Abstract: A computer system generates an outline of a roof of a structure based on a set of lateral images depicting the structure. For each image in the set of lateral images, one or more rooflines corresponding to the roof of the structure are determined. The computer system determines how the rooflines connect to one another. Based on the determination, the rooflines are connected to generate an outline of the roof.

Abstract: A method for positioning a vehicle, a vehicle and an electronic device are disclosed, which relate to a field of automatic driving and intelligent transportation. The method includes: obtaining a map related to a region, the region being a region where a positioning signal such as a GNSS signal is limited, such as a tunnel, the region including a plurality of positioning markers, and the map including positioning mark data corresponding to the plurality of positioning markers; capturing image data for the region when the vehicle travels through the region; and determining positioning information of the vehicle by matching the image data with the positioning mark data in the map based on an estimated posture of the vehicle. The map here may be a high-precision map.

Abstract: An apparatus for classifying a picture of a reaction of reactants in a predetermined container; the apparatus comprising a first neural network arranged for receiving an input picture of a reaction of reactants in a predetermined container and for providing, for each reaction category of a first plurality of reaction categories, a probability that the input picture shows a reaction that belongs to said reaction category; a second neural network arranged for, if the first neural response provides a highest probability that the input picture shows a reaction that belongs to a predetermined reaction category, receiving a predetermined portion of the input picture and providing, for each reaction category of a second plurality of reaction categories, a probability that said predetermined portion of the input picture shows a reaction that belongs to said reaction category.

Abstract: The present application discloses a person re-identification method and apparatus based on a deep learning network, a device, and a medium. The method includes: obtaining an initial person re-identification network; creating a homogeneous training network corresponding to the initial person re-identification network, where the homogeneous training network comprises a plurality of homogeneous branches with a same network structure; training the homogeneous training network by using a target loss function, and determining a final weight parameter of each network layer in the homogeneous training network; and loading the final weight parameter by using the initial person re-identification network to obtain a final person re-identification network, to perform a person re-identification task by using the final person re-identification network.

Abstract: The method for determining at least one morphological data item of a wriststrap wearer in order to prepare (E3) a wristwatch strap taking into account this item includes: acquiring (E1) at least one morphological data item (1; 2) of the wriststrap wearer from an acquisition device of a determination system (11) provided with at least one sensor, such as a camera, a laser or a lens, configured to scan, photograph, or film at least a portion of the wrist and/or hand of the wearer, wherein the determination system (11) is designed to receive and optionally emit waves to acquire at least one morphological characteristic of the wearer of the bracelet in a first operation (O11), the at least one morphological data item being a dimension (1) from among a circumference and/or height and/or width of a wrist portion (p1) and/or of a portion of the hand (p2) of the wearer.

Abstract: There is provided an information processing apparatus to measure a distance in a real space with a simpler operation, the information processing apparatus including: an acquisition section that acquires an image captured by a predetermined imaging section, and position information based on at least any of a position and a direction of the imaging section; an estimation section that estimates a first position and a second position in a real space, on a basis of a first image and a second image which are the image captured at each of a first viewpoint and a second viewpoint, and first position information and second position information which are the position information about each of the first viewpoint and the second viewpoint; and a measurement section that measures a distance between the first position and the second position on a basis of the estimation result.

Abstract: A driver assistance apparatus includes a global positioning system (GPS) module configured to obtain position data of a vehicle; a Light Detection And Ranging (LiDAR) installed in the vehicle to have an external field of view of the vehicle, and configured to obtain first image data for the external field of view of the vehicle; a communication interface configured to receive second image data obtained by an external LiDAR disposed at a position different from the vehicle; and a controller including at least one processor configured to process the first image data and the second image data. The controller may be configured to compare the first image data and the second image data, and to correct the position data when an error occurs as a result of the comparison.

Abstract: Methods and systems for generating dynamic picture metadata are presented. Given an input picture generated on a mastering display, characteristics of a target display which is different than the mastering display, and an initial set of dynamic metadata for the input picture, an iterative algorithm: maps the input image to a mapped image for the target display according to a display management process and the image metadata, compares the input image to the mapped image according to a visual appearance-matching metric, and updates the image metadata using an optimization technique until a visibility difference value between the input image and the mapped image according to the visual appearance-matching metric is below a threshold.

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: Human mesh model recovery may utilize prior knowledge of the hierarchical structural correlation between different parts of a human body. Such structural correlation may be between a root kinematic chain of the human body and a head or limb kinematic chain of the human body. Shape and/or pose parameters relating to the human mesh model may be determined by first determining the parameters associated with the root kinematic chain and then using those parameters to predict the parameters associated with the head or limb kinematic chain. Such a task can be accomplished using a system comprising one or more processors and one or more storage devices storing instructions that, when executed by the one or more processors, cause the one or more processors to implement one or more neural networks trained to perform functions related to the task.

