Abstract: The invention is directed to a foodstuff extrusion portioning device and more specifically a cutter head assembly on such an extruder having a servo motor, a cutter shuttle coupled to a cutting element, a controller and being programmed via a product variable to provide a velocity profile. The cutter in the velocity profile has a first velocity and it reduces speed to a second velocity and goes more slowly through the last portion of the foodstuff. The at least two velocities being fully programmable and the controller can provide for instantaneous and additional programmed velocities throughout the cutting profile. The cutter further providing tilt control so it can drop the portion at the moment the portion detaches from the extruded foodstuff stream. It cuts and/or breaks off portions in a far more uniform and controllable manner to more accurately portion and better place the cut portions.

Abstract: Systems and methods for target inspection are provided. The system includes a camera, at least one sensor, and a controller. The camera acquires images of a target over time and the sensor(s) acquire motion data characterizing camera and target movement. The controller generates, using a first computer vision (CV) algorithm, an initial prediction and confidence level regarding an object of interest for a first image acquired at a first time. The controller also determines, using the motion data, a motion parameter characterizing relative motion between the camera and the target at the first time. The controller additionally receives a weighting based upon a second image acquired at a second time prior to the first time. The controller generates, using a second CV algorithm, a final prediction and confidence level for the first image based upon the first image, the initial prediction and confidence level, the motion parameter, and the weighting.

Abstract: A method of building a model of defect inspection for a light-emitting diode (LED) display is adapted to be implemented by a model-building system. The model-building system stores captured images respectively of LED displays that were displaying images. Each of the captured images corresponds to a status tag that indicates a status of the image being displayed by the respective one of the LED displays. The method includes: performing data preprocessing on the captured images to result in pieces of pre-processed data that respectively correspond to the captured images; and building a model of defect inspection by using an algorithm of machine learning based on the pieces of pre-processed data and the status tags.

Abstract: The invention relates to techniques for material testing of optical test pieces, for example of lenses. Angle-variable illumination, using a suitable illumination module, and/or angle-variable detection are carried out in order to create a digital contrast. The digital contrast can be, for example, a digital phase contrast. A defect detection algorithm for automated material testing based on a result image with digital contrast can be used. For example, an artificial neural network can be used.

Abstract: An information processing device includes a displacement calculation means and a motion estimation means. The displacement calculation means acquires time-series images obtained by capturing images of a measurement target region of a structure supported by a supporting member. The displacement calculation means calculates a three-dimensional displacement of the measurement target region from the acquired time-series images. The motion estimation means estimates a motion of the supporting member in the structure based on the three-dimensional displacement of the measurement target region.

Abstract: The defect inspection device includes a robot arm including a hold unit for holding an object and a driving unit for moving the object; a first camera unit photographing an exterior of the object; an illumination unit irradiating light to the exterior of the object; and a control unit determining whether there is a defect in the object based on an image of the object photographed by the first camera unit.

Abstract: This defect inspection apparatus (100) is provided with an excitation unit (1), a laser illumination unit (2), an interference unit (3), an imaging unit (35), and a control unit (4) for generating a moving image (61) related to the propagation of an elastic wave of an inspection target (7). The control unit is configured to perform control to display an identified measurement inappropriate region (81) in such a manner as to be distinguishable from a measurement appropriate region (82) in which the vibration state has been correctly acquired in the moving image (61).

Abstract: An embodiment provides a method of operating wire harness manufacturing equipment, including: adding, using the manufacturing equipment, an element to a wire to form a combination of the element and the wire; capturing, using an imaging device, one or more of an upper image and a lower image of the combination; analyzing, using one or more processors operatively coupled to the imaging device, the one or more of the upper image and the lower image to detect a defect; and thereafter indicating that the defect has been detected. Other embodiments are described and claimed.

Abstract: A system for classifying a pattern of interest (POI) on a semiconductor specimen is disclosed. The system comprises a processor and memory circuitry. The memory circuitry is configured to obtain a high-resolution image of the POI, and to generate data usable for classifying the POI in accordance with a defectiveness-related classification. To generate the data, a machine learning model is utilized that has been trained in accordance with training samples. The training samples include a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI. The training samples also include a label associated with the image, the label being derivative of low-resolution inspection of the respective training pattern.

