Abstract: Geometric extraction is performed on an unstructured document by recognizing textual blocks on at least a portion of a page of the unstructured document, generating bounding boxes that surround and correspond to the textual blocks, determining search paths having coordinates of two endpoints and connecting at least two bounding boxes, and generating a graph representation of the at least a portion of the page, the graph representation including the plurality of textual blocks, the coordinates of the vertices of each bounding box and the coordinates of the two endpoints of each search path.

Abstract: System, methods, and other embodiments described herein relate to a manner of training a depth prediction system using bounding boxes. In one embodiment, a method includes segmenting an image to mask areas beyond bounding boxes and identify unmasked areas within the bounding boxes. The method also includes training a depth model using depth losses from comparing weighted points associated with pixels of the image within the unmasked areas to ground-truth depth. The method also includes providing the depth model for object detection.

Abstract: The field of the invention relates to a system to provide artificial intelligence processing of a dental image for determining a relative health risk, i.e. the probability of getting a human disease based on dental x-ray pathologies. The dental image or image (received from a source such as an x-ray, a camera, or an image capturing device) may be matched to a known computer stored genomic pathology dataset, such as a DNA dataset or a RNA dataset. Anatomic variances on a dental x-ray, such as periodontitis (bone disease associated with teeth), can be matched and identified to a known genomic pathology dataset, such as a diabetes DNA dataset, by an artificial intelligence system to produce at least one of: a relative pathology health risk, a treatment recommendation, a product recommendation for an individual patient.

Abstract: A system and method for video authentication may apply machine learning to analyze whether a person's face captured by live video matches a face in a photo ID captured by live video and to analyze other features based on a video session with the person. For example, machine learning may be applied to analyze a set of features indicating whether the person is a real, live person (as opposed to a photo image held up over the person's face in the video, etc.). Finally, the machine learning may be applied to analyze a set of features to determine whether a lower probability prediction that the person's face captured by live video matches a face in a photo ID captured by live video should be either pass authentication (due to one or more features/circumstances mitigating the lower probability) or fail authentication (due to one or more features not mitigating the lower probability). In such a situation, the set of features may indicate that mitigating factors/conditions exist that can offset the lower probability.

Abstract: The present disclosure relates to an image processing method and a system thereof. The method may include: obtaining an image including at least one pixel or voxel; causing, via an interface, at least one interface element corresponding to at least one candidate texture model to be displayed, wherein the at least one candidate texture model is selected from a library including a plurality of candidate texture models that correspond to a plurality of candidate categories; receiving, via the at least one interface element, a selection of a texture model from the at least one candidate texture model; associating the texture model with the image; determining an output parameter of the at least one pixel or voxel based on the texture model; and generating an output image based on the output parameter of the at least one pixel or voxel.

Abstract: The present disclosure relates to methods and apparatuses for processing an image, training an image recognition network and recognizing an image. The method of processing an image includes: obtaining a plurality of original images from an original image set, where at least one of the plurality of original images includes an annotation area; obtaining at least one first image by splicing the plurality of original images; for each of the at least one first image, adjusting a shape and/or size of the first image based on the plurality of original images to form a second image; obtaining respective positions of the at least one annotation area in the second image by converting respective positions of the at least one annotation area in the plurality of original images.

Abstract: An apparatus includes at least one processor configured to execute a plurality of tasks including a first normal acquiring task configured to acquire first normal information of an object, a designated area acquiring task configured to acquire a designated portion in the object, the designated area being designated by a user, a second normal acquiring task configured to acquire second normal information of the object, the second normal information being normal information having a lower frequency than a frequency of the first normal information, a virtual light source determining task configured to determine a virtual light source condition based on the second normal information corresponding to the designated portion, and a rendering task configured to generate a rendering image using the first normal information and the virtual light source condition.

Abstract: Embodiments are directed to techniques for interacting with a hierarchical video segmentation using a metadata panel with a composite list of video metadata. The composite list is segmented into selectable metadata segments at locations corresponding to boundaries of video segments defined by a hierarchical segmentation. In some embodiments, the finest level of a hierarchical segmentation identifies the smallest interaction unit of a video—semantically defined video segments of unequal duration called clip atoms, and higher levels cluster the clip atoms into coarser sets of video segments. One or more metadata segments can be selected in various ways, such as by clicking or tapping on a metadata segment or by performing a metadata search. When a metadata segment is selected, a corresponding video segment is emphasized on the video timeline, a playback cursor is moved to the first video frame of the video segment, and the first video frame is presented.

Abstract: A method and apparatus for sorting is described, and which includes providing a product stream formed of individual objects of interest having feature aspects which can be detected; generating multiple images of each of the respective objects of interest; classifying the feature aspects of the objects of interest; identifying complementary images by analyzing some of the multiplicity of images; fusing the complementary images to form an aggregated region representation of the complementary images; and sorting the respective objects of interest based at least in part upon the aggregated region representation which is formed.

Abstract: The invention relates to a method for processing large multiplexed image data of a biological sample, the method comprising the steps of, recording a plurality of images of a biological sample, wherein the plurality of images comprises images having a different entity of the biological sample targeted with a predefined stain, determining spatially corresponding image pixels in the plurality of registered images, associating the spatially corresponding image pixels to a pixel profile, wherein each pixel profile comprises the pixel values of the spatially corresponding pixels and wherein the pixel profile is associated with the respective image coordinate of the spatially corresponding pixels, pooling the pixel profiles by means of a clustering method configured to determine pixel profiles with similar values, and thereby generating a plurality of clusters, each comprising pixel profiles with similar pixel values, for each cluster assigning a cluster value to the image coordinate of the pixel profiles comprised

Abstract: A classification device includes: an imaging unit that images a plurality of granular classification target objects each of which is disposed in one of a plurality of holes provided in a tray to acquire an image including the plurality of classification target objects; an acquisition unit that acquires images of each of the plurality of classification target objects by cutting out the images from the acquired image; and a sorting unit that classifies the acquired images of each of the plurality of classification target objects by using machine learning results and sorts the plurality of classification target objects based on the classification result.

