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.

Abstract: Automated systems and methods are presented for retrospectively analyzing clinical trial data. A plurality of image derived from biological samples of patients in a cohort population are accessed. Image features are computed based on the plurality of images. A diagnostic feature metric is derived based on the computed image features. A cut point value is determined by applying a statistical minimization method using the derived diagnostic feature metric and patient outcome data from the cohort population, in which the cut point value identifies a patient in the cohort population as positive or negative for a diagnostic test.

Abstract: The present invention relates to an image processing apparatus for deriving multi-dimensional images of an object and an according system and method. The image processing apparatus comprises an interface configured to provide 3D image data of an object and to provide a sequence of images of the object. The image processing apparatus further comprises a processing unit configured to obtain a personalized 3D model of the object by applying a model-based segmentation to the 3D image data of the object and to adapt the personalized 3D model based on at least a part of the images of the sequence of images of the object.

Abstract: The present disclosure relates to method and apparatus for processing cross matching image based on deep learning.

Abstract: A method for assigning a number of lanes and a direction of travel on a path includes receiving location data including a plurality of location points, projecting the plurality of location points on to an aggregation axis perpendicular to a centerline of the vehicle path, grouping the plurality of location points as projected onto the aggregation axis into one or more clusters, and determining the number of vehicle lanes of the vehicle path based on a count of the one or more clusters.

Abstract: According to an embodiment, a reading system includes a reader and a calculator. The reader reads, from a character image, a character that is displayed by a segment display. The calculator performs one of first, second, third, or fourth processing. In the first processing, the calculator calculates a first score based on a state of pixels of the character. In the second processing, the calculator calculates a second score based on a match ratio between the pixels and the extracted pixels. In the third processing, the calculator calculates a third score based on a ratio of a length of the character image in first and second direction. In the fourth processing, the calculator calculates a fourth score based on a comparison result between the detected result and preset patterns. The calculator calculates a certainty of the reading by using one of the first, second, third, or fourth score.

Abstract: Systems and methods for generating synthetic video are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include generating a static background image and determining the location of a reference edge. The operations may include determining a perspective of an observation point. The operations may include generating synthetic difference images that include respective synthetic object movement edges. The operations may include determining a location of the respective synthetic object movement edge and generating adjusted difference images corresponding to the individual synthetic difference images. Adjusted difference images may be based on synthetic difference images, locations of the respective synthetic object movement edges, the perspective of the observation point, and the location of the reference edge.

Abstract: A method of tracking 3D position and orientation of an entity in a moving platform is described. The method comprises receiving data sensed by an inertial measurement unit mounted on the entity. Visual tracking data is also received, computed from images depicting the moving platform or the entity in the moving platform. The method computes the 3D position and orientation of the entity by estimating a plurality of states using the visual tracking data and the data sensed by the inertial measurement unit, where the states comprise both states of the moving platform and states of the entity.

Abstract: There is provided a method of computing at least one slide-level tissue type for a tissue image of tissue extracted from a patient, comprising: receiving a tissue image of a slide including tissue extracted from the patient, segmenting tissue objects of the tissue image, creating a tissue image patches from the segmented tissue objects of the tissue image, classifying, by a patch-level classifier, each of the plurality of tissue image patches into at least one patch-level tissue type, wherein each of the classified tissue image patches is associated with a relative location within the tissue image, analyzing, by a slide-level analysis code, the classified at least one patch-level tissue type and associated relative location for each of the plurality of tissue image patches outputted by the patch-level classifier, for computing at least one slide-level tissue type for the tissue image, and providing the at least one slide-level tissue type.

Abstract: A body and hand association method includes: an image to be detected of which an image content includes a body and a hand is acquired; a body bounding box of the body and a hand bounding box of the hand are determined in the image to be detected; an association probability between the body bounding box and the hand bounding box is determined; a circumscribed box of the body bounding box and the hand bounding box is determined; and a degree of association between the body and hand in the circumscribed box is determined based on a hand key point in the circumscribed box and the association probability.

Abstract: A first user generates video data for performance of an activity, such as a fitness exercise, by performing the activity in front of a camera. Based on the video data, the amount of movement of different parts of the first user's body is determined. Data representing the position of the first user over time is generated. The data may take the form of a function or a signal that is based on the function. The locations of body parts that move significantly are prioritized over other body parts when determining this data. At a subsequent time, a second user performs the activity. The number of times the second user completes the activity is counted by determining the number of times the second user reaches a position corresponding to a maximum value in the data representing the position of the first user.

