Abstract: A method in an illustrative embodiment includes dividing a first image having a first resolution into a plurality of image patches, and converting each of the plurality of image patches into an image patch having a second resolution by using a plurality of candidate conversion models, the second resolution being higher than the first resolution. The method may include determining a plurality of quality factors corresponding to the plurality of candidate conversion models for each of the converted image patches. The method may include selecting a conversion model for each image patch from the plurality of candidate conversion models based on the plurality of quality factors and computation load factors of the plurality of candidate conversion models, and generating metadata for converting the first image into a second image having the second resolution based on position information of each image patch and the conversion model selected for each image patch.

Abstract: The present embodiments relate to a method and device. The method comprises obtaining at least one first point from at least one point of a point cloud by projecting said point of the point cloud onto a projection plane and obtaining at least one other point of the point cloud determined according to said at least one first point; determining and encoding at least one interpolation coding mode for said at least one first point based on at least one reconstructed point obtained from said at least one first point and at least one interpolation point defined by said at least one interpolation coding mode to approximate said at least one other point of the point cloud; and signaling said at least interpolation coding mode as values of image data.

Abstract: A system is provided that is configured to encode an image in accordance with a variable resolution image format. The variable resolution image format allows the specification of a number of windows in terms of their location and resolution. The image can be decomposed into a minimum number of square superpixels such that all specified windows are at the assigned resolution or better. By encoding one image where only critical portions are at the high resolution while less critical portions are at intermediate or lower resolutions, the number of bits that need to be transmitted from the system to a remote host subsystem can be dramatically reduced.

Abstract: A first image collected by an image collector is acquired. A map generated based on a correspondence between a distortion rate and a field of view of the image collector is acquired. The map includes a mapping relation between a raw pixel coordinate of a pixel in a raw image collected by the image collector and a target pixel coordinate that has been corrected. A second image that has been corrected is acquired by correcting the first image based on the map.

Abstract: Disclosed techniques for image processing three-dimensional image data include: obtaining three-dimensional image data representing contiguous slices parallel to a plane, constructing training data from the image data by, for each of a plurality of angles: rotating the image data in the plane to produce rotated image data, blurring the rotated image data in a dimension parallel to the plane to produce low resolution rotated image data, and introducing aliasing into the low resolution rotated image data in the dimension parallel to the plane to produce aliased low resolution rotated image data, training an anti-aliasing neural network with the aliased low resolution image data and the low resolution image data, training a super-resolution neural network with the aliased low resolution image data and the rotated image data, and processing the image data using the trained anti-aliasing neural network and the trained super-resolution neural network to produce processed image data.

Abstract: This production method of a trained model (10) includes a step of optimizing a training model (11) so that a value of a loss function (A) becomes small. The loss function (A) is configured to output a relatively large value in a case where the contrast of the predetermined area (PA) in a brightness-adjusted image adjusted in brightness becomes high with respect to training data (20) than in a case where the contrast of the predetermined area in the brightness-adjusted image (3) becomes low with respect to the training data.

Abstract: Disclosed is a method for automatic classification of pathological images based on a staining intensity matrix. This method directly extracts the staining intensity matrix irrelevant to a stain ratio, a staining platform, a scanning platform and some human factors in the pathological image as the feature information of classification, without restoring normalized stained images, while retaining all impurity-free information related to diagnosis. It avoids the phenomenon that the diagnostic effect of the existing computer-aided diagnosis method of pathological images based on the traditional color normalization method changes with the changes of the selected standard pathological sections. Moreover, it avoids the error introduced by the need to restore the stained image, and has a higher diagnostic accuracy and a more stable diagnostic effect. At the same time, the method can realize the diagnosis of pathological images in a shorter time, which is easy to realize and more practical.

