Exemplary embodiments described herein include methods of systems for visualization of test cell environments. Exemplary embodiments may include a test cell presence system and method of providing test cell visualization that displays and permits virtual interaction with complex, three-dimensional (3-D) data sets. Exemplary embodiments permit visualization through digital reality, such as Virtual Reality (VR), Augmented Reality (AR), and other display solutions.

According to an image transmission method, a pre-processed image is inputted into a compression system, which automatically localizes at least one primary feature in the pre-processed image and then automatically determines a position within the pre-processed image of at least one landmark corresponding to the at least one primary feature. It then automatically foveates the pre-processed image to emphasize portions of the pre-processed image within a pre-determined measure of closeness to the landmark(s) and transmits the foveated image to at least one recipient system, which reconstructs a received version of the pre-processed image by inverse foveation. In one embodiment, an incompressible fluid mechanics model is used to determine the degree of foveation at different points in the pre-processed image.

An illustrative point cloud compression system accesses an input point cloud dataset representative of a point cloud comprising a plurality of points. The point cloud compression system identifies a first attribute dataset and a second attribute dataset within the input point cloud dataset. Based on an application of a transform algorithm to the first and second attribute datasets, respectively, the point cloud compression system generates 1) a first low-frequency component and a first high-frequency component of the first attribute dataset, and 2) a second low-frequency component and a second high-frequency component of the second attribute dataset. The point cloud compression system then generates an output point cloud dