Abstract: Systems and methods capture image dynamics and use those captured image dynamics for image feature recognition and classification. Other methods and systems train a neural network to capture image dynamics. An image vector representing image dynamics is extracted from an image of an image stream using a first neural network. A second neural network, predicts a previous and/or subsequent image in the image stream from the image vector. The predicted previous and/or subsequent image is compared with an actual previous and/or subsequent image from the image stream. The first and second neural networks are trained using the result of the comparison.

Abstract: The object detection device extracts a plurality of predetermined features from an image in which a target object is represented, calculates an entire coincidence degree between the plurality of predetermined features set for an entire model pattern of the target object and the plurality of predetermined features extracted from a corresponding region on the image while changing a relative positional relationship between the image and the model pattern, and calculates, for each partial region including a part of the model pattern, a partial coincidence degree between the predetermined features included in the partial region and the predetermined features extracted from a region corresponding to the partial region on the image. Then, the object detection device determines whether or not the target object is represented in the region on the image corresponding to the model pattern based on the entire coincidence degree and the partial coincidence degree.

Abstract: PET/MR images are compensated with simplified adaptive algorithms for truncated parts of the body. The compensation adapts to a specific location of truncation of the body or organ in the MR image, and to attributes of the truncation in the truncated body part. Anatomical structures in a PET image that do not require any compensation are masked using a MR image with a smaller field of view. The organs that are not masked are then classified as types of anatomical structures, the orientation of the anatomical structures, and type of truncation. Structure specific algorithms are used to compensate for a truncated anatomical structure. The compensation is validated for correctness and the ROI is filled in where there is missing voxel data. Attenuation maps are generated from the compensated ROI.

Abstract: An image dataset comprising pixel depth arrays might be processed by an interpolator, wherein interpolation is based on pixel samples. Input pixels to be interpolated from and an interpolated pixel might comprise deep pixels, each represented with a list of samples. Accumulation curves might be generated from each input pixel, weights applied, and accumulation curves combined to form an interpolation accumulation curve. An interpolated deep pixel can be derived from the interpolation accumulation curve, taking into account zero-depth samples as needed. Samples might represent color values of pixels.

Abstract: Described are street level intelligence platforms, systems, and methods that can include a fleet of swarm vehicles having imaging devices. Images captured by the imaging devices can be used to produce and/or be integrated into maps of the area to produce high-definition maps in near real-time. Such maps may provide enhanced street level intelligence useful for fleet management, navigation, traffic monitoring, and/or so forth.

Abstract: Method and system for detecting vehicle occupant actions are disclosed. For example, the method includes receiving, by one or more processors, previously classified image data from a previously classified image database, the previously classified image data representing driver postures that are rotated and scaled to be standardized for a range of different drivers and different locations of a vehicle interior sensor within a given vehicle, wherein the previously classified image data is representative of previously classified vehicle occupant actions; receiving, by the one or more processors, current image data from the vehicle interior sensor subsequent to the vehicle interior sensor being registered within the given vehicle, wherein the current image data is representative of current vehicle occupant actions; and determining, by the one or more processors, a vehicle occupant action based at least in part upon a comparison of the current image data and the previously classified image data.

Abstract: A method, an apparatus, a device, and a storage medium for extracting a cardiovascular vessel from a CTA image, the method including the steps of: performing erosion operation and dilation operation on image data successively via a preset structural element to obtain a structure mask; performing a slice-by-slice transformation on the plane of section images of the structural mask to acquire the first ascending aortic structure in the structural mask, and acquiring an aortic center position and an aortic radius in the last slice of the plane of section image of the said structural mask; establishing a binarized sphere structure according to the aortic center position and the aortic radius, and synthesizing a second ascending aorta structure by combining the first ascending aorta structure with the structure mask and the binarized sphere structure.

Abstract: Example implementations involve a location system, which can involve associating each location of one or more unidentified targets detected from sensor data of the one or more sensors with identifiers corresponding to the transmitter of each of the one or more pairs of electronic devices, by calculating first distance relationships indicative of relationships of distances between the each location of the one or more unidentified targets detected from the sensor data of the one or more sensors and a reference point; calculating second distance relationships indicative of relationships of distances between the transmitter and the receiver of each of the one or more pairs of electronic devices; and associating the identifiers corresponding to the transmitter of the each of the one or more pairs of electronic devices with the each location of the one or more unidentified targets based on the first distance relationships and the second distance relationships.

Abstract: Described are street level intelligence platforms, systems, and methods that can include a fleet of swarm vehicles having imaging devices. Images captured by the imaging devices can be used to produce and/or be integrated into maps of the area to produce high-definition maps in near real-time. Such maps may provide enhanced street level intelligence useful for fleet management, navigation, traffic monitoring, and/or so forth.

Abstract: A system for motion detection may include at least one storage medium that includes a set of instructions, and at least one processor in communication with the at least one storage medium. When executing the set of instructions, the at least one processor may be configured to cause the system to obtain data related to a video scene of a space from at least one video camera; detect an object in the video scene; classify the object as a human object or a non-human object; when the object is classified as a human object, track movements of the human object; and determine a posture of the human object in the video scene based on the movements of the human object.

Abstract: Described herein are embodiments for fast training of adversarially robust models against adversarial attacks. Embodiments for model training by perturbing both the image and the label, which may be referred to as Bilateral Adversarial Training (BAT), are disclosed. To generate the adversarial label, one or more closed-form heuristic solutions are derived. One-step targeted attack is used with the target label being the most confusing class. It is shown in various experiments that random start and the most confusing target attack effectively prevent the label leaking and gradient masking problem. Coupled with the adversarial label part, embodiments of presented models significantly improve the state-of-the-art results. Experiments on one or more computationally challenging dataset also demonstrate the effectiveness of the presented BAT method embodiments.