Abstract: A management and control system is provided for a user to interface with an inspecting apparatus having at least one digital optical instrument. The management and control system comprises a processor configured to receive images from the at least one digital optical instrument, analyze the images, and transmit instructions to the inspecting apparatus, and a display configured to display analysis of the images wherein the user is capable of interfacing and providing instructions to the inspecting apparatus based on the analysis of the images, wherein the display simultaneously displays histograms and thumbnail-image generated in the processor based on the images. A method for controlling and managing the inspecting apparatus for items as well as an UI are disclosed.

Abstract: A system for inspecting a tube can comprise a first image acquisition device that is configured to capture at least one image of the first end of the tube. A second image acquisition device can be configured to capture at least one image of at least a portion of the outer surface of the circumferential wall of the tube. A first conveyor can be configured to position the tube so that the first image acquisition device can capture the at least one image of the first end of the tube and the second image acquisition device can capture the at least one image of at least a portion of the circumferential wall of the tube. A computing device can be configured to determine, based on the at least one image from the first image acquisition device or the at least one image from the second image acquisition device, if the tube has a flaw.

Abstract: According to one embodiment, a grain size estimation device includes an acquisition unit that acquires a captured image of a surface segment of an object inducing metal; and an estimation unit that estimates a grain size of the surface segment of the object indicated in the acquired image, based on a predictive model generated by machine learning using images of metal surfaces and grain sizes in the metal surfaces as training data.

Abstract: A defect characterization method includes: a first scanning image and target defect coordinates in the first scanning image are obtained; a first defect image is obtained according to the target defect coordinates in the first scanning image, the first defect image containing a defect area where a target defect is located and a noise area not containing the target defect; the noise area is marked, Automatic Defect Review (ADR) calculation is performed on the defect area, and a pixel level value of a defect in the defect area is obtained; coordinates of the defect with a maximum pixel level value are obtained, and a second defect image is obtained according to the coordinates of the defect with the maximum pixel level value; and the defect with the maximum pixel level value is classified according to the second defect image.

Abstract: A method for detecting data defects and a computing device applying the method obtains a test image for analysis. A field to which the test image relates is determined. Based on the field, a target convolutional layer is determined from a convolutional neural network. The target convolutional layer is used to extract features of the test image. A target score of the test image and a score threshold corresponding to the field are determined. If the target score is less than the score threshold, it is determined that the test image reveals defects, thereby improving an accuracy of defect detection.

Abstract: Provided is a veneer sorting control device including: a sorting condition setting unit 11 that sets sorting conditions for each of a plurality of kinds of defects so as to sort a veneer into a plurality of quality ranks; a defect detection unit 13 that detects the plurality of kinds of defects with respect to each of a plurality of pieces of veneer image data acquired from an image storage unit 100; a quality rank sorting unit 14 that sorts a plurality of the veneers into a plurality of quality ranks in correspondence with the sorting conditions which are set and defect detection states; a first totalization unit 15 that totalizes the number or a number ratio of the veneers in the plurality of quality ranks which are sorted; and a display control unit 17 that displays the totalization result on a screen. The number of the veneers sorted into the plurality of quality ranks can be confirmed by a simulation using the veneer image data stored in the image storage unit 100.

Abstract: Multimodal systems are provided for managing safety in an industrial environment. The system comprises: (a) a computer vision component for generating a first output data; (b) a real-time locating component for generating a second output data about an object within the industrial environment and a mobile tag device deployed to the object; (c) a LIDAR component for generating a third output data; and (d) an edge computing device connected to the computer vision component, the real-time locating component and the LIDAR component via a local network, and is configured to: (i) receive a data stream including the first output data, the second output data and the third output data, (ii) process the data stream using a machine learning algorithm trained model to generate a safety related result and feedback data, and (iii) deliver the feedback data to the object via the mobile tag device.

Abstract: To easily and correctly determine substantial identity of a plurality of images in each of which an object is placed, provided is an image judgement method including steps of obtaining first object data from a first image with a use of an R-CNN 12, which is a first machine learning model, where the first object data indicates an attribute and a layout of an object in the first image, obtaining second object data from a second image with a use of the R-CNN 12, where the second object data indicates an attribute and a layout of an object in the second image, and determining substantial identity of the first image and the second image based on the first object data and the second object data with a use of a CNN, which is a second machine learning model.