Abstract: A system for determining a length of an object includes a rod, an imaging device couplable to the rod, a laser rangefinder, one or more sensors, and a processor. The processor can receive image data, the orientation of the rod relative to the surface, and the distance to the surface from the imaging device and can determine the length of the rod. A system for surveying trees within a plot includes an imaging device, one or more sensors for measuring at least one of a pitch, a roll, or a compass bearing of the imaging device, and one or more processors configured to receive images of the plot including trees within the plot, associate one or more sensor measurements with the images, store the images with their associated measurements, and generate a survey of trees in the plot using at least the images and the one or more sensor measurements.

Abstract: A method for controlling a robot to pick up an object in various positions. The method includes: defining a plurality of reference points on the object; mapping a first camera image of the object in a known position onto a first descriptor image; identifying the descriptors of the reference points from the first descriptor image; mapping a second camera image of the object in an unknown position onto a second descriptor image; searching the identified descriptors of the reference points in the second descriptor image; ascertaining the positions of the reference points in the three-dimensional space in the unknown position from the found positions; and ascertaining a pickup pose of the object for the unknown position from the ascertained positions of the reference points.

Abstract: The systems and methods herein provide improved methodologies for visualization on a user's display of sensor data (e.g., 2D and 3D information obtained from or derived from sensors) for objects, components, or features of interest in a scene. The previously acquired sensor data is processable for concurrent display of objects/features/scene or location visualizations to a user during their real-time navigation of a scene camera during a variety of user visualization activities. Sensor data can be acquired via the operation of vehicles configured with one or more sensors, such as unmanned aerial vehicles, or from other methodologies, or from any other suitable sensor data acquisition activities. Objects etc. for which acquired sensor data can be visualized by a user on a display includes buildings, parts of buildings, and infrastructure elements, among other things.

Abstract: The disclosure provides a learning framework that unifies both semantic segmentation and semantic edge detection. A learnable recurrent message passing layer is disclosed where semantic edges are considered as explicitly learned gating signals to refine segmentation and improve dense prediction quality by finding compact structures for message paths. The disclosure includes a method for coupled segmentation and edge learning. In one example, the method includes: (1) receiving an input image, (2) generating, from the input image, a semantic feature map, an affinity map, and a semantic edge map from a single backbone network of a convolutional neural network (CNN), and (3) producing a refined semantic feature map by smoothing pixels of the semantic feature map using spatial propagation, and controlling the smoothing using both affinity values from the affinity map and edge values from the semantic edge map.

Abstract: A system includes first and second sensors and a controller. The first sensor is of a first type and is configured to sense objects around a vehicle and to capture first data about the objects in a frame. The second sensor is of a second type and is configured to sense the objects around the vehicle and to capture second data about the objects in the frame. The controller is configured to down-sample the first and second data to generate down-sampled first and second data having a lower resolution than the first and second data. The controller is configured to identify a first set of the objects by processing the down-sampled first and second data having the lower resolution. The controller is configured to identify a second set of the objects by selectively processing the first and second data from the frame.

Abstract: A method of generating a correction plan for a knee of a patient includes obtaining a ratio of reference bone density to reference ligament tension in a reference population. A bone of the knee of the patient may be imaged. From the image of the bone, a first dataset may be determined including at least one site of ligament attachment and existing dwell points of a medial femoral condyle and lateral femoral condyle of the patient on a tibia of the patient. Desired positions of contact in three dimensions of the femoral condyles of the patient with the tibia of the patient may be obtained by determining a relationship in which a ratio of bone density to ligament tension of the patient is substantially equal to the ratio of reference bone density to reference ligament tension.

Abstract: A method with algorithm updating includes: receiving a first input batch including one or more first images; generating a first output batch with respect to the first input batch using an algorithm configured to generate a disparity image, the first output batch including one or more first output images; receiving a second input batch corresponding to the first input batch, the second input batch including one or more second images having viewpoints that are different from viewpoints of the one or more first images; generating a test batch based on the first output batch and the second input batch, the test batch including one or more test images; and updating the algorithm based on a difference between the first input batch and the test batch.

Abstract: A document is received by a first computer system from a second computer system. The document is received through a network. A document level security code is received by the first computer system through a second network. A content level security code is received by the first computer system and through the second network. A first validation operation is performed by the first computer system. The performance is based on the document level security code. The first computer system determines the document is an altered document. The determination is based on the performance of the first validation operation. A second validation operation is executed on the altered document. The second validation operation is executed by the first computer system and in response to the determination. An alteration status of the document is detected by the first computer and based on the second validation operation.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a biometric authentication system. In one aspect, a method includes broadcasting, through a transceiver, a signal that includes a kiosk identifier; receiving, from a server system, a reference image of the user based on a mobile computing device associated with the user having received the signal and providing the kiosk identifier to the server system; capturing, with a camera, an image of the user; and authenticating the user based on a comparison of the captured image of the user to the reference image of the user based on a comparison of the captured image of the user to the reference image of the user.