Abstract: An apparatus and method for loop plating, the apparatus including a scanning system, at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to generate, using an optical scanning system, a first scan of a plate digitally connected to a user profile, track content data of the plate as function of the first scan, classify the content data to the user profile, wherein classifying the content data includes generating a loop assessment, verify the content data as function of the classification, generate, using the optical scanning system, a second scan of the plate, identify plating feedback as a function of the second scan, and update the user profile based on the plating feedback, wherein updating the user profile includes generating a progress report.

Abstract: In some examples, an autonomous robotic welding system comprises a workspace including a part having a seam, a sensor configured to capture multiple images within the workspace, a robot configured to lay weld along the seam, and a controller. The controller is configured to identify the seam on the part in the workspace based on the multiple images, plan a path for the robot to follow when welding the seam, the path including multiple different configurations of the robot, and instruct the robot to weld the seam according to the planned path.

Abstract: Characteristics of a user's movement are evaluated based on performance of activities by a user within a field of view of a camera. Video data representing performance of a series of movements by the user is acquired by the camera. Pose data is determined based on the video data, the pose data representing positions of the user's body while performing the movements. The pose data is compared to a set of existing videos that correspond to known errors to identify errors performed by the user. The errors may be used to generate scores for various characteristics of the user's movement. Based on the errors, exercises or other activities to improve the movement of the user may be determined and included in an output presented to the user.

Abstract: A text correction method and apparatus can take advantage of a greatly reduced number of error-ground truth pairs to train a deep learning model. To generate these error-ground truth pairs, different characters in a ground truth word are replaced with a symbol, not appearing in any ground truth words, to generate error words which are paired with that ground truth word to provide error-ground truth word pairs. This process may be repeated for all ground truth words for which training is to be performed. In embodiments, pairs of characters in a ground truth word may be replaced with a symbol to generate the error words which are paired with that ground truth word to provide error-ground truth word pairs. Again, this process may be repeated for all ground truth words for which training is to be performed.

Abstract: The present application discloses an image processing method, an image processing device, and a computer device. The image processing method includes: acquiring a first to-be-processed chromaticity data set and a second to-be-processed chromaticity data set of a preset color of a to-be-processed picture, acquiring a Gaussian probability of the preset color, identifying a picture region in the to-be-processed picture that contains the preset color of the preset scene, and acquiring a color output correction value. The method of the present application can effectively improve a color cast phenomenon and a quality of the displayed picture.

Abstract: A process operable using a computerized system for providing one or more output images of the spinal region of a subject for which anatomical landmarks applicable for clinical assessment are labeled in a pre-trained neural network (120a,120b) for clinical assessment of malalignment of a spine of a subject, the computerized system (100a,100b) including an image data acquisition device (110a), a pre-trained neural network (120a,120b) and an output module (140a) operably interconnected together via a communication link, said process including the steps of (i) by an image data acquisition device (110a,), acquiring one or more data input sets indicative of the spinal region of a subject, wherein each data input set of the one or more data input sets is indicative of an optical image of said subject at one or more corresponding postures of the subject; (ii) in a pre-trained neural network (120a,120b), providing labels to anatomical landmarks of said one or more data input sets each of which is indicative of said op

Abstract: Described herein are systems and methods of training models to segment images. A device may identify a training dataset. The training dataset may include images each having a region of interest. The training dataset may include first annotations. The device may train, using the training dataset, an image segmentation model having parameters to generate a corresponding first segmented images. The device may provide the first segmented images for presentation on a user interface to obtain feedback. The device may receive, via the user interface, a feedback dataset including second annotations for at least a subset of the first segmented images. Each of the second annotations may label at least a second portion of the region of interest in a corresponding image of the subset. The device may retrain, using the feedback dataset received via the user interface, the image segmentation model.

Abstract: In an embodiment a method for detecting a presence of at least one object in a field of view of a time of flight sensor includes successively generating, by the time of flight sensor, histograms, each histogram comprising several classes associating a number of photons detected at a given acquisition period, adding several successively generated histograms so as to obtain a summed histogram and analyzing the summed histogram to detect the presence of at least one object in the field of view of the time of flight sensor.

Abstract: Provided is a method for detecting line-of-sight. The method for detecting line-of-sight includes: determining, based on a key feature point in a face image, a face posture and an eye pupil rotational displacement corresponding to the face image, wherein the eye pupil rotational displacement is a displacement of a pupil center relative to an eyeball center in the face image; and acquiring a line-of-sight direction of an actual face by back-projecting, based on a preset projection function and the face posture, the eye pupil rotational displacement to a three-dimensional space where the actual face is located.

Abstract: An image processing system for generating a display for a vehicle. The image processing system receives images, respectively, from a plurality of cameras mounted on the vehicle. The field of view of the plurality of cameras at least partially overlaps. The field of view of one or more of the plurality of cameras includes a region underneath the vehicle. The images are transformed to a common reference plane using a perspective transformation algorithm and based on intrinsic and extrinsic parameters of the plurality of cameras, to thereby obtain a projected image. The projected image is projected to the common reference plane. A display is generated based on the projected image. The display includes a synthetic depiction of the vehicle including an outer profile. The display includes an image area within the outer profile that is based on the projected image for the region underneath the vehicle.

