Abstract: A system for evaluating a battery cell includes an imaging device configured to take an image of at least part of the battery cell, and a processor. The processor is configured to perform: determining a region of interest in the acquired image, reducing a sharpness of the acquired image to generate a reference image, comparing the acquired image and the reference image, and identifying a discontinuity of the battery cell based on a difference between the acquired image and the reference image.

Abstract: An electronic device according to various embodiments may include a communication circuit and a processor, wherein the processor is configured to receive a streaming image photographed by a first external electronic device through the communication circuit from a first external electronic device, identify a start of a fastening action of a user with respect to a target connector based on the streaming image, periodically obtain sensor data from a second external electronic device worn on the user based on identifying the start of the fastening action, identify an end of the fastening action with respect to the target connector based on the streaming image, and determine whether the target connector is fastened based on the sensor data obtained from the start of the fastening action to the end of the fastening action.

Abstract: A nuclear medicine diagnosis apparatus according to an embodiment includes a processing circuit. The processing circuit is configured: to obtain coincidence data including a direct incidence event to a gamma ray detector and a scattering event in a subject; to obtain an electron density function of the subject and geometric information of the gamma ray detector; to estimate a first probability value corresponding to the direct incidence event in the subject and a second probability value corresponding to the scattering event, based on one or both of the electron density function and the geometric information; and to reconstruct a Positron Emission Tomography (PET) image based on the first probability value, the second probability value, and the coincidence data. The processing circuit is configured to reconstruct the PET image based on a system matrix that is based on the first probability value and the second probability value.

Abstract: This disclosure is directed to, in part, a processing pipeline for detecting predefined activity using image data, identifying an item-of-interest and a location of the item-of-interest across frames of the image data, determining a trajectory of the item-of-interest, and determining an identifier of the item-of-interest and an action taken with respect to the item-of-interest. The processing pipeline may utilize one or more trained classifiers and, in some instances, additional data to identify items that are placed into or removed from a tote (e.g., basket, cart, or other receptacle) by users in material handling facilities as the users move around the material handling facilities.

Abstract: A method includes obtaining a batch of training data including multiple paired image-text pairs and multiple unpaired image-text pairs, where each paired image-text pair and each unpaired image-text pair includes an image and a text. The method also includes training a machine learning model using the training data based on an optimization of a combination of losses. The losses include, for each paired image-text pair, (i) a first multi-modal representation loss based on the paired image-text pair and (ii) a second multi-modal representation loss based on two or more unpaired image-text pairs, selected from among the multiple unpaired image-text pairs, wherein each of the two or more unpaired image-text pairs includes either the image or the text of the paired image-text pair.

Abstract: The present invention is directed to a helmet wearing determination system including a imaging means that is installed in a predetermined position and images a two-wheel vehicle that travels on a road; and a helmet wearing determination means that processes an image imaged by the imaging means, estimates a rider head region corresponding to a head of a person who rides on the two-wheel vehicle that travels on the road, compares image characteristics of the rider head region with image characteristics according to the head at a time when a helmet is worn or/and at a time when a helmet is not worn, and determines whether or not the rider wears the helmet.

Abstract: Described are methods for identifying the in-field positions of plant features on a plant by plant basis. These positions are determined based on images captured as a vehicle (e.g., tractor, sprayer, etc.) including one or more cameras travels through the field along a row of crops. The in-field positions of the plant features are useful for a variety of purposes including, for example, generating three-dimensional data models of plants growing in the field, assessing plant growth and phenotypic features, determining what kinds of treatments to apply including both where to apply the treatments and how much, determining whether to remove weeds or other undesirable plants, and so on.

Abstract: Examples described herein include methods and computing systems which may include examples of calculating risk scores for certain natural disasters perils based on machine learning model outputs. For example, a machine learning model may weight each of the pixels of a map in accordance with the set of weights associated with a structure, to calculate a risk score for a particular natural disaster peril associated with that structure. A plurality of risk selections may be provided to a user computing device for selection by a user, with those risk selections being associated with that risk score. Advantageously, the computing system facilitates the interaction of datasets with different measurement parameters in a machine learning model. In normalizing datasets before providing the datasets to input nodes of a machine learning model, a computing system may efficiently provide hazard and vulnerability outputs of the machine learning model.

Abstract: A three-dimensional measurement system projects patterned light onto a target object, images the target object from different viewpoints to obtain a first image and a second image, obtains first depth information using the first image with a spatially encoded pattern technique, defines a search range for a corresponding point based on parallax between the first image and the second image predicted from the first depth information, and obtains second depth information by performing stereo matching for the defined search range using the first image and the second image. The second depth information has a higher spatial resolution than the first depth information. The patterned light has a combined pattern combining a spatially encoded pattern including a plurality of unit elements arranged regularly and a random pattern including a plurality of random pieces arranged randomly. Each of the plurality of unit elements is an area corresponding to a depth value.

Abstract: This document discloses system, method, and computer program product embodiments for mitigating the addition of false object information to a track that provides a spatial description of an object, such as a track of radar data or lidar data. The system will analyze two or more frames captured in a relatively small time period and determine whether one or more parameters of an object detected in the frames remain consistent in a specified model. Models that the system may consider include a constant velocity model, a surface model, a constant speed rate model or a constant course rate model. If one or more parameters of the detected object are not consistent over the sequential frames in the specified model, the system may prune the track to exclude one or more of the sequential frames from the track.