Abstract: A computer system generates an outline of a roof of a structure based on a set of lateral images depicting the structure. For each image in the set of lateral images, one or more rooflines corresponding to the roof of the structure are determined. The computer system determines how the rooflines connect to one another. Based on the determination, the rooflines are connected to generate an outline of the roof.

Abstract: Techniques are discussed for determining a velocity of an object in an environment from a sequence of images (e.g., two or more). A first image of the sequence is transformed to align the object with an image center. Additional images in the sequence are transformed by the same amount to form a sequence of transformed images. Such sequence is input into a machine learned model trained to output a scaled velocity of the object (a relative object velocity (ROV)) according to the transformed coordinate system. The ROV is then converted to the camera coordinate system by applying an inverse of the transformation. Using a depth associated with the object and the ROV of the object in the camera coordinate frame, an actual velocity of the object in the environment is determined relative to the camera.

Abstract: Systems and methods may utilize a predictive analysis model to analyze a contract or other document. A system may parse a document and/or a repository of information associated with the document. The system may identify one or more terms in the document and corresponding terms in the repository. The system may determine a difference parameter between a first term extracted from the document and a second term extracted from the repository. The system may determine whether the difference between the first term and the second term, represented by the difference parameter, is likely to be acceptable to the user using a predictive analysis model. The system may report a validation parameter indicating a level of acceptability associated with the difference. User feedback on the accuracy of the predictive analysis model is used to train, modify, and improve the predictive analysis model.

Abstract: Body and hand correlation method, apparatus, device, storage medium and computer program are provided. Method includes: in an image of which an image content includes a body to be correlated and a hand to be correlated, a first correlation probability between a body detection box of the body and a hand detection box of the hand is determined; a second correlation probability between the body and a wrist key point in a key point is determined based on the key point in the body detection box, the key point including the wrist key point and elbow key point belonging to the same arm; a third correlation probability between the hand detection box and the wrist key point is determined based on the wrist key point and elbow key point; and a correlation degree between the body and the hand is determined based on the first, second, and third correlation probabilities.

Abstract: Certain aspects of the present disclosure provide a method for assessing the performance of a physical activity, including: recording motion capture data while a training subject demonstrates a physical activity sequence; identifying one or more primary body elements based on the motion capture data; deriving one or more path characteristic metrics based on a state variable set and the one or more primary body elements, wherein the state variable set defines the state of a body at any given time; and defining an ideal activity path of the physical activity sequence based on the one or more path characteristic metrics.

Abstract: A method for categorizing biological structure of interest (BSOI) in digitized images of biological tissues comprises a stage of identifying BSOIs in digitized images and further comprises presenting an image from the plurality of images that comprises at least one BSOI with high level of entropy to a user, receiving from the user input indicative of a category to be associated with the BSOI that had the high level of entropy and updating the cell categories classifier according to the category of the BSOI provided by the user.

Abstract: An object tracking method is performed by an electronic device, the method including: obtaining a target image frame currently acquired by a target camera, the target image frame including a to-be-tracked target object; in response to determining that the target object is not a current globally-tracked object, constructing a search recognition tree for the target object according to a position relationship pair database generated based on video stream data acquired by the target camera, the search recognition tree including a root node and T levels of child nodes; and recognizing, according to action values of the child nodes in the search recognition tree, a target action to be performed by the target object in a next image frame following the target image frame.

Abstract: An image matching device includes a weight determination unit and a matching unit. Based on a synthesized frequency characteristic obtained by synthesizing a frequency characteristic of a first image with a frequency characteristic of a second image and an ideal synthesized frequency characteristic that is an ideal one obtained by synthesizing the frequency characteristic of the first image with the frequency characteristic of the second image, the weight determination unit determines a weight relating to frequency in performing matching of the first image and a third image. The matching unit performs matching of the first image and the third image based on the determined weight.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for group identification and monitoring. The methods, systems, and apparatus include actions of determining, using one or more first frames of a video sequence, that two people each of whom is depicted in at least one of the first frames satisfy a grouping criteria that indicates that the two people should be grouped for video analysis, determining, using one or more second frames of the video sequence at least some of which were captured after the one or more first frames and which depict at least one of the two people, that the two people satisfy a degrouping criteria that indicates that the two people should not be grouped for video analysis, determining that a physical distance between the two people violates a distance criteria, and providing an alert that the distance criteria is violated.

Abstract: A system and method for developing applications (Apps) for automated assessment and analysis of processed biological samples. Such samples are obtained, combined with nutrient media and incubated. The incubated samples are imaged and the image information is classified according to predetermined criteria. The classified image information is then evaluated according to Apps derived from classified historical image information in a data base. The classified historical image information is compared with the classified image information to provide guidance on further processing of the biological sample through Apps tailored to process provide sample process guidance tailored to the classifications assigned to the image information.