Abstract: The present disclosure proposes a computer implemented of object recognition of an object to be identified using a method for reconstruction of a 3D point cloud. The method comprises the steps of acquiring, by a mobile device, a plurality of pictures of said object, sending the acquired pictures to a cloud server, reconstructing, by the cloud server, a 3D points cloud reconstruction of the object, performing a 3D match search in a 3D database using the 3D points cloud reconstruction, to identify the object, the 3D match search comprising a comparison of the reconstructed 3D points cloud of the object with 3D points clouds of known objects stored in the 3D database.

Abstract: The technology disclosed attenuates spatial crosstalk from sequencing images for base calling. The technology disclosed accesses a section of an image output by a biosensor, where the section of the image includes a plurality of pixels depicting intensity emission values from a plurality of clusters within the biosensor and from locations within the biosensor that are adjacent to the plurality of clusters. The plurality of clusters includes a target cluster. The section of the image is convolved with a convolution kernel, to generate a feature map comprising a plurality of features having a corresponding plurality of feature values. A weighted feature value is assigned to the target cluster, where the weighted feature value is based on one or more features values of the plurality of feature values of the feature map. The weighted feature value assigned to the target cluster is processed, to base call the target cluster.

Abstract: Apparatus are provided for generating an object classification for an object, the apparatus comprising an image sensor, a radar sensor, and a processing unit, the processing unit configured to perform the steps of: receiving image data for the object from the image sensor, operating an image-based object classifier on the image data to generate an image-based object classification, receiving radar data for the object from the radar sensor, operating a radar-based object classifier on the radar data to generate a radar-based object classification, selecting between the image-based object classification and the radar-based object classification to output as the object classification for the object, determining if a training condition is met by the radar-based object classification, and training the radar-based object classifier using the image-based object classification when the training condition is met by the radar-based object classification.

Abstract: A multilevel clustered data set for multidimensional vectors is created by defining a plurality of clusters based on each of the signed dimensions of the vectors, each dimension functioning as an axis. Vectors are assigned to each cluster by measuring cosine similarity between a vector and each axis. Sub-clusters are defined as ranges of cosine similarity values within a cluster, and each vector is assigned into the appropriate range based on their cosine similarity value with the axis of the cluster. Searching for a matching vector to a new vector is efficiently achieved in near-constant time by measuring cosine similarity for the new vector with each axis to identify the closest cluster, reusing the cosine similarity of the new vector and axis to determine which sub-cluster corresponds to the appropriate range of values, and then comparing each vector within the sub-cluster until a match is found or ruled out.

Abstract: Disclosed herein are methods and systems for detection and discrimination of optical signals from a densely packed substrate. There have broad applications for biomolecule detection near or below the diffraction limit of optical systems, including in improving the efficiency and accuracy or polynucleotide sequencing applications.

Abstract: In generation of training data for image recognition, in order to reduce a user's workload of assigning a label to a captured image, a learning device includes a processor for performing operations to generate training data and a display device, wherein the processor is configured to acquire a captured image from an image capturing device; acquire one or more candidate objects recognized based on an identification model for a recognition target included in the captured image; and display, on the display device, information on the candidate objects as respective label candidates for the captured image.

Abstract: One embodiment provides for a method for evaluation of an artificial intelligence (AI) service, the method includes partitioning, by a processor, data into in-domain data and out-of-domain data. The processor defines held-out data from the in-domain data and the out-of-domain data for evaluation by domain and sub-domain based on building a taxonomy of domains and sub-domains for the AI service. The processor further determines distribution underlying performance metrics for the held-out data using statistical processing. The processor also determines performance guarantees for multiple settings conditioned on multiple characteristics of an application scenario for the held-out data of the taxonomy based on the underlying performance metrics. The processor further provides confidence intervals based on the performance guarantees.

Abstract: In one implementation, a method of generating a confidence value for a result from a primary task is performed at an image processing system. The method includes obtaining, by a feature extractor portion of the neural network, a set of feature maps for an image data frame; generating a contextual information vector associated with the image data frame based on results from one or more auxiliary tasks performed on the set of feature maps by an auxiliary task sub-network portion of the neural network; performing, by a primary task sub-network portion of the neural network, a primary task on the set of feature maps for the image data frame in order to generate a primary task result; and generating a confidence value based on the contextual information vector, wherein the confidence value corresponds to a reliability metric for the primary task result.

Abstract: A process and device for the plenoptic capture of photographic or cinematographic images are described, both based on the correlation measure or “Correlation Plenoptic Imaging” (CPI), comprising the steps of splitting a primary light beam (6) coming from at least one light source in at least two distinct light beams (7, 8), directing said distinct light beams towards two image capturing sensors to capture images, so that the first light beam is directed towards at least one first capturing sensor to capture a spatial measure of a scene and the second light beam is directed towards a second capturing sensor to capture an angular measure of said scene, said angular measure being adapted to provide the propagation direction of the light beam coming from the scene, said spatial measure being adapted to provide the conventional two-dimensional capture of the image of the scene.

Abstract: Described is a computer-implemented method for determining uncertainties in measurement data from a measurement of an object, wherein a digital representation of the object is generated by the measurement. The object representation has items of image information, which each indicate a value of a measurement variable for the object at a defined position of the object. Statistical noise is superimposed on the image information. The method includes determining the object representation, determining a distance field from the image information relating to the object representation, determining a strength of the statistical noise in the image information, determining an uncertainty of the distance values of the distance field on the basis of the strength of the statistical noise, and determining an uncertainty of the position of at least one point on at least one material boundary surface from the uncertainty of the distance values of the distance field.