Abstract: A method and a system for monitoring GIS instrument sulfur hexafluoride data based on edge computing are provided. The method includes the following specific steps: acquiring an instrument image; preprocessing the instrument image based on a generative adversarial network to obtain a first image; establishing a keypoint detection model to perform instrument reading on the first image to obtain reading data; and sending the reading data to a power supply system scheduling background, storing the data and performing early warning operation according to the reading data.

Abstract: A subtle defect detection method based on coarse-to-fine strategy, including: (S1) acquiring data of an image to be detected via a charge-coupled device (CCD) camera; (S2) constructing a defect area location network and preprocessing the image to be detected to initially determine a defect position; (S3) constructing a defect point detection network; and training the defect point detection network by using a defect segmentation loss function; and (S4) subjecting subtle defects in the image to be detected to quantitative extraction and segmentation via the defect point detection network.

Abstract: Current inspection processes employed for pipeline networks data acquisition aided with manually locating and recording defects/observations, thus leading labor intensive, prone to error and a time-consuming task thereby resulting in process inefficiencies. Embodiments of the present disclosure provide systems and methods for that leverage artificial intelligence/machine learning models and image processing techniques to automate log and data processing, reports and insights generation thereby reduce dependency on manual analysis, improve annual productivity of survey meterage and bring in process and cost efficiencies into overall asset health management for utilities, thereby enhancing accuracy in defect identification, analysis, classification thereof.

Abstract: A non-destructive testing (NDT) system can provide a tree model of an inspection on a display of an NDT device and on a web page configured in a web browser on a computing device coupled to the NDT device. Inspection data acquired using the NDT device can be provided in real-time as the inspection data is associated with a node configured in the tree model. The NDT system can generate an inspection tree model based on an inspection template including a template tree model. Defect properties, inspection instructions, and/or image transforms can be applied to nodes of the template tree model such that the generated inspection tree model includes the applied defect properties, inspection instructions, and/or image transforms, which can then be applied to the inspection data acquired at the inspection point location corresponding to each node.

Abstract: Techniques for facilitating testing analytics of imaging systems and methods using molds are provided. In one example, a system includes a mold temperature controller configured to apply a thermal signature to a mold of a target. The system further includes a focal plane array configured to capture an infrared image of the mold. The system further includes an image analytics device configured to determine thermal analytics associated with the mold based on the infrared image. Related devices and methods are also provided.

Abstract: A system for installation or repair work includes a mobile device and a central server. The mobile device includes a camera and a first processor. The first processor is configured to execute processing instructions including an algorithm to evaluate photographs recorded by the camera. The central server is configured to wirelessly communicate with the mobile device. The central server includes a second processor configured to execute control instructions stored on a second memory to cause the central server to: (i) receive at least one photograph evaluated by the first processor of the mobile device; (ii) perform machine learning using the at least one photograph to improve the algorithm used to evaluate the at least one photograph by the first processor; (iii) update the processing instructions using the improved algorithm; and (iv) transmit the updated processing instructions to the mobile device to enable evaluation of a subsequent photograph.

Abstract: A detection sensitivity is set such that a detection sensitivity for a defect corresponding to a predetermined local pattern in a reference image, which is a reference printing result, is lower than for a region other than the predetermined local pattern in the reference image. Image data representing an image of an inspection target is acquired and the image of the inspection target is inspected based on the reference image and the set detection sensitivity.

Abstract: A device receives an input image of a portion of a patient's body, and applies the input image to a feature extraction model, the feature extraction model comprising a trained machine learning model that is configured to generate an output that comprises, for each respective location of a plurality of locations in the input image, an indication that the input image contains an object of interest that is indicative of a presence of a disease state at the respective location. The device applies the output of the feature extraction model to a diagnostic model, the diagnostic model comprising a trained machine learning model that is configured to output a diagnosis of a disease condition in the patient based on the output of the feature extraction model. The device outputs the determined diagnosis of a disease condition in the patient obtained from the diagnostic model.

