Abstract: Methods and systems for facilitating photo-based estimation are described. In an aspect, a server is configured to receive, via a communications module from a remote computing device, a signal comprising image data obtained by the remote computing device through activation of a submission application. The server may obtain verification data that includes at least one of policy data obtained from at least one of the stored profiles or sensor data received from the remote computing device. The server may evaluate the image data based on the verification data to determine whether the image data is valid. The server may, upon determining that the image data is not valid, generate an error.

Abstract: Embodiments relate generally to waste receptacles and more particularly to smart waste receptacles capable of identifying waste. A waste receptacle may identify waste using machine vision and perform a task in response to identifying the waste. In an embodiment, the smart waste receptacle may instruct a user to direct the waste to one of a plurality of waste bins based on a category of the waste. In an embodiment, the smart waste receptacle may place an order to replenish the stock of a product based on the identification of waste. In an embodiment, the smart waste receptacle is created by attaching a smart device to a standard waste receptacle.

Abstract: A face detection method includes scaling an input image to images of various sizes according to certain proportions by means of an image pyramid; passing the resultant images through a first-level network in a sliding window manner to predict face coordinates, face confidences, and face orientations; filtering out the most negative samples by confidence rankings and sending the remaining image patches to a second-level network. Through a second-level network, filtering out non-face samples; applying a regression to obtain more precise position coordinates and providing prediction results of the face orientations. Through an angle arbitration mechanism, combining the prediction results of the preceding two networks to make a final arbitration for a rotation angle of each sample, rotating each of the image patches upright according to the arbitration result made by the angle arbitration mechanism and sending to a third-level network for fine-tuning to predict positions of keypoints.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. The processor may be configured to blur each of the training geospatial images. The processor may also be configured to iteratively operate a training model to identify a given feature from each of the blurred training geospatial images so that the blurring is reduced with each iteration.

Abstract: A novel method and apparatus for face authentication is disclosed. The disclosed method comprises detecting a motion by a subject within a predetermined area of view, assigning a unique session identification number to the subject detected within a predetermined area of view, detecting a facial area of the subject detected within a predetermined area of view, generating an image of the facial area of the subject, assessing a quality of the image of the facial area of the subject, conducing an incremental training of the image of the facial area of the subject, determining an identity of the subject based on the image of the facial area of the subject, identifying an intent of the subject, and authorizing access to a point of entry based on the determined identity of the subject and based on the intent of the subject.

Abstract: According to various embodiments, an electronic device includes: a housing; a display exposed through a part of the housing and located within the housing; a fingerprint sensor located at a portion of the housing and coupled with the display; a processor electrically coupled with the display and the fingerprint sensor; and at least one memory electrically coupled with the processor, wherein the memory stores fingerprint data for confirmation and stores instructions to be executed by the processor and the instructions that, when executed, cause the processor to receive the fingerprint data of a user using the fingerprint sensor, to analyze the received fingerprint data to determine whether at least a part of the fingerprint data is a result of at least a part of the finger at least partially covered with an external material, and to at least partially compare a part of the stored fingerprint data for confirmation with the analyzed fingerprint data.

Abstract: Systems and methods are disclosed for image reconstruction and enhancement, using a computer system. One method includes acquiring a plurality of images associated with a target anatomy; determining, using a processor, one or more associations between subdivisions of localized anatomy of the target anatomy identified from the plurality of images, and local image regions identified from the plurality of images; performing an initial image reconstruction based on image acquisition information of the target anatomy; and updating the initial image reconstruction or generating a new image reconstruction based on the image acquisition information and the one or more determined associations.

Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.

Abstract: Examples of a character recognition system are provided. In an example, the system may receive an object detection requirement pertaining to a video clip. The system may identify a visual media feature map from visual media data to process the object detection requirement. The system may implement an artificial intelligence component to segment the visual media feature map into a plurality of regions, and identify a plurality of image proposals therein. The system may implement a first cognitive learning operation to allocate a human face identity for a human face and an object name for an object present in the video clip. The system may determine a face identity model for the human face present in the plurality of image proposals and generate a tagged face identity model. The system may implement a second cognitive learning operation to assemble the plurality of frames with an appurtenant tagged face identity model.

Abstract: Techniques are disclosed relating to identifying duplicate coding and/or design used in different parts of a user interface (e.g., a web application). In some embodiments, a plurality of visible elements of an interface are detected based on hierarchical user interface code. In some embodiments, a device groups sets of the plurality of visible elements based on determined coordinates and depth levels for the detected elements. In some embodiments, groups that meet a threshold similarity criterion are identified. In some embodiments the threshold similarity criterion is based on classification of detected elements and/or geospatial analysis.

Abstract: An image creation and editing tool can use the data produced from training a neural network to add stylized representations of an object to an image. An object classification will correspond to an object representation, and pixel values for the object representation can be added to, or blended with, the pixel values of an image in order to add a visualization of a type of object to the image. Such an approach can be used to add stylized representations of objects to existing images or create new images based on those representations. The visualizations can be used to create patterns and textures as well, as may be used to paint or fill various regions of an image. Such patterns can enable regions to be filled where image data has been deleted, such as to remove an undesired object, in a way that appears natural for the contents of the image.