Abstract: A method of performing convolution in a neural network with variable dilation rate is provided. The method includes receiving a size of a first kernel and a dilation rate, determining at least one of size of one or more disintegrated kernels based on the size of the first kernel, a baseline architecture of a memory and the dilation rate, determining an address of one or more blocks of an input image based on the dilation rate, and one or more parameters associated with a size of the input image and the memory. Thereafter, the one or more blocks of the input image and the one or more disintegrated kernels are fetched from the memory, and an output image is obtained based on convolution of each of the one or more disintegrated kernels and the one or more blocks of the input image.

Abstract: Methods and apparatuses for performing patient registration. 3D scan data from a 3D scanner, preoperative image data, first tracking data from a first tracking system, and second tracking data from a second tracking system are mapped to a common coordinate space. The 3D scan data and the first tracking data are mapped to each other using a transformation that is determined based on a calibration relating the 3D scan coordinate space and the tracking coordinate space. The 3D scan data and the preoperative image data are mapped to each other using a surface matching algorithm. The first tracking data and the second tracking data are mapped to each other based on tracking of the patient reference device.

Abstract: A learning device learns at last one parameter of a learning model such that each intermediate feature quantity becomes similar to a reference feature quantity, the each intermediate feature quantity being calculated as a result of inputting a plurality of sets of augmentation training data to a first neural network in the learning model, the plurality of augmentation training data being generated by performing data augmentation based on same first original training data. The learning device learns at last one parameter of a second network, in the learning model, using second original training data, which is different than the first original training data, and using the reference feature quantity.

Abstract: In an embodiment, the systems and methods discussed herein are related to generating, via a processor, a Markov Decision Process (MDP), the MDP including a state space, an action space, a transition function, a reward function, and a discount factor. A reinforcement learning (RL) model is applied, via the processor, to the MDP to generate a RL agent. An input data associated with a first user is received at the RL agent. At least one counterfactual explanation (CFE) is generated via the processor and by the RL agent and based on the input data. A representation of the at least one CFE and at least one recommended remedial action is caused to transmit, via the processor, to at least one of a compute device of the first user or a compute device of a second user different from and associated with the first user.

Abstract: A method for implementing a dynamic three-dimensional lung map view for navigating a probe inside a patient's lungs includes loading a navigation plan into a navigation system, the navigation plan including a planned pathway shown in a 3D model generated from a plurality of CT images, inserting the probe into a patient's airways, registering a sensed location of the probe with the planned pathway, selecting a target in the navigation plan, presenting a view of the 3D model showing the planned pathway and indicating the sensed location of the probe, navigating the probe through the airways of the patient's lungs toward the target, iteratively adjusting the presented view of the 3D model showing the planned pathway based on the sensed location of the probe, and updating the presented view by removing at least a part of an object forming part of the 3D model.

Abstract: Systems and methods are provided for generating and using statistical data which is indicative of a difference in shape of a type of anatomical structure between images acquired by a first imaging modality and images acquired by a second imaging modality. This statistical data may then be used to modify a first segmentation of the anatomical structure which is obtained from an image acquired by the first imaging modality so as to predict the shape of the anatomical structure in the second imaging modality, or in general, to generate a second segmentation of the anatomical structure as it may appear in the second imaging modality based on the statistical data and the first segmentation.

Abstract: A control method performs a learning loop and includes acquiring first layout data generated based on a first album creation parameter from a server to which layout data generated by a user is uploaded, outputting an estimated feature value based on the first layout data, generating a second album creation parameter based on the output estimated feature value, generating third layout data based on the generated second album creation parameter and an image used for second layout data acquired from the server, and updating the first album creation parameter and the third album creation parameter based on a feature value based on the generated third layout data and a feature value based on the second layout data. When the updated album creation parameter satisfies a predetermined condition, the updated album creation parameter becomes downloadable to a terminal device of a first user.

Abstract: The present invention relates to a method of identifying a brand and a model of a vehicle. The method comprises obtaining one or more video frames. Further, the method comprises detecting one of presence or absence of text in the at least one vehicle. Upon detecting the presence of the text, the method comprises determining at least one of the brand and the model of the at least one vehicle by accumulating the text recognized in the one or more video frames. Further, identifying at least one of the brand and the model of the at least one vehicle based on an accumulated text. Upon detecting the absence of the text, the method comprises determining the brand of the at least one vehicle based on logo associated with the vehicle.

Abstract: An image of a check that is in the field of view of a camera is monitored prior to the image of the check being captured. The camera is associated with a mobile device. When the image of the check in the field of view passes monitoring criteria, an image may be taken by the camera and provided from the mobile device to a financial institution. The image capture may be performed automatically as soon as the image of the check is determined to pass the monitoring criteria. The check may be deposited in a user's bank account based on the image. Any technique for sending the image to the financial institution may be used. Feedback may be provided to the user of the camera regarding the image of the check in the field of view.

Abstract: An image of a check that is in the field of view of a camera is monitored prior to the image of the check being captured. The camera is associated with a mobile device. When the image of the check in the field of view passes monitoring criteria, an image may be taken by the camera and provided from the mobile device to a financial institution. The image capture may be performed automatically as soon as the image of the check is determined to pass the monitoring criteria. The check may be deposited in a user's bank account based on the image. Any technique for sending the image to the financial institution may be used. Feedback may be provided to the user of the camera regarding the image of the check in the field of view.