Abstract: Disclosed are systems, methods, and computer program products for tumor lesion identification, segmentation, tracking and analysis, wherein a 3D spatial distribution of the tumor lesions in a target organ forms a unique 3D point structure for a patient.

Abstract: A system and method for metal artifact avoidance in 3D x-ray imaging is provided. The method includes determining a 3D location of metal in an object or volume of interest to be scanned; estimating a source-detector orbit that will reduce the severity of metal artifacts; moving an imaging system to locations consistent with the source-detector orbit that was estimated; and scanning the object according to the source-detector orbit.

Abstract: A system and method includes acquisition of a plurality of images, each of the plurality of images representing radiotracer concentration in a respective volume at a respective time period, and training of a neural network, based on the plurality of images, to output data indicative of radiation dose associated with a first volume over a first time period based on a first image representing radiotracer concentration in the first volume over a second time period, the second time period being shorter than the first time period.

Abstract: Systems and methods are provided for automatically imaging and analyzing cell samples in an incubator. An actuated microscope operates to generate images of samples within wells of a sample container across days, weeks, or months. A plurality of images is generated for each scan of a particular well, and the images within such a scan are used to image and analysis metabolically active cells in the well. Tins analysis includes generating a “range image” by subtracting the minimum intensity value, across the scan, for each pixel from the maximum intensity value. This range image thus emphasizes cells or portions of cells that exhibit changes in activity over a scan period (e.g., neurons, myocytes, cardiomyocytes) while de-emphasizing regions that exhibit consistently high intensities when images (e.g., regions exhibiting a great deal of autofluorescence unrelated to cell activity).

Abstract: A preoperative survival prediction method and a computing device applying the method include constructing a data seta according to a plurality of enhanced medical images and a resection margin of each enhanced medical image and obtaining a plurality of training data sets from the constructed data set. For each training data set, multi-task prediction models are trained. A target multi-task prediction model is selected from the plurality, and a resection margin prediction value and a survival risk prediction value are obtained by predicting an enhanced medical image to be measured through the target multi-task prediction model. The multi-task prediction model more effectively captures the changes over time of the tumor in multiple stages, so as to enable a joint prediction of a resection margin prediction value and a survival risk prediction value.

Abstract: The present invention relates to a method for fabricating a dental restoration, comprising the steps of rendering (S101) a first digital tooth model with a first material combination for generating a first actual data set representing the optical properties of the first digital tooth model; determining (S102) a first deviation between a target data set and the first actual data set; rendering (S103) a second digital tooth model based on a second combination of materials to generate a second actual data set representing the optical properties of the second digital tooth model; determining (S104) a second deviation between the target data set and the second actual data set; and fabricating (S105) the dental restoration based on the first digital tooth model when the first deviation is less than the second deviation and fabricating the dental restoration based on the second digital tooth model when the second deviation is less than the first deviation.

Abstract: Method of processing a digital image relating to a histological tissue, to vary a color by forcing it towards a target average color of a digital reference image. The method includes a segmentation of the image regions that express a hue in a neighborhood of the hue of a comparison color and the calculation of an average coloration of the segmented area and if this average coloration differs under a predetermined threshold from the comparison color, then calculation and application of a corrective factor for each point of the image that expresses a hue around the hue of the average color, if instead the average color deviates beyond the predetermined one threshold from the comparison staining, then (Step 5) the average staining is set as the comparison staining and the segmentation is resumed from (Step 2).

Abstract: Methods and systems are disclosed for performing operations comprising: receiving a monocular image that includes a depiction of a whole body of a user; generating a segmentation of the whole body of the user based on the monocular image; accessing a video feed comprising a plurality of monocular images received prior to the monocular image; smoothing, using the video feed, the segmentation of the whole body generated based on the monocular image to provide a smoothed segmentation; and applying one or more visual effects to the monocular image based on the smoothed segmentation.

Abstract: Method for cutting a three-dimensional model of a dental scene, or “scene model.” The method includes acquiring a view of the scene model, called the “analysis view.” The method includes analyzing the analysis view by a neural network in order to identify, in the analysis view, at least one elementary zone representing an element of the dental scene, and assigning a value to at least one attribute of the elementary zone. The method includes identifying a region of the scene model represented by the elementary zone on the analysis view, and assigning, in the region, a value to an attribute of the scene model in accordance with the value of the attribute of the elementary zone.

