Abstract: An electronic device used in a method for expanding image depth obtains first images by a first sensor, the first images comprising depth information. The electronic device obtains second images by a second sensor, the second images comprising gradient information, and the first images correspond to the second images. The electronic device determines the pixels in the first images which contain expandable content according to the gradient information of the second images, applies expansion accordingly to the pixels in the first images to generate third images, and generate target depth maps according to the gradient information of the second images and the depth information of the third images.

Abstract: A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

Abstract: Provided is a computer-recordable recording medium having stored thereon a video file including artificial intelligence (AI) encoding data, wherein the AI encoding data includes: image data including encoding information of a low resolution image generated by AI down-scaling a high resolution image; and AI data about AI up-scaling of the low resolution image reconstructed according to the image data, wherein the AI data includes: AI target data indicating whether AI up-scaling is to be applied to at least one frame; and AI supplementary data about up-scaling deep neural network (DNN) information used for AI up-scaling of the at least one frame from among a plurality of pieces of pre-set default DNN configuration information, when AI up-scaling is applied to the at least one frame.

Abstract: In accordance with some embodiments of the disclosed subject matter, systems, methods, and media for selectively presenting images captured by confocal laser endomicroscopy (CLE) are provided. In some embodiments, a method comprises: receiving images captured by a CLE device during brain surgery; providing the images to a convolution neural network (CNN) trained using at least a plurality of images of brain tissue captured by a CLE device and labeled diagnostic or non-diagnostic; receiving an indication, from the CNN, likelihoods that the images are diagnostic images; determining, based on the likelihoods, which of the images are diagnostic images; and in response to determining that an image is a diagnostic image, causing the image to be presented during the brain surgery.

Abstract: An apparatus can include a memory, a communication interface, and a processor. The processor is configured to receive image data from an imaging device and first contextual data associated with the image data. The image data includes at least one image of a field of view. The processor is also configured to receive second contextual data associated with a user of a user device. The second contextual data is generated in response to the user device receiving a wireless signal sent by an antenna operably coupled to the imaging device. The processor is further configured to determine a potential presence of the user in the image data based on comparing the first contextual data with the second contextual data, analyze the image data to identify the user in the image data, and send the image data to the user.

Abstract: Artificial intelligence using convolutional neural network with Hough Transform. In an embodiment, a convolutional neural network (CNN) comprises convolution layers, a Hough Transform (HT) layer, and a Transposed Hough Transform (THT) layer, arranged such that at least one convolution layer precedes the HT layer, at least one convolution layer is between the HT and THT layers, and at least one convolution layer follows the THT layer. The HT layer converts its input from a first space into a second space, and the THT layer converts its input from the second space into the first space. The CNN may be applied to an input image to perform semantic image segmentation, so as to produce an output image representing a result of the semantic image segmentation.

Abstract: Aspects of the technology described herein relate to receiving an ultrasound image, automatically determining a location of a specific point on an anatomical structure depicted in the ultrasound image, and displaying an indicator of the location of the specific point on the anatomical structure on the ultrasound image. In some embodiments, the anatomical structure is a bladder. In some embodiments, the specific point is the centroid. In some embodiments, a statistical model determines the specific point. The indicator may be, for example, a symbol located at the specific point, a horizontal line extending through the specific point from one edge of the anatomical structure to another, and/or a vertical line extending through the specific point from one edge of the anatomical structure to another.

Abstract: Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself—rather than using a priori definitions for what should constitute a measurable unit of action—identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

Abstract: A computerized system of performing fish census which otherwise requires high level of domain knowledge and expertise is described. Divers with minimal knowledge of fish can obtain high quality population and species distribution measurements using a stereo camera rig and fish video analyzer software that was developed. The system has two major components: a camera rig and software for fish size, density and biomass estimation. The camera rig consists of a simple stand on which one to four pairs of stereo cameras are mounted to take videos of the benthic floor for a few minutes. The collected videos are uploaded to a server which performs stereo analysis and image recognition. The software produces video clips containing estimates of fish size, density and species biodiversity and a log report containing information about the individual fishes for further end user analysis.

Abstract: A system and method for lateral vehicle detection is disclosed. A particular embodiment can be configured to: receive lateral image data from at least one laterally-facing camera associated with an autonomous vehicle; warp the lateral image data based on a line parallel to a side of the autonomous vehicle; perform object extraction on the warped lateral image data to identify extracted objects in the warped lateral image data; and apply bounding boxes around the extracted objects.