Abstract: A method for determining an expansion coefficient of a test material comprises: receiving first image data of a compound material, wherein the compound material comprises a plate and a layer of the test material, which is attached to the plate; receiving second image data of the compound material, which has been exposed to an environmental condition, before the second image data has been recorded; determining a measured deformation of the compound material by comparing the first image data and the second image data; and performing a simulated deformation of a model of the compound material exposed to the environmental condition and determining the expansion coefficient of the test material by varying the expansion coefficient until the simulate deformation conforms to the measured deformation.

Abstract: A spot-checking method for a visual inspection system includes obtaining a plurality of to-be-inspected images, inspecting the plurality of to-be-inspected images to obtain defect types and/or parameters of a target object in the plurality of to-be-inspected images, and confirming availability of the visual inspection system based on the defect types and/or the parameters.

Abstract: An apparatus includes at least one processor configured to obtain stereo-spatio-temporal data measurements of plants in a growing area. The stereo-spatio-temporal data measurements include (i) first spatio-temporal data measurements of the plants in the growing area and (ii) second spatio-temporal data measurements of the plants in the growing area. The at least one processor is also configured to analyze the stereo-spatio-temporal data measurements to identify one or more actual or potential problems associated with one or more of the plants. The at least one processor is further configured to generate a graphical user interface identifying at least one of the one or more actual or potential problems with the one or more plants. The first and second spatio-temporal data measurements of each stereo-spatio-temporal data measurement are associated with at least one common plant characteristic and different three-dimensional positions within the growing area taken at one or more known times.

Abstract: A system generates a context mask based on quantitative image data. The system obtains the quantitative image data which was captured via a quantitative imaging of a sample. The quantitative image data is compared to previous quantitative image data through application of the quantitative image data to a neural network trained using the previous quantitative image data and corresponding constructed context masks. The comparison generates the context mask for the quantitative image data. The context mask provides context for the quantitative parameter values that facilities characterization of the sample.

Abstract: A determination method of non-destructively and easily determining a state of an aggregate of a plurality of cells formed by three-dimensional culture is provided. A determination method according to the disclosed technology includes generating a phase difference image of an aggregate of a plurality of cells from a hologram obtained by imaging the aggregate, deriving a phase difference amount density by dividing a total phase difference amount that is a value obtained by integrating a phase difference amount of each of a plurality of pixels constituting the phase difference image by a volume of the aggregate, and determining a state of the aggregate on the basis of a time transition of the phase difference amount density.

Abstract: A method for characterising a perivascular region using medical imaging data of a subject. The method comprises calculating the value of a radiomic signature of the perivascular region using the medical imaging data. Also disclosed is a method for deriving a radiomic signature for predicting cardiovascular risk. The method comprises obtaining a radiomic dataset and using the radiomic dataset to construct a perivascular radiomic signature. Also disclosed are systems for performing the aforementioned methods.

Abstract: Techniques are described for tailoring automatic exposure control (AEC) settings to specific patient anatomies and clinical tasks. According to an embodiment, computer-implemented method comprises receiving one or more scout images captured of an anatomical region of a patient in association with performance of a computed tomography (CT) scan. The method further comprises employing a first machine learning model to estimate, based on the one or more scout images, expected organ doses representative of expected radiation doses exposed to organs in the anatomical region under different AEC patterns for the CT scan. The method can further comprises employing a second machine learning model to estimate, based on the one or more scout images, expected measures of image quality in target and background regions of scan images captured under the different AEC patterns, and determining an optimal AEC pattern based on the expected organ doses and the expected measures of image quality.