Abstract: A method and system may survey a property using aerial images captured from an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV) or from a satellite device. The method may include identifying a commercial property for a UAV to perform surveillance, and directing the UAV to hover over the commercial property and capture aerial images at predetermined time intervals. Furthermore, the method may include receiving the aerial images of the commercial property captured at the predetermined time intervals, detecting a surveillance event at the commercial property, generating a surveillance alert, and transmitting the surveillance alert to an electronic device associated with an owner of the commercial property.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model to segment magnified images of tissue samples. The method includes obtaining a magnified image of a tissue sample; processing an input comprising: the image, features derived from the image, or both, in accordance with current values of model parameters of a machine learning model to generate an automatic segmentation of the image into a plurality of tissue classes; providing, to a user through a user interface, an indication of: (i) the image, and (ii) the automatic segmentation of the image; determining an edited segmentation of the image, comprising applying modifications specified by the user to the automatic segmentation of the image; and determining updated values of the model parameters of the machine learning model based the edited segmentation of the image.

Abstract: An identification device configured to acquire a collation fixed image of a part of a region where a fixed pattern is formed, and a collation amorphous image of a part of a region where an amorphous pattern formed when the fixed pattern is formed on a region different from the region where the fixed pattern is formed, of a collation molded article; refer to a storage unit that stores, as a registered image in advance for a mold, a fixed image and an amorphous image pair of a part of a region where a fixed and an amorphous pattern are formed, of each of plural molded articles formed using the mold; specify a mold that forms the collation molded article using the registered fixed and collation fixed images, and identify whether the collation amorphous image matches the registered amorphous image of any of the registered images corresponding to the mold.

Abstract: The present disclosure provides a target tracking method to be executed by a computing device, including: generating an optical flow image in accordance with a series of event data from a dynamic vision sensor, each event being triggered by movement of a target object relative to the dynamic vision sensor; determining a movement speed and a movement direction of at least one target object in a current image frame in accordance with the optical flow image; predicting a position of a corresponding target object in a next image frame in accordance with the movement direction and the movement speed; and when a predicted position is within a range of a field of view, taking the corresponding target object as a to-be-tracked target. The present disclosure further provides a computing device.

Abstract: Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.

Abstract: The present application relates generally to identifying regions of interest in images, including but not limited to whole slide image region of interest identification, prioritization, de-duplication, and normalization via interpretable rules, nuclear region counting, point set registration, and histogram specification color normalization. This disclosure describes systems and methods for analyzing and extracting regions of interest from images, for example biomedical images depicting a tissue sample from biopsy or ectomy. Techniques directed to quality control estimation, granular classification, and coarse classification of regions of biomedical images are described herein. Using the described techniques, patches of images corresponding to regions of interest can be extracted and analyzed individually or in parallel to determine pixels correspond to features of interest and pixels that do not.

Abstract: The present invention provides a sensor recognition integration device capable of preventing a rapid change in coordinates of an integrated object and preventing, for example, an erroneous determination in an autonomous driving plan determination device, even when the combination of sensors that perform detection changes. In the present invention, since an object position is estimated in a state where information of a position detected by a sensor that recognizes an object in an external field is corrected or changed, the rapid change of coordinates of an integrated object is prevented even when the combination of sensors that perform detection changes.

Abstract: Systems, methods, and computer-readable media for providing tools to validate color contrast are provided. To do so, three discrete color check processes are performed to ensure a user is able to identify when an icon is at risk of being inaccessible by some users. A border score considers each pixel at the edge of an icon compared to the background in which it is placed. An area score considers each discrete pixel of an icon compared to the background in which it is placed. A grid score considers a subdivision of an icon compared to the background in which it is placed. Using each of these three independent process, a summative score is provided. The summative score categories the icon into a risk level. Depending on the risk level, the icon may need to be refined to ensure it becomes more accessible.

Abstract: Techniques for de-aliasing depth ambiguities included within infrared phase depth images are described herein. An illuminator emits reference light towards a target object. Some of this light is reflected back and detected. A phase image is generated based on phase differences between the reference light and the reflected light. The phase differences represent changes in depth within overlapping sinusoidal periods of the reference and reflected light. The phase image also includes ambiguities because multiple different depths within the phase image share the same phase difference value, even though these depths actually correspond to different real-world depths. The phase image is fed as input to a machine learning (“ML”) component, which is configured to de-alias the ambiguities by determining, for each pixel in the phase image, a corresponding de-aliasing interval. A depth map is generated based on the phase image and any de-aliasing intervals generated by the ML component.