Abstract: Techniques are described that facilitate integrating artificial intelligence (AI) informatics in healthcare systems using a distributed learning platform. In one embodiment, a computer-implemented is provided that comprises interfacing, by a system operatively coupled to a processor, with a medical imaging application that provides for viewing medical image data. The method further comprises, facilitating, by the system, generation of structured diagnostic data according to a defined ontology in association with usage of the imaging application to perform a clinical evaluation of the medical image data. The method further comprises providing, by the system, the structured diagnostic data to one or more machine learning systems, wherein based on the providing, the one or more machine learning systems employ the structured diagnostic data as training data to generate or train one or more diagnostic models configured to provide artificial intelligence-based diagnostic evaluations of new medical image data.

Abstract: One or more embodiments of a document organization system quickly and conveniently provide digital documents to a user on a client device based on a physical object. In particular, the document organization system can receive an image of a physical document and an identifier from a first client device, identify digital documents that match the physical document, and provide the matching digital documents to a second client device, which displays the identifier. In another embodiment, the document organization system allows a user to bind digital documents to a physical object and later recall the digital documents using the physical object. In addition, the document organization system can store and recall the layout arrangement of digital documents on a client device when binding and recalling the digital documents to the physical object.

Abstract: An ultrasound imaging apparatus (200, 600) may include at least one controller (210, 610) which may be configured to: acquire ultrasound data of an anatomical region-of-interest (ROI), the ultrasound data including at least two frames acquired at different times over an interval of time as a push force is applied to induce movement in the anatomical ROI; determine a correlation between at least two of the acquired frames and form corresponding correlation coefficients; generate a correlation coefficient (CC) map based upon the determined correlation information between the at least two frames; and distinguish fluid from tissue within the CC map based upon a comparison of the correlation coefficients with at least one threshold value.

Abstract: Method and system for tracking a moving object may be used in virtual or augmented reality systems, in-site logistics systems, robotics systems and control systems of unmanned moving objects. The method of tracking includes a step of automatic adjusting a tracking area by detection and registration of unique combinations of elementary optical patterns, and a step of tracking change in position and/or orientation of a moving object by detection of unique combinations of elementary optical patterns and comparison thereof with the unique combinations of elementary optical patterns registered during the tracking area adjustment. The system for tracking a moving object comprises at least one tracker located on a moving object the tracker including an optical sensor, at least one marker strip including active markers forming elementary optical patterns in a picture obtained from the optical sensor, and a central processing unit.

Abstract: Certain aspects of the present disclosure are directed to methods and apparatus for predicting subject motion using probabilistic models. One example method generally includes receiving training data comprising a set of subject pose trees. The set of subject pose trees comprises a plurality of subsets of subject pose trees associated with an image in a sequence of images, and each subject pose tree in the subset indicates a location along an axis of the image at which each of a plurality of joints of a subject is located. The received training data may be processed in a convolutional neural network to generate a trained probabilistic model for predicting joint distribution and subject motion based on density estimation. The trained probabilistic model may be deployed to a computer vision system and configured to generate a probability distribution for the location of each joint along the axis.

Abstract: An object of the present invention is to provide an information presenting apparatus, method, and a program capable of promoting a user to variously use images according to a category to which an image owned by a user belongs. The category classifying unit classifies images uploaded from the first user terminal and the second user terminal to the SNS server by machine learning into any one of defined categories such as “nature field”, “art field”, “wedding field”, “snap field” and the like. The category determining unit determines the category of the trigger image identified by the trigger image identification information. The recommended information extracting unit extracts images (recommended images) belonging to the same category as the category determined by the category determining unit.

Abstract: An aspect of the present disclosure includes acquiring an image representing a shadow component in an image capturing environment, the shadow component being reflected in a multi-valued image obtained by capturing an image of a subject; specifying an area having a luminance greater than a predetermined luminance value, the area being included in the image representing the shadow component acquired in the acquiring; correcting the image in such a manner that a luminance of an outer peripheral area of the specified area deceases; and generating a binary image by performing binarization processing on a pixel value of a pixel of interest in the multi-valued image based on a pixel value at the same coordinates as those of the pixel of interest in the corrected image.

Abstract: Some implementations related to archiving of functional images. In some implementations, a method includes accessing images and determining one or more functional labels corresponding to each of the images and one or more confidence scores corresponding to the functional labels. A functional image score is determined for each of the images based on the functional labels having a corresponding confidence score that meets a respective threshold for the functional labels. In response to determining that the functional image score meets a functional image score threshold, a functional image signal is provided that indicates that one or more of the images that meet the functional image score threshold are functional images. The functional images are determined to be archived, and are archived by associating an archive attribute with the functional images such that functional images having the archive attribute are excluded from display in views of the images.

Abstract: A method of selecting frames of a video sequence. Image data for a plurality of consecutive frames of the video sequence is captured using a camera. Frames from the plurality of consecutive frames where the camera is moving are identified using the captured image data, wherein each of the identified frames comprises a subject. A size of the subject captured in each of the identified frames is determined. The identified frames are selected by detecting that the camera is moving towards and with the subject based on the size of the subject within each of a plurality of the identified frames.