensional phase space passes through a lower dimensional plane, the projection line being parallel to a noise free phase evolution line, compare the location to a plurality of independent terms of a predetermined matrix of points in the lower dimensional plane, locate a corresponding set of noiseless phase orders by using a selected set of independent terms to reference a look-up table, determine a distance value for the pixel based upon the corresponding set of noiseless phase orders, and output the distance value for the pixel.

Abstract: An image of a zone of inhibition (ZOI) plate is received. The image is processed to detect sutures within the image of the ZOI plate using computer vision, detect a contour of a ZOI within the image of the ZOI plate using computer vision, generate an overlay based on smoothening the contour of the ZOI and perform one or measurements on the image of the ZOI plate, wherein each of the one or more measurements comprises a distance between the sutures and the overlay.

Abstract: A detection unit detects a lesion candidate contained in display frame data (a tomographic image), using a machine learned detection model. An exclusion processing unit collates a feature vector of the detected lesion candidate with feature vectors registered in an exclusion database to determine whether the detected lesion candidate is an exclusion target. When the detected lesion candidate is an exclusion target, a mark display control unit restricts display of a mark for indicating a lesion candidate.

Abstract: In some implementations, a device may receive an image that depicts an environment associated with a vehicle. The device may partition the image into a plurality of subsections. The device may analyze the plurality of subsections to determine respective subsection information, wherein subsection information, for an individual subsection, indicates: a probability score that the subsection includes a line segment associated with an object class, a position of a representative point of the line segment, and a direction of the line segment. The device may identify, based on the respective subsection information of the plurality of subsections, a line associated with the object class that is associated with a set of subsections of the plurality of subsections. The device may perform one or more actions based on identifying the line associated with the object class.

Abstract: A method of identifying characters in images extracts features of a detection image including characters. Enhancement processing is performed on the detection image according to the features to obtain an enhanced image. Closed edges of the characters are detected in the enhanced image. First rectangular outlines of the characters are determined according to the closed edges. The first rectangular outlines are corrected to obtain second rectangular outlines. The characters are cropped from the detection image according to the second rectangular outlines. The method identifies characters in images accurately and rapidly.

Abstract: Broadly speaking, the present techniques generally relate to machine learning models comprising neural network layers, in which the quantisation level of each layer of the model can be independently selected at run-time. In particular, the present application relates to a computer-implemented method for analysing input data on a device using a trained machine learning, ML, model, comprising independently selecting a quantisation level for each of a plurality of network layers of the model at runtime. The present application also relates to a computer-implemented method of training a machine learning model so that the quantisation level of each of the plurality of network layers is independently selectable at runtime. A single trained model with a single set of weights can therefore be deployed, with the quantisation of each layer selected at runtime to suit the capabilities of the device and available resource.

Abstract: A computer-implemented method for modifying X-ray projection images of a subject region includes: generating a set of combined two-dimensional (2D) projections of a subject region, wherein each combined 2D projection includes one or more mask-bordering pixels and one or more mask-edge pixels; forming a three-dimensional (3D) matrix of the set of combined 2D projections; based on the 3D matrix, generating a linear algebraic system for determining pixel values for pixels indicated in a set of 2D projection metal masks, wherein a first change in slope of pixel value associated with a mask-edge pixel of a combined 2D projection is constrained to equal a second change in slope of pixel value associated with a mask-bordering pixel of a combined 2D projection; determining values for a variable vector of the linear algebraic system; and generating a set of inpainted 2D projections by modifying initial 2D projections with values for the variable vector.

Abstract: A computing system receives an indication of an energy project. The computing system monitors satellite image data of the energy project. The computing system determines that a first activity has been initiated for the energy project based on a change in satellite image data. The computing system monitors telemetry data associated with a location corresponding to the energy project and the satellite image data of the energy project. The computing system converts the telemetry data associated with the location corresponding to the energy project to a telemetry activity index. The computing system converts the satellite image data associated with the energy project to a satellite activity index. The computing system determines that the first activity has ended for the energy project based on changes in at least one of the telemetry activity index and the pad activity index.

Abstract: In example embodiments, a superelevation tool extracts and displays superelevation data from a 3D roadway model by accessing roadway meshes and a horizonal alignment from the 3D roadway model, extracting a plurality of template drops from the one or more roadway meshes at locations along the horizonal alignment to produce an ordered list of template drops and processing the template drops of the ordered list to identify top-facing roadway edges in each template drop that represent top pavement surface of the roadway at the location of the template drop, iteratively searching for a superelevation candidate and detecting superelevation data from the superelevation candidate at least in part by comparing cross-slopes of the top-facing roadway edges of consecutive template drops in the ordered list, wherein a superelevation candidate includes at least two or more template drops having cross-slopes that are locked, and providing a visualization of the detected superelevation data.

Abstract: Provided is a contour shape recognition method, including: sampling and extracting salient feature points of a contour of a shape sample; calculating a feature function of the shape sample at a semi-global scale by using three types of shape descriptors; dividing the scale with a single pixel as a spacing to acquire a shape feature function in a full-scale space; storing feature function values at various scales into a matrix to acquire three types of feature grayscale map representations of the shape sample in the full-scale space; synthesizing the three types of grayscale map representations of the shape sample, as three channels of RGB, into a color feature representation image; constructing a two-stream convolutional neural network by taking the shape sample and the feature representation image as inputs at the same time; and training the two-stream convolutional neural network, and inputting a test sample into a trained network model to achieve shape classification.