Abstract: The present disclosure relates to a deep learning neural network that can identify corpora lutea in the ovaries and a rules-based technique that can count the corpora lutea identified in the ovaries and infer an ovarian toxicity of a compound based on the count of the corpora lutea (CL). Particularly, aspects of the present disclosure are directed to obtaining a set of images of tissue slices from ovaries treated with an amount of a compound; generating, using a neural network model, the set of images with a bounding box around objects that are identified as the CL within the set of images based on coordinates predicted for the bounding box; counting the bounding boxes within the set of images to obtain a CL count for the ovaries; and determining an ovarian toxicity of the compound at the amount based on the CL count.

Abstract: A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a first neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a plurality of second models of a second neural network module, respectively, to obtain a plurality of prediction values corresponding to each of the plurality of second models and a model number predicting having cancer in the plurality of prediction values; and outputting a determined result based on the model number predicting having cancer and a number threshold value. Further, processes between the first model and the second models can be automated, thereby improving identification rate of pancreatic cancer.

Abstract: Embodiments relate to a system that generates a crop yield component map for a field. The system determines amounts of nitrogen applied to each portion of the field by a set of nitrogen applicator farming machines. The system accesses crop yield data associated with a crop that was grown in the field. The crop yield data was generated by a set of harvester farming machines that travelled through the field and harvested plant parts of the crop. The system determines, by analyzing the crop yield data, plant part metrics for the harvested plant parts in each field portion. The system generates a crop yield component map that maps, for each field portion, a plant part metric associated with the field portion and an amount of nitrogen applied to the field portion. The component map may then be provided for display.

Abstract: Some embodiments are directed to a system and computer-implemented method are provided for determining one or more regions for tissue dissection in a series of pathology slides using a series of images which represent a digitized version of the series of pathology slides. Annotations are obtained for at least two reference images, with each annotation representing a region for tissue dissection in the respective reference image. Annotations are then generated for intermediate images between the reference images on the basis of bidirectional image registration and the subsequent propagation of both annotations to each intermediate image, which annotations are then combined to obtain a combined annotation for each intermediate image. The above measures are well suited to generate annotations for series of pathology slides which contain tissue slices of a tissue of interest having a complex 3D shape.

Abstract: The subject disclosure presents systems and methods for automatically selecting meaningful regions on a whole-slide image and performing quality control on the resulting collection of FOVs. Density maps may be generated quantifying the local density of detection results. The heat maps as well as combinations of maps (such as a local sum, ratio, etc.) may be provided as input into an automated FOV selection operation. The selection operation may select regions of each heat map that represent extreme and average representative regions, based on one or more rules. One or more rules may be defined in order to generate the list of candidate FOVs. The rules may generally be formulated such that FOVs chosen for quality control are the ones that require the most scrutiny and will benefit the most from an assessment by an expert observer.

Abstract: Aspects of the technology described herein relate to receiving an ultrasound image, automatically determining a location of a specific point on an anatomical structure depicted in the ultrasound image, and displaying an indicator of the location of the specific point on the anatomical structure on the ultrasound image. In some embodiments, the anatomical structure is a bladder. In some embodiments, the specific point is the centroid. In some embodiments, a statistical model determines the specific point. The indicator may be, for example, a symbol located at the specific point, a horizontal line extending through the specific point from one edge of the anatomical structure to another, and/or a vertical line extending through the specific point from one edge of the anatomical structure to another.

Abstract: A method for evaluating the effectiveness of a skin treatment on a skin feature includes correcting the color of a first image before the skin treatment, correcting the color of a second image after the skin treatment, determining the sizes of the skin feature in the first and second images, and comparing the corrected colors and sizes of the skin feature in the first and second images. In some embodiments, the first and second images include the skin feature and an indicator adjacent to the skin feature, where the indicator has a standard color and a known size.

Abstract: An image segmentation method system, the system comprising: a training subsystem configured to train a segmentation machine learning model using annotated training data comprising images associated with respective segmentation annotations, so as to generate a trained segmentation machine learning model; a model evaluator; and a segmentation subsystem configured to perform segmentation of a structure or material in an image using the trained segmentation machine learning model. The model evaluator is configured to evaluate the segmentation machine learning model by (i) controlling the segmentation subsystem to segment at least one evaluation image associated with an existing segmentation annotation using the segmentation machine learning model and thereby generate a segmentation of the annotated evaluation image, and (ii) forming a comparison of the segmentation of the annotated evaluation image and the existing segmentation annotation.

Abstract: Embodiments of this application disclose methods, systems, and devices for image region localization and medical image processing. In one aspect, a method comprises acquiring three-dimensional images of a target body part of a patient. The three-dimensional images comprise a plurality of magnetic resonant imaging (MRI) modalities. The method comprises registering a first image set of a first modality with a second image set of a second modality. After the registering, image features of the three-dimensional images are extracted. The image features are fused to obtain fused features. The method also comprises determining voxel types corresponding to voxels in the three-dimensional images according to the fused features. The method also comprises selecting, from the three-dimensional images, target voxels having a preset voxel type, obtaining position information of the target voxels, and localizing a target region within the target body part based on the position information of the target voxels.

Abstract: A computer-implemented method of annotating conventional retina images, the method comprising: receiving a conventional image of a retina, the conventional retina image captured using an image capture device; receiving an associated crosssectional image of said retina, the cross-sectional image captured using a crosssectional imaging system; determining a disease location in an image plane of the cross-sectional image; and generating annotation data for annotating the disease location in an image plane of the conventional image, by projecting the disease location from the image plane of the cross-sectional image into to the image plane of the conventional image, based on a known mapping between the cross-sectional image and the conventional image.

Abstract: A visibility determination system determines a visibility condition based on an image taken by a camera. The visibility determination system includes: an acquisition part, configured to acquire a first extracted image obtained by extracting an edge of an imaging target in a first image taken by the camera; and a determination part, configured to determine visibility based on the first extracted image and a second extracted image that is obtained by extracting the edge of the imaging target in a second image taken by the camera at a different time from the first image.

Abstract: A heterogeneous image registration method includes: performing edge detection on collected images, in combination with a curvature scale space strategy to extract contour curved segments in an edge image. Implementing a feature point detection strategy based on global and local curvature detecting feature points in the contour curved segments, and obtaining the nearest minimum local curvature of the feature points pointing to starting and end points of the contour, respectively. Calculating the number of neighborhood sampling points and neighborhood auxiliary feature points of neighborhoods on both edges of each of the feature points according to the nearest minimum local curvature. Using neighborhood auxiliary feature points and feature points to form a feature triangle, calculating an angle bisector vector and a main direction corresponding to the feature point in the feature triangle.

Abstract: A method and system are for analyzing a plurality of interconnected blood vessels, preferably for providing output data for the analysis of a plurality of interconnected blood vessels, e.g. for vessel segmentation, vessel labeling, and vessel occlusion detection. The output data include a graphical representation of a vessel tree model of a patient. The method of an embodiment, in the most general terms includes receiving input data, e.g. via an interface; generating output data by applying algorithmic operations to the input data; and providing the output data, e.g. via an interface.

Abstract: Various disclosed embodiments are directed to refining or correcting individual semantic segmentation/instance segmentation masks that have already been produced by baseline models in order to generate a final coherent panoptic segmentation map. Specifically, a refinement model, such as an encoder-decoder-based neural network, generates or predicts various data objects, such as foreground masks, bounding box offset maps, center maps, center offset maps, and coordinate convolution. This, among other functionality described herein, improves the inaccuracies and computing resource consumption of existing technologies.

Abstract: An image foreground segmentation algorithm based on edge knowledge transformation includes the following steps: 1) construction of an image segmentation framework with edge self-supervised mechanism; 2) construction of an inner edge and outer edge discriminator; 3) generation of pseudo-segmented triplet data; 4) two edge adversary foreground segmentation guided by a very few labeled samples of the target category. According to the image foreground segmentation algorithm based on edge knowledge transformation established in the above steps, under the guidance of a very small number of labeled segmentation samples of the target category, the inner and outer edge discrimination network transforms edge knowledge of a large amount of open-source labeled data of a non-target category into a foreground segmentation network for the target category image by the way of adversary of segmentation results, and realize the segmentation of foreground target category objects of the image.

Abstract: A computer-implemented neural network system for decomposing input video data. A video data input receives a sequence of video image frames. The sequence is encoded, using a 3D spatio-temporal encoder neural network, into a set of latent variables representing a compressed version of the sequence. A 3D spatio-temporal decoder neural network processes the set of latent variables to generate two or more sets of decomposed video data; these may be stored, communicated, and/or made available to a user interface. Input video including undesired features such as reflections, shadows, and occlusions may thus be decomposed into two or more video sequences, one in which the undesired features are suppressed, and another containing the undesired features.

Abstract: A video target tracking method is provided to a computing device, the method includes: obtaining a partial detection map corresponding to a target image frame in a to-be-detected video; obtaining a relative motion saliency map corresponding to the target image frame; determining constraint information corresponding to the target image frame according to the partial detection map and the relative motion saliency map; adjusting a parameter of an image segmentation model by using the constraint information, to obtain an adjusted image segmentation model; and extracting a target object from the target image frame by using the adjusted image segmentation model.

Abstract: An apparatus is configured to perform a tracking process using a first frame image and a second frame image separated from the first frame image by a predetermined period of time in a video image, and includes a detection unit configured to detect a subject as a tracking target from each of the first frame image and the second frame image, a first determination unit configured to determine whether the tracking targets are in a moving state or in a non-moving state in each combination of the tracking target detected from the first frame image and the tracking target detected from the second frame image, and a second determination unit configured to perform matching determination to determine whether the combination is a combination of identical tracking targets if the combination of the tracking targets is a combination of tracking targets in the non-moving state.

Abstract: An information processing device includes a reference image generation unit that generates a reference image by using a partial area including a head of a tracking target in a whole-body image of the tracking target, a comparative image generation unit that generates a comparative image to be used for comparison with the reference image by using the partial area containing the head of the tracking target in a tracking image of the tracking target where feet are not contained, an adjustment unit that compares the reference image with the comparative image, and adjusts a position of the head of the tracking target in the comparative image, and a feet estimation unit that estimates a position of the feet of the tracking target in the tracking image by using the comparative image where the position of the head of the tracking target is adjusted.

Abstract: A unit derives displacement amounts of feature points in an entire region of a face shape. A unit derives separated displacement amounts, each of which is a displacement amount of each feature point for every deformation pattern of a partial region of the face shape, based on the displacement amounts of the feature points in the entire region of the face shape and deformation degrees for every deformation pattern of the partial regions. A unit derives normalized and separated displacement amounts, which are obtained by normalizing the separated displacement amounts, for every deformation pattern of the partial regions. A unit generates weight values of the feature points in the entire region of the face shape based on the normalized and separated displacement amounts for every deformation pattern of the partial regions of the face shape and the deformation degrees for every deformation pattern of the partial regions.

Abstract: A method of determining macrotexture of an object is disclosed which includes obtaining a plurality of stereo images from an object by an imaging device, generating a coordinate system for each image of the plurality of stereo images, detecting one or more keypoints each having a coordinate in each image of the plurality of stereo images, wherein the coordinate system is based on a plurality of ground control points (GCPs) with apriori position knowledge of each of the plurality of GCPs, generating a sparse point cloud based on the one or more keypoints, reconstructing a 3D dense point cloud of the object based on the generated sparse point cloud and based on neighboring pixels of each of the one or more keypoints and calculating the coordinates of each pixel of the 3D dense point cloud, and obtaining the macrotexture based on the reconstructed 3D dense point cloud of the object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining estimated ground truth object keypoint labels for sensor readings of objects. In one aspect, a method comprises obtaining a plurality of sets of label data for a sensor reading of an object; obtaining respective quality control data corresponding to each of the plurality of sets of label data, the respective quality control data comprising: data indicating whether the labeled location of the first object keypoint in the corresponding set of label data is accurate; and determining an estimated ground truth location for the first object keypoint in the sensor data keypoint from (i) the labeled locations that were indicated as accurate by the corresponding quality control data and (ii) not from the labeled locations that were indicated as not accurate by the corresponding quality control data.

