Abstract: Techniques for integrating sensor data into a scene or map based on statistical data of captured environmental data are discussed herein. The data may be stored as a multi-resolution voxel space and the techniques may comprise first applying a pre-alignment or localization technique prior to fully integrating the sensor data.

Abstract: An audio acquisition device positioning method is provided. In the method, a first image that includes an audio acquisition device is obtained. The audio acquisition device in the first image is identified. First coordinate data of the identified audio acquisition device in the first image is obtained. First displacement data is determined according to the first coordinate data and historical coordinate data of the audio acquisition device. First coordinates of the audio acquisition device are determined according to the first displacement data.

Abstract: A computer-implemented system and method of image cross-correlation improves the sub-pixel accuracy of the correlation surface and subsequent processing thereof. One or both of the template or search windows are resampled using the fractional portions of the correlation offsets X and Y produced by the initial image cross-correlation. The resampled window is then correlated with the other original window to produce a resampled cross-correlation surface. Removing the fractional or sub-pixel offsets between the template and search windows improves the “sameness” of the represented imagery thereby improving the quality and accuracy of the correlation surface, which in turn improves the quality and accuracy of the FOM or other processing of the correlation surface. The process may be iterated to improve accuracy or modified to generate resampled cross-correlation surfaces for multiple possible offsets and to accept the one with the most certainty.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for determining a target anchor, a device and a storage medium. The method may include: extracting a plurality of feature maps of an original image using a feature extraction network; inputting the plurality of feature maps into a feature pyramid network to perform feature fusion, to obtain a plurality of fused feature maps; and using a region proposal network to implement operations as follows: determining an initial anchor of a network head using the fused feature map, based on a size of each fused feature map, and determining an offset parameter of the initial anchor, based on a ratio of the size of the fused feature map to the original image, and generating a plurality of candidate anchors in different directions, based on the offset parameter of the initial anchor.

Abstract: An apriori guidance network for multitask medical image synthesis is provided. The apriori guidance network includes a generator and a discriminator, wherein the generator includes an apriori guidance module configured to convert an input feature map into a target modal image pointing to a target domain according to an apriori feature, and the apriori feature is a deep feature of the target modal image. The generator is configured to generate a corresponding target domain image by taking the apriori feature of the target modal image and source modal image data as an input. The discriminator is configured to discriminate an authenticity of the target domain image outputted by the generator.

Abstract: Provided is an information processing apparatus that includes an acquisition section that acquires a multi-spectral image consistent with an imaging result of a subject by using light divided into a plurality of wavelength bands and an extraction section that extracts a feature value by performing a computational process on the multi-spectral image on the basis of coefficients appropriate to absorption spectral properties of one or more pigments.

Abstract: Methods, non-transitory computer-readable storage media, and computer or computer systems directed to detecting, analyzing, and tracking roads and grading activity using satellite or aerial imagery in combination with a machine learned model are described.

Abstract: The scaled Peak Signal-to-Noise Ratio (PSNR) method scales PSNR to 1.00 or 100% from infinite decibel values. The PSNR describes the ratio between the maximum possible value (power) of a signal and the power of signal's noise. Noise affects the quality of its representation. Signals dynamically fluctuate from the smallest to largest possible quantity. The unsettled range to maximum from minimum values of the noise is unknown. Therefore, the PSNR usually forms on a logarithmic way (log) of describing a ratio, the decibel (dB). The dB is able to gauge the level of power, communication signal (intensity or voltage), sound pressure, and intensity of several other items including images.

Abstract: An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.

Abstract: Methods, non-transitory computer-readable storage media, and computer or computer systems directed to detecting, analyzing, and tracking stages of housing construction using satellite or aerial imagery in combination with a machine learned model are described.

Abstract: A learning device, an image generating device, a learning method, an image generating method, and a program are provided which can improve accuracy of estimation of an environment outside an angle of view of an image input to an image generating section. A second learning data obtaining section (64) obtains an input image. A second learning section (66) obtains result data indicating a result of execution of semantic segmentation on the input image. An input data generating section (38) generates input data obtained by coupling the input image and the result data to each other. By using the input data as input, the second learning section (66) performs learning of a wide view angle image generating section (28) that generates, in response to input of data obtained by coupling an image to a result of execution of semantic segmentation on the image, an image having a wider angle of view than the image.

Abstract: A tracking device includes a processor including hardware, and the processor sets a start frame, extracts multiple representative points of a contour of a tracking target, tracks the extracted multiple representative points, performs outlier determination based on an interrelationship of the tracked multiple representative points, performs a process of removing an outlier representative point determined to be an outlier, and extracts new representative points based on multiple representative points after the process of removing the outlier representative point when a given condition is met.