Abstract: In a method for generating at least one medical result image from a plurality of single-frame images, the plurality of single-frame images referring to a medical image acquisition procedure conducted with a medical imaging system, at least one first parameter representative for the medical image acquisition procedure is acquired, and the at least one first parameter is evaluated to generate an evaluation result. Based on the evaluation result, it is decided whether to: (i) combine at least partially the plurality of single-frame images to generate the result image before permanently storing the result image, or (ii) permanently store the plurality of single-frame images before combining them to generate the result image.

Abstract: Disclosed is an automatic reorientation method from an SPECT three-dimensional reconstructed image to a standard view, wherein a rigid registration parameter P between a SPECT three-dimensional reconstructed image A and a standard SPECT image R is extracted by using a rigid registration algorithm to form a mapping database of A and P; features of the image A are extracted by using a three-layer convolution module, and are converted into a 6-dimensional feature vector T after three times of full connection, and T is applied to A through a spatial transformer network to form an orientation result predicted by the network, thus establishing the automatic reorientation model of the SPECT three-dimensional reconstructed image. The SPECT three-dimensional reconstructed image to be orientated is taken as an input. A standard view can be obtained by using the automatic reorientation model of the SPECT three-dimensional reconstructed image for automatic turning.

Abstract: A display method including the steps of: acquiring, at a first predetermined frequency, a first series of one-spatial-dimensional instant images of the finish line of a race; simultaneously acquiring a second series of two spatial-dimensional instant images of the same finish line (19) at a second predetermined frequency; forming a temporal image (10) of the photo finish type from the first series of instant images; choosing an instant (15, 22) of the temporal image (10); analysing a two-spatial-dimensional image of the second series, the image being correlated with the selected instant (15, 22) to determine at least one distance; and displaying the analysed two-dimensional image with the determined distance or distances. Also, a display system for implementing the method.

Abstract: A method of scanning an oral cavity including: acquiring, using an intraoral scanner (IOS) head, without changing a position of the IOS head, a first image of a first region of interest (ROI) and a second image of a second ROI where the first and the second ROIs are of different portions of a dental arch of the oral cavity and do not overlap; reconstructing depth information for the first and the second ROI; and generating a single model of the dental arch by combing the depth information.

Abstract: The embodiments herein provide a system and method for integrating objects in monocular simultaneous localization and mapping (SLAM). State of art object SLAM approach use two popular threads. In first, instance specific models are assumed to be known a priori. In second, a general model for an object such as ellipsoids and cuboids is used. However, these generic models just give the label of the object category and do not give much information about the object pose in the map. The method and system disclosed provide a SLAM framework on a real monocular sequence wherein joint optimization is performed on object localization and edges using category level shape priors and bundle adjustment. The method provides a better visualization incorporating object representations in the scene along with the 3D structure of the base SLAM system, which makes it useful for augmented reality (AR) applications.

Abstract: An interpolation method may include scheduling spatially adjacent image blocks for interpolation, and calculating ranges of values of an attribute of the image blocks, wherein at least one value for a first one of the image blocks may be used for a second one of the image blocks. Calculating the ranges of values may include calculating a root value of the attribute at a root location of an array of the spatially adjacent image blocks, and adding incremental values of the attribute to the root value at points of the image blocks that are offset from the root location. The root location may be centrally located in an array of the spatially adjacent image blocks. The interpolation may be calculated in a diagonal hierarchical manner based on a plane equation.

Abstract: A system for estimating a pose of one or more persons in a scene includes a camera configured to capture one or more images of the scene; and a data processor configured to execute computer executable instructions for: (i) receiving the one or more images of the scene from the camera; (ii) extracting features from the one or more images of the scene for providing inputs to a keypoint subnet and a person detection subnet; (iii) generating one or more keypoints using the keypoint subnet; (iv) generating one or more person instances using the person detection subnet; (v) assigning the one or more keypoints to the one or more person instances by learning pose structures from image data; and (vi) determining one or more poses of the one or more persons in the scene using the assignment of the one or more keypoints to the one or more person instances.

Abstract: An object of the present invention is to provide a dispensing audit support apparatus and a dispensing audit support method with a high collation accuracy robustness. According to a dispensing audit support apparatus according to one aspect of the present invention, since a position, shape and size of a region of interest are set according to a position of a collation-target medicine in a captured image, and a position, shape and size of a master image are set according to the set region of interest, it is possible to avoid or reduce distortion of the medicine shape, blur, inclusion of an end part into the image, and the like due to the position and orientation of the collation-target medicine. Therefore, influence on collation accuracy is small, and it is possible to enhance the robustness of the collation accuracy.

Abstract: An object of the present invention is to provide a dispensing audit support apparatus and a dispensing audit support method with a high collation accuracy robustness. According to a dispensing audit support apparatus according to one aspect of the present invention, since a position, shape and size of a region of interest are set according to a position of a collation-target medicine in a captured image, and a position, shape and size of a master image are set according to the set region of interest, it is possible to avoid or reduce distortion of the medicine shape, blur, inclusion of an end part into the image, and the like due to the position and orientation of the collation-target medicine. Therefore, influence on collation accuracy is small, and it is possible to enhance the robustness of the collation accuracy.