Abstract: The present disclosure provides for ultrasound systems and methods to pre-process ultrasound data to distinguish abnormal tissue from normal tissue. An exemplary method can include receiving a set of ultrasound data and partitioning the set into a set of windows. The method can then provide for processing the set of windows to determine a power spectrum for each window. The power spectrum for each window can be processed to determine a normalized power spectrum for each window. This normalized power spectrum can be processed for each window with a machine learning model. The method can then provide for displaying an image where each window of the set of windows is displayed using a unique identifier based on the output of the machine learning model.

Abstract: The present invention relates to a system and a method for calibration between coordinate systems of a 3D camera and a medical imaging apparatus and an application thereof.

Abstract: An information processing apparatus (2000) detects that a customer (30) takes up a product from a display area (20), using a captured image (12). The information processing apparatus (2000) generates information (customer information) indicating the acquisition count by the customer (30) in a manner that is associated with an identifier of the customer (30). The information processing apparatus 2000 sets the acquisition count, which is indicated by the customer information on the customer (30), in a case where the customer (30) makes payment for the products, to zero.

Abstract: A surgical image capture and display system includes a handheld image capture and pointing device and a display assembly. An image is captured by an image sensor of the handheld device and displayed on the display assembly. The image sensor detects light emitted by one or more beacons of the display assembly. The system determines, based on the light emitted by the one or more beacons, a position or orientation of the handheld device relative to the display assembly. The system updates display of a graphical user interface comprising the image on the display assembly in accordance with the determined position or orientation of the handheld device.

Abstract: The present disclosure relates to a method performed in a fingerprint analysis system for facilitating differentiating between a live finger and a spoof finger. The method comprises acquiring a plurality of time-sequences of images, each of the time-sequences showing a respective finger as it engages a detection surface of a fingerprint sensor. Each of the time-sequences comprises at least a first image and a last image showing a fingerprint topography of the finger, wherein the respective fingers of some of the time-sequences are known to be live fingers and the respective fingers of some other of the time-sequences are known to be spoof fingers. The method also comprises training a machine learning algorithm on the plurality of time-sequences to produce a model of the machine learning algorithm for differentiating between a live finger and a spoof finger.

Abstract: Methods, devices, and systems may be utilized for detecting one or more properties of a plant area and generating a map of the plant area indicating at least one property of the plant area. The system comprises an inspection system associated with a transport device, the inspection system including one or more sensors configured to generate data for a plant area including to: capture at least 3D image data and 2D image data; and generate geolocational data. The datacenter is configured to: receive the 3D image data, 2D image data, and geolocational data from the inspection system; correlate the 3D image data, 2D image data, and geolocational data; and analyze the data for the plant area. A dashboard is configured to display a map with icons corresponding to the proper geolocation and image data with the analysis.

Abstract: The disclosed system may include a housing dimensioned to secure various components including at least one physical processor and various sensors. The system may also include a camera mounted to the housing, as well as physical memory with computer-executable instructions that, when executed by the physical processor, cause the physical processor to: acquire images of a surrounding environment using the camera mounted to the housing, identify features of the surrounding environment from the acquired images, generate a map using the features identified from the acquired images, access sensor data generated by the sensors, and determine a current pose of the system in the surrounding environment based on the features in the generated map and the accessed sensor data. Various other methods, apparatuses, and computer-readable media are also disclosed.

Abstract: Disclosed herein are methods and systems for determining liveness of a user, comprising analyzing visual content of a screen of a client device used by a user to access an online service, adjusting one or more visual objects displayed on the screen of the client device according to dynamically changing patterns, capturing a sequence of images depicting one or more reflecting surfaces associated with the user viewing the screen while the visual objects are displayed, analyzing the images to identify a reflection of the displayed visual objects in the reflecting surfaces and verifying liveness of the user based on one or more of a plurality of reflection attributes of the identified reflection.

Abstract: In one aspect, a computerized method for anti-counterfeiting solution using a machine learning (ML) model includes the step of providing a pre-defined set of feature detection rules, a pre-defined set of edge detection rules, a pre-defined threshold percentage, an original seal, an original fingerprint of the original seal, and a pre-trained fingerprint identification model. The pre-trained fingerprint identification model is trained by a specified ML algorithm using one or more digital images of the original seal. With a digital camera of a scanning device, the method scans a seal whose authenticity is to be determined. The seal is used to secure a transportation container. The method uses the pre-defined set of feature detection rules to detect and extract an extracted feature image at a specified position on the seal. The method breaks down the extracted feature image of the seal into a ‘kn’ number of sub-images by forming a ‘k’ rows x ‘n’ columns of a grid of the extracted feature image.

Abstract: Detection of whether a video is a fake video derived from an original video and altered is undertaken using both image analysis and frequency domain analysis of one or more frames of the video. The analysis may be implemented using neural networks.