Abstract: Improving the accuracy of predicted segmentation masks, including: extracting a ground-truth RGB image buffer and a binary contour image buffer from a ground-truth RGB image container for segmentation training; generating predicted segmentation masks from the ground-truth RGB image buffer; generating second binary contours from the predicted segmentation masks using a particular algorithm; computing a segmentation loss between manually-segmented masks of the ground-truth RGB image buffer and the predicted segmentation masks; computing a contour accuracy loss between contours of the binary contour image buffer and the binary contours of the predicted segmentation masks; computing a total loss as a weighted average of the segmentation loss and the contour accuracy loss; and generating improved binary contours by compensating the contours of the binary contour image buffer with the computed total loss, wherein the improved binary contours are used to improve the accuracy of the predicted segmentation masks.

Abstract: The invention discloses an attention-based joint image and feature adaptive semantic segmentation method. First, the image adaptation procedure is used to transform the source domain image Xs to a target-domain-like image Xs-t with an appearance similar with the target domain image Xt, to reduce the domain gap between the source domain and the target domain at the image appearance level; then using the feature adaptation procedure to align the features between Xs-t and Xt in the semantic prediction space and the image generation space, respectively, to extract the domain-invariant features, to reduce the domain difference between Xs-t and Xt. In addition, the present invention introduces an attention module in the feature adaptation procedure to help the feature adaptation procedure pay more attention to image regions worthy of attention. Finally, combining the image adaptation procedure and the feature adaptation procedure in the end-to-end manner.

Abstract: A method for foreground and background segmentation related to a virtual three-dimensional (3D) video conference, the method may include segmenting each image of multiple images of a video stream, to segments, each segment has one or more properties that are constant; determining temporal properties of the segments; and classifying each segment as a background segment or a foreground segment, based at least in part, on the temporal properties of the segments.

Abstract: A swing analysis system is disclosed herein. The swing analysis system includes a motion capture system comprising at least one motion capture device configured to detect motion of one or more body segments of a user and generate first output data, and further configured to detect the motion of a head and/or face of the user, a hand and/or fingers of the user, and/or an object being manipulated by the user, and generate second output data; and at least one data processing device operatively coupled to the motion capture system, the at least one data processing device configured to determine one or more swing performance parameters for the user using first positional information of the one or more body segments of the user, and/or second positional information for the head and/or face of the user, the hand and/or fingers of the user, and/or the object being manipulated by the user.

Abstract: A tracking device includes a processor including hardware. The processor is configured to: set a start frame and an end frame in a video including multiple frames; perform forward tracking in frames subsequent to the start frame based on a region of a tracking target in the start frame; perform backward tracking in frames previous to the end frame based on a region of the tracking target in the end frame; and generate a combined mask image by combining a forward mask image based on the forward tracking with a backward mask image based on the backward tracking.

Abstract: A production line monitoring method and a monitoring system thereof are provided. The monitoring system is configured to obtain a plurality of images of an operator, determine a motion type of the operator in the plurality of images based on an image recognition model, determine a time of occurrence and a motion period of the motion type, and record the time of occurrence and the motion period of the motion type.

Abstract: Methods, apparatus, and systems for sensing or tracking relative position between objects or locations. A digital camera or imager captures one or more fiducials in its field of view. By calibration and processing, the imaged fiducials can be identified and distinguished from other objects and background in camera space, and position of imaged fiducials in camera space relative to a reference can be translated to position of the actual fiducials in physical space. In one example, the fiducials are IR LEDs.

Abstract: Disclosed are systems and methods to detect and track an object across frames of a video. One of the disclosed methods includes: detecting a first group of one or more objects, using a first neural network, in each frame of the video, wherein each detected head of the first group comprises a leading and a trailing edge; grouping the leading and trailing edges of the one or more objects into groups of leading edges and groups of trailing edges based at least on coordinates of the leading and trailing edges; generating a list of no-edge-detect frames by identifying frames of the video missing a group of leading edges or a group of trailing edges; analyzing the no-edge-detect frames in the list of no-edge-detect frames, using an optical image classification engine, to detect a second group of one or more objects in the no-edge-detect frames; and merging the first and second groups of one or more objects to form a merged list of detected objects in the video.