Abstract: Disclosed is a system and method for operating an imaging system. The imaging system may move or be moved to acquire image data of a subject at different positions relative to the subject. The image data may, thereafter, be combined to form a single image.

Abstract: A method is described for providing a neural network for directly validating an environment map in a vehicle by means of sensor data. Valid or legitimate environment data is provided in a feature representation from map data and sensor data. Invalid or illegitimate environment data is provided in a feature representation from map data and sensor data. A neutral network is trained using the valid environment data and the invalid environment data.

Abstract: A computer implemented network for executing a self-supervised scene change detection method, wherein at least one image pair with images captured at different instances of time is processed to detect structural changes caused by an appearance or disappearance of an object in the image pair, and wherein a self-supervised pretraining method is employed that utilizes an unlabelled image pair or pairs to learn representations for scene change detection, and wherein the aligned image pair is subjected to a differencing based self-supervised pre-training method to maximize a correlation between changed regions in the images which provide the structural changes that occur in the image pairs.

Abstract: A system and method of detecting display fit measurements and/or ophthalmic measurements for a head mounted wearable computing device including a display device is provided. An image of a fitting frame worn by a user of the computing device is captured by the user, through an application running on the computing device. One or more visual markers each including a distinct pattern are detected in the image including the fitting frame. A model of the fitting frame, and configuration information associated with the fitting frame, are determined based on the detection of the pattern. A three-dimensional pose of the fitting frame is determined based on the detected visual marker(s) and patterns, and the configuration information associated with the fitting frame. The display device of the head mounted wearable computing device can then be configured based on the three-dimensional pose of the fitting frame as captured in the image.

Abstract: Various examples include a system and network to map of substrates within a substrate carrier (e.g., such as silicon wafers within a wafer cassette), and a classification of a state of each substrate, as well as the carrier in which the substrates are placed. In various examples provided herein, an image acquisition system, such as a camera, acquires multiple images of the substrates within the carrier. The image or images are then processed with a deep-convolutional neural-network to classify a state of the substrate relative to a substrate slot including empty slots, occupied slots (e.g., properly loaded slots), double-loaded slots, cross-slotted, and protruded (where a substrate is not fully loaded into a slot).

Abstract: An edge detecting device includes a feature extracting circuit configured to extract first and second feature data from an input image; a prototype generating circuit configured to generate prototype data using the first feature data and an input label, the prototype data including foreground and background information of an object; a region detecting circuit configured to generate a segmentation mask by detecting a region of an object using the first feature data and the prototype data; and an edge extracting circuit configured to generate an edge map by combining the segmentation mask and the second feature data.

Abstract: A method for computing, a computing device, and a computer-readable storage medium are provided. The method includes determining a first pixel block in a cache. The first pixel block is composed of a 2m row×2n column pixel matrix and includes original pixel data and pixel data related to the original pixel data. The first pixel block is read from the cache. At least part of the pixel data related to the original pixel data is used for padding related to the original pixel data. The original pixel data includes pixel data from the (n+1)th column to the 2nth column in the (m+1)th row to the 2mth row in the 2m row×2 n column pixel matrix. When reading data from the cache, pixel data that needs to be obtained after insert-zero and padding operations on the original pixel data in back propagation can be read at one time.

Abstract: In the athletic competition involving jumping such as figure skating, it is desired to measure jumping height and flight distance by calculating a trajectory of a moving object as a target from moving images captured by a monocular camera. Provided is a device, a method and a program to calculate the trajectory of the moving object jumping in three dimensions from information of a plurality of image frames captured by the monocular camera by detecting a specific point of the moving object, calculating an amount of change with respect to three-dimensional positions of the specific point in the consecutive image frames, and calculating the trajectory of the specific point from a positional relation between straight lines having the positions of the specific point and a curved line capable of expressing a parabolic motion passing through a take-off point and a landing point.

Abstract: A pure pose solution method and system for a multi-view camera pose and scene are provided. The method includes: a pure rotation recognition (PRR) step: performing PRR on all views, and marking views having a pure rotation abnormality, to obtain marked views and non-marked views; a global translation linear (GTL) calculation step: selecting one of the non-marked views as a reference view, constructing a constraint tr=0, constructing a GTL constraint, solving a global translation (I), reconstructing a global translation of the marked views according to tr and (I), and screening out a correct solution of the global translation; and a structure analytical reconstruction (SAR) step: performing analytical reconstruction on coordinates of all 3D points according to a correct solution of a global pose. The method and system can greatly improve the computational efficiency and robustness of the multi-view camera pose and scene structure reconstruction.

Abstract: A detection method for an image segmentation manner is provided. The detection method includes obtaining four sub-images input by a serial digital interface and formed by dividing an original image, extracting four groups of reference pixel points from the four sub-images according to a set extraction manner, calculating pixel difference values of pixel points according to the four groups of reference pixel points, and determining an image segmentation manner of the serial digital interface according to the pixel difference values.