Abstract: Systems, methods, and computer-readable media for evaluating a set of computed tomography data associated with a lattice structure. The lattice structure may be additively manufactured. The computed tomography data may be segmented using a filter for identifying blob-like structures to identify nodes present within the lattice structure. A three-dimensional path traversal is applied to volumetric data to identify a plurality of struts within the lattice structure that are compared to corresponding struts within a set if three-dimensional mesh data of the lattice structure to identify defective struts. Further, two-dimensional slices may be extracted from each of the computed tomography data and the mesh data and compared to identify one or more inconsistencies indicative of defects within the lattice structure.

Abstract: Methods and systems for image processing are provided. A target image may be acquired, wherein the target image may include a plurality of elements, an element of which may correspond to a pixel or a voxel. The target image may be decomposed into at least one layer, wherein the at least one layer may include a low frequency sub-image and a high frequency sub-image. The at least one layer may be transformed. The transformed layer may be reconstructed into a composite image.

Abstract: A projection device and a projection picture correction method thereof are provided. When the projection device performs projection toward a projection surface, a plurality of target coordinates of a plurality of target vertexes are obtained based on a plurality of planes of the projection surface. The plurality of planes are not coplanar with each other, and the plurality of target vertexes form a target polygon. A first direction scaling process is performed respectively on a plurality of first image portions of an original trapezoidal image and a trapezoidal image block is generated. A second direction scaling process is performed respectively on a plurality of second image portions of the trapezoidal image block and a target image block aligned with the target polygon is generated. An output image including the target image block is projected onto the projection surface.

Abstract: An integrated navigation solution is provided for a device within a moving platform. Motion sensor data from a sensor assembly of the device is obtained, optical samples from at least one optical sensor for the platform are obtained and map information for an environment encompassing the platform is obtained. Correspondingly, an integrated navigation solution is generated based at least in part on the obtained motion sensor data using a nonlinear state estimation technique, wherein the nonlinear state estimation technique uses a nonlinear measurement model for optical sensor data. Generating the integrated navigation solution includes using the sensor data with the nonlinear state estimation technique and integrating the optical sensor data directly by updating the nonlinear state estimation technique using the nonlinear measurement model and the map information. The integrated navigation solution is then provided.

Abstract: The present invention provides a pose estimation method for an unmanned aerial vehicle based on point, line and plane feature fusion, the method comprises the following steps: S1 extracting an RGB image and a depth map by using an RGB-D camera; S2 constructing a plane parameter space (PPS) according to the depth map, and mapping the depth map from a Cartesian space to the plane parameter space (PPS); S3 constructing an inverse octree structure, comprising: reversely constructing cell nodes for the plane parameter space (PPS) according to an octree rule, and for each cell node, performing fitting by using Gaussian distribution; S4 extracting a plane feature, comprising: extracting the plane feature on the basis of degree of feature extraction algorithm of the inverse octree; S5 extracting a linear feature, comprising: extracting a linear feature on the RGB image by using LSD algorithm detection.

Abstract: Embodiments of this disclosure include a method and an apparatus for processing image. The method may include obtaining a to-be-processed image and performing image semantic segmentation on the to-be-processed image to obtain a semantically-segmented image. The semantically-segmented image may include a target region and a non-target region obtained through the semantic segmentation. The method may further include performing pose recognition on the to-be-processed image, to obtain a pose-recognized image recognizing skeletal region. The method may further include fusing the target region and the non-target region of the semantically-segmented image with the skeletal region of the pose-recognized image, to obtain a trimap comprising foreground region, background region, and recognition region. The method may further include generating, according to the to-be-processed image and the trimap, a transparency mask image for separating image from the to-be-processed image.

Abstract: An object tracking device includes a storage unit that stores in advance a reference for a movement amount of an object between frames for each position or area on a fisheye image, a determining unit that determines, based on a position of the object in a first frame image and the reference for a movement amount associated with the position of the object in the first frame image, a position of a search area in a second frame image subsequent to the first frame image, and a search unit that searches the search area in the second frame image for the object to specify a position of the object in the second frame image.

Abstract: Example apparatus disclosed herein are to process a first image of a first video segment from the image capture sensor with a machine learning algorithm to determine a first score for the first image, the machine learning algorithm to detect actions associated with images, the actions associated with labels. Disclosed example apparatus are also to determine a second score for the first video segment based on respective first scores for corresponding images in the first video segment. Disclosed example apparatus are further to determine, based on the second score, whether to retain the first video segment in the memory.