Abstract: A processor-implemented method of tracking a target object includes: extracting a feature from frames of an input image; selecting one a neural network model from among a plurality of neural network models that is provided in advance based on a feature value range, based on a feature value of a target object that is included in the feature of a previous frame among the frames; and generating a bounding box of the target object included in a current frame among the frames, based on the selected neural network model.

Abstract: Accurate vehicle localization is arguably the most critical and fundamental task for autonomous vehicle navigation. While dense 3D point-cloud-based maps enable precise localization, they impose significant storage and transmission burdens when used in city-scale environments. A highly compressed representation for LiDAR maps, along with an efficient and robust real-time alignment algorithm for on-vehicle LiDAR scans, is proposed here. The proposed mapping framework, requires less than 0.1% of the storage space of the original 3D point cloud map. In essence, mapping framework emulates an original map through feature likelihood functions. In particular, the mapping framework models planar, pole and curb features. These three feature classes are long-term stable, distinct and common among vehicular roadways. Multiclass feature points are extracted from LiDAR scans through feature detection.

Abstract: A medical apparatus includes a registration tool, which includes a position sensor, A position-tracking system is configured to acquire position coordinates of the sensor in a first frame of reference defined by the position-tracking system. A processing unit is configured to receive 3D image data with respect to the body of the patient in a second frame of reference, to generate a 2D image of the surface of the patient based on the 3D image data, to render the 2D image to a display screen, and to superimpose onto the 2D image icons indicating locations of respective landmarks. The processing unit receives the position coordinates acquired by the position-tracking system while the registration tool contacts the locations on the patient corresponding to the icons on the display, and registers the first and second frames of reference by comparing the position coordinates to the three-dimensional image data.

Abstract: A time-of-flight camera includes a light emitter, a photo detector, and a controller. The time-of-flight camera may determine depth motion of an object by emitting light pulses, receiving reflected light pulses from the object, and accumulating a plurality of charges based on the reflected light pulses. The depth motion may be determined by the controller through analysis of the accumulated charges.

Abstract: An example method to control a light source in structured light imaging associated with a target includes obtaining two-dimensional image information and depth information of an image including the target and a scene, associating the depth information with pixels of the image, selecting a first set of pixels of the pixels based on a first set of the depth information and a second set of pixels of the pixels based on a second set of the depth information, calculating a first average pixel luminance (APL) of the first set of pixels and a total APL of the first and the second sets of pixels, obtaining a weighted APL based on the first APL and the total APL and controlling the light source based on the weighted APL.

Abstract: A system is provided for automatic identification and annotation of objects in a point cloud in real time. The system can automatically annotate a point cloud that identifies coordinates of objects in three-dimensional space while data is being collected for the point cloud. The system can train models of physical objects based on training data, and apply the models to point clouds that are generated by various point cloud generating devices to annotate the points in the point clouds with object identifiers. The solution of automatically annotated point cloud can be used for various applications, such as blueprints, map navigation, and determination of robotic movement in a warehouse.

Abstract: An information processing apparatus includes management circuitry that manages three-dimensional data obtained by a scanner in the order of obtaining, display controller circuitry that controls a display to show a three-dimensional image generated from combined data generated by combining a plurality of pieces of three-dimensional data, and input circuitry that accepts an operation by a user. When the input circuitry accepts an operation to select three-dimensional data, the management circuitry sets, from the selected three-dimensional data of last obtained three-dimensional data, data to be deleted. The display controller circuitry controls the display to show a three-dimensional image modified to exclude the data to be deleted.

Abstract: The present invention relates to a method and system for automatic localisation of static objects in an urban environment. More particularly, the present invention relates to the use of noisy 2-Dimensional (2D) image data to identify and determine 3-Dimensional (3D) positions of objects in large scale urban or city environments. Aspects and/or embodiments seek to provide a method, system, and vehicle for automatically locating static 3D objects in urban environments by using a voting-based triangulation technique. Aspects and/or embodiments also provide a method for updating map data after automatically new 3D static objects in an environment.