Abstract: Provided are method and apparatus for data structuring of text. The apparatus for data structuring of text includes a data extraction unit configured to extract text and location information of the text from an image based on an optical character recognition (OCR) technique, a data processing unit configured to generate a text unit based on the text and the location information, a form classification unit configured to classify a form of the image based on the text, a labeling unit configured to label the text unit as first text, second text, and third text respectively corresponding to an item name, an item value, or others based on the classified form, a relationship identification unit configured to map and structure the second text corresponding to the first text, and a misrecognition correction unit configured to determine misrecognition of the first text and correct the first text determined to be misrecognized.

Abstract: A device may obtain field images of a tissue sample, apply, to the field images, spatial distortion and illumination-based corrections (including corrections for photobleaching of reagents) to derive processed field images, identify, in each processed field image, a primary area including data useful for cell or subcellular component characterization, identify, in the processed field images, areas that overlap with one another, and derive information regarding a spatial mapping of cell(s) and/or sub-cellular components of the tissue sample. Deriving the information may include performing segmentation based on the data included in the primary area of each processed field image, and obtaining flux measurements based on other data included in the overlapping areas.

Abstract: A system and method for automatically estimating a hepatorenal index (HRI) from ultrasound images s provided. The method includes acquiring a sequence of ultrasound image data until a desired ultrasound image view is obtained. The method includes segmenting, a liver and a renal cortex in the obtained ultrasound image view. The method includes identifying valid samples in the liver and the renal cortex in the obtained ultrasound image view by excluding invalid samples. The method includes automatically positioning a liver region of interest and a renal cortex region of interest in the obtained ultrasound image view based on the valid samples and at least one criterion. The method includes determining an HRI and causing a display system to present the HRI.

Abstract: Computer-implemented methods and systems are provided for generating encoded representations for multiple magnifications of one or more query images. An example method for involves operating a processor to obtain a query image and identify a set of anchor points within the query image. The processor is operable to generate a plurality of sub-images for a plurality of magnification levels for each anchor point. Each sub-image includes the anchor point and corresponds to a magnification level of the plurality of magnification levels. The processor is operable to, for each magnification level, apply an artificial neural network model to a group of sub-images having that magnification level to extract a feature vector representative of image characteristics of the query image at that magnification level; and to generate an encoded representation for multiple magnifications of the query image based on the feature vectors extracted for the plurality of magnification levels.

Abstract: An optical device includes an optical sensor, one or more diffusive optical elements disposed on a surface of a display screen and configured to diffuse and to distribute a first portion of light associated with a subject across an array of sensor elements of the optical sensor, and one or more processors. The display screen includes one or more transmissive components that allow the first portion of light to pass through the display screen via the surface of the display screen, and one or more blocking components that prevent a second portion of light associated with the subject from passing through the display screen. The one or more processors are configured to obtain, from the optical sensor, sensor data associated with the first portion of light, process the sensor data to determine an image of the subject, and perform one or more actions based on the image of the subject.

Abstract: Embodiments are directed to use of a rapid-test-validation computing device to determine if a result of a rapid test device is valid and identify the result. The rapid-test-validation computing device captures images of the rapid test device and employs a first artificial intelligence mechanism to determine if the rapid test device is properly aligned in the images. The rapid-test-validation computing device then employs a second artificial intelligence mechanism to determine if a result of the rapid test device is valid or invalid. If the result is valid, the rapid-test-validation computing device employs a third artificial intelligence mechanism to determine and present an objective output of the rapid test device result to a user; otherwise, the rapid-test-validation computing device presents a notification to the user that the rapid test device result is invalid.

Abstract: The present invention relates to an organ classification system, and the system for classifying the type of an organ may comprise: a search unit for searching an input image for a frame where an image regarding an organ starts, on the basis of a plurality of organ images pre-learned according to types of organs; an obtainment unit for obtaining an image of a found frame from the input image; and a storage unit for storing location information of the found frame in the input image.

Abstract: Provided is a liveness verification method and device. A liveness verification device acquires a first image and a second image, and select one or more liveness models based on respective analyses of the first image and the second image, including analyses based on an object part being detected in the first image and/or the second image, and to verify, using the selected one or more liveness models, a liveness of the object based on the first image and/or the second image. The first image may be a color image and the second image may be an Infrared image.

Abstract: An image processing method includes displaying a first image, where the first image has a first size. The method further includes receiving an operation of a user, where the operation of the user is any one of the following operations: a profile picture setting operation, a screen setting operation, a picture sharing operation, a contact list operation, or a picture sending operation. The method further includes displaying a second image, where the second image has a second size, the second size is less than the first size, and the second image includes salient content on the first image.

Abstract: Aspects of the present disclosure involve a system comprising a medium storing a program and method for machine-learning based selection of a representative video frame. The program and method provide for receiving a set of video frames; determining a first subset of frames by removing frames outside of an image quality threshold; determining a second subset by removing frames outside of an image stillness threshold; computing feature data for each frame in the second subset; providing, for each frame in the second subset, the feature data to a machine learning model (MLM), the MLM being configured to output a score for each frame in the second subset of frames based on the feature data, the MLM having been trained with a first set of images labeled based on aesthetics, and with a second set of images labeled based on image quality; and selecting a frame based on output scores.