Abstract: A computer-implemented method for 3D scanning of a real object with a camera having a 3D position including receiving, from the camera, an image of the real object, displaying on a screen, in an augmented reality view, the image of the real object enclosed within a virtual 3D box and, superimposed to the real object, a virtual structure made of a set of planar tiles, and being anchored to the virtual 3D box, each tile corresponding to a predetermined pose of the camera, detecting that a tile is pointed at with the camera; acquiring, from the camera, a frame of the virtual 3D box, thereby validating said tile, said frame being a projection of the virtual 3D box on the image, iterating for different 3D positions of the camera, until a sufficient number of tiles is validated for scanning the real object, and implementing a 3D reconstruction algorithm with all captured frames.

Abstract: Methods and apparatus for processing images captured by cameras for use in stereo depth determinations are described and/or for using depth information generated by processing such images is described. Images are captured using a reference camera and at least one additional camera. Filtering is implemented as part of a patch matching process to reduce the risk of erroneous matches, e.g., due to image blur in one image but not another. The filtering may be, and sometimes is, implemented on a patch basis. A candidate patch is generated by filtering a portion of an image captured by a camera, e.g., a second camera by performing a sharpening or blurring operation to a portion of the image to generate a candidate patch. The amount of blurring or sharpening that is applied to generate the candidate patch depends, in some embodiments, on the relative difference between the candidate patch and the reference patch.

Abstract: Systems and methods are provided that include a plurality of sensors, communicatively coupled to one another, to periodically transmit positional location information. A digital image capture device, communicatively coupled to the plurality of sensors, may capture an image of a scene which includes at least one of the plurality of sensors. A processor, communicatively coupled to the digital capture device, may determine a measurement of a least one object in the captured image of the scene, where the measurement of the at least one object is based at least in part on the positional location information received by the digital image capture device at the time that the image of the scene is captured. A display device, communicatively coupled to the processor, may display the determined measurements of the at least one object.

Abstract: Remaining material on a material package and its location may be determined. First, image data associated with a material package may be received by a server. Next, material package data associated with the material package may be received. A Machine Learning (ML) model may then be used to determine an estimated remaining amount of material associated with the material package based upon input derived from the image data and the material package data.

Abstract: Systems, methods, devices and computer readable media for determining a geographical location of a license plate are described herein. A first image of a license plate is acquired by a first image acquisition device of a camera unit and a second image of the license plate is acquired by a second image acquisition device of the camera unit. A three-dimensional position of the license plate relative to the camera unit is determined based on stereoscopic image processing of the first image and the second image. A geographical location of the camera unit is obtained. A geographical location of the license plate is determined from the three-dimensional position of the license plate relative to the camera unit and the geographical location of the camera unit. Other systems, methods, devices and computer readable media for detecting a license plate and identifying a license plate are described herein.

Abstract: Systems and methods detect the posture of a user of an IHS (Information Handling System). An image is generated of a user and of the physical environment in which the user is operating the IHS. The image is processed to generate segregated images of the user and of the physical environment. The segregated image of the physical environment is classified as corresponding to a known environment in which the user has operated the IHS and that is associated with a probability of an ergonomic posture being used while in that particular environment. The segregated image of the user is processed to determine a physical posture of the user relative to the IHS. An ergonomic risk score is generated based on deviations of the user's posture from an ideal posture. The ergonomic risk score is scaled based on the probability of an ergonomic posture being used, due to the environment.