Abstract: A system includes a neural network implemented by one or more computers, in which the neural network includes an image depth prediction neural network and a camera motion estimation neural network. The neural network is configured to receive a sequence of images. The neural network is configured to process each image in the sequence of images using the image depth prediction neural network to generate, for each image, a respective depth output that characterizes a depth of the image, and to process a subset of images in the sequence of images using the camera motion estimation neural network to generate a camera motion output that characterizes the motion of a camera between the images in the subset. The image depth prediction neural network and the camera motion estimation neural network have been jointly trained using an unsupervised learning technique.

Abstract: A method includes obtaining a first plurality of feature vectors associated with a first image and a second plurality of feature vectors associated with a second image. The method also includes generating a plurality of transformed feature vectors by transforming each respective feature vector of the first plurality of feature vectors by a kernel matrix trained to define an elliptical inner product space. The method additionally includes generating a cost volume by determining, for each respective transformed feature vector of the plurality of transformed feature vectors, a plurality of inner products, wherein each respective inner product of the plurality of inner products is between the respective transformed feature vector and a corresponding candidate feature vector of a corresponding subset of the second plurality of feature vectors. The method further includes determining, based on the cost volume, a pixel correspondence between the first image and the second image.

Abstract: The present teaching relates to a method, system, medium, and implementation of processing image data in an autonomous driving vehicle. Sensor data acquired by one or more types of sensors deployed on the vehicle are continuously received. The sensor data provide different information about surrounding of the vehicle. Based on a first data set acquired by a first sensor of a first type of the one or more types of sensors at a specific time, an object is detected, where the first data set provides a first type of information about the surrounding of the vehicle. Depth information of the object is then estimated via object centric stereo at object level based on the object detected as well as a second data set acquired by a second sensor of the first type of the one or more types of sensors at the specific time. The second data set provides the first type of information about the surrounding of the vehicle with a different perspective as compared with the first data set.

Abstract: An apparatus and method for applying a multi-component curable composition is disclosed. The method for applying a multi-component curable composition includes a) applying a plurality of fluids constituting the multi-component curable composition to a surface of a bonding target object layer by layer, b) acquiring a two-dimensional image of all the fluids applied layer by layer using an image sensor, c) measuring a height of all the fluids applied layer by layer using a distance sensor, and d) calculating a total volume of the multi-component curable composition by using the two-dimensional image and height of the fluids obtained in b) and c).

Abstract: Embodiments of the present disclosure provide a method and apparatus for detecting a target object, an electronic device, and a computer readable storage medium. The method may include: acquiring a to-be-detected image; processing the to-be-detected image using an object detection model including a deformable convolutional layer to obtain an offset prediction result of the deformable convolutional layer; and adjusting a size of an anchor point using the offset prediction result, and determining position information of the target object in the to-be-detected image using the anchor point of the adjusted size.

Abstract: Systems and methods for reducing error from noisy data received from a high frequency sensor by fusing received input with data received from a low frequency sensor by collecting a first set of dynamic inputs from the high frequency sensor, collecting a correction input point from the low frequency sensor, and adjusting a propagation path of a second set of dynamic inputs from the high frequency sensor based on the correction input point either by full translation to the correction input point or dampened approach towards the correction input point.

Abstract: Provided is a system and method for fusion recognition using an active stick filter. The system for fusion recognition using the active stick filter includes a data input unit configured to receive input information for calibration between an image and a heterogeneous sensor, a matrix calculation unit configured to calculate a correlation for projection of information of the heterogeneous sensor, a projection unit configured to project the information of the heterogeneous sensor onto an image domain using the correlation, and a two-dimensional (2D) heterogeneous sensor fusion unit configured to perform stick calibration modeling and design and apply a stick calibration filter.

Abstract: Optical center is determined on a column-by-column and row-by-row basis by identifying brightest pixels in respective columns and rows. The brightest pixels in each column are identified and a line is fit to those pixels. Similarly, brightest pixels in each row are identified and a second line is fit to those pixels. The intersection of the two lines is the optical center.