A method, apparatus and computer program product are provided for learning to generate maps from raw geospatial observations from sensors traveling within an environment. Methods may include: receiving a plurality of sequences of geospatial observations from discrete trajectories; aligning the trajectories to generate aligned geospatial observations; concatenating the aligned geospatial observations; processing the concatenated, aligned geospatial observations using one or more Set Transformers; generating, from the at least one Set Transformer, map geometries including objects from the geospatial observations; and providing at least one of navigational assistance or at least semi-autonomous vehicle control based on the map geometries. According to some embodiments, aligning the trajectories includes applying a geospatial offset for one or more of the trajectories.

According to one or more embodiments, a non-transitory computer readable medium storing a program which, when executed, causes a computer to perform processing comprising acquiring image data, applying style transfer to the image data a plurality of times based on one or more style images, and outputting data after the style transfer is applied.

A method includes applying both a first dedicated functional-anatomical registration scheme to a first volume of interest to deform the first volume of interest and a second dedicated functional-anatomical registration scheme to a second volume of interest to deform the second volume of interest, wherein the first volume of interest at least partially encompasses the second volume of interest. The method includes identifying or segmenting relevant organs or anatomical structures related to a first group and a second group in the first volume of interest and the second volume of interest, respectively; generating a spatially smooth-transition weight mask that gives higher weight to image data corresponding to the identified or segmented relevant organs or anatomical structures related to the first group and the second group; and generating a final cohesive registered image volume from the first image volume and the second image volume utilizing the spatially smooth-transition weight mask.

Aspects of the disclosure provide a device for processing frames with aliasing artifacts. For example, the device can include a motion estimation circuit, a warping circuit coupled to the motion estimation circuit, and a temporal decision circuit coupled to the warping circuit. The motion estimation circuit can estimate a motion value between a current frame and a previous frame. The warping circuit can warp the previous frame based on the motion value such that the warped previous frame is aligned with the current frame and determine whether the current frame and the warped previous frame are consistent. The temporal decision circuit can generate an output frame, the output frame including either the current frame and the warped previous frame when the current frame and the warped previous frame are consistent, or the current frame when the current frame and the warped previous frame are not consistent.

A spirometry system includes an imaging device configured to capture upper body movement images of a subject during inhalation and exhalation of the subject. The system further includes at least one controller configured to receive the captured images from the imaging device and, based upon the received images, determine at least one of an image-based spirometry flow-volume curve for the subject or an image-based spirometry parameter for the subject.

A method of content production includes generating a survey of a performance area that includes a point cloud representing a first physical object, in a survey graph hierarchy, constraining the point cloud and a taking camera coordinate system as child nodes of an origin of a survey coordinate system, obtaining virtual content including a first virtual object that corresponds to the first physical object, applying a transformation to the origin of the survey coordinate system so that at least a portion of the point cloud that represents the first physical object is substantially aligned with a portion of the virtual content that represents the first virtual object, displaying the first virtual object on one or more displays from a perspective of the taking camera, capturing, using the taking camera, one or more images of the performance area, and generating content based on the one or more images.

An image coloring method includes: acquiring first color a priori information about an image-to-be-colored; transforming the first color a priori information to obtain second color a priori information aligned with the image-to-be-colored; downsampling the image-to-be-colored to obtain a first image feature; performing modulation coloring processing on the first image feature based on the second color a priori information to obtain a second image feature; and upsampling the second image feature based on the second color a priori information to obtain a first colored image, where the first colored image is aligned with the image-to-be-colored.

Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.

A system can receive downhole acquisition data relating to a wellbore. The system can pre-process the downhole acquisition data. The system can generate an incomplete borehole image using the downhole acquisition data. The system can determine a sparse representation based on the incomplete borehole image by performing an optimization with respect to the incomplete borehole image. The system can generate a complete borehole image based on an inverse of the sparse representation.

Examples of methods are described. In some examples, a method includes determining, using a variational autoencoder model, a latent space representation. In some examples, the latent space representation is of object model data. In some examples, the method includes predicting manufacturing powder degradation. In some examples, predicting the manufacturing powder degradation is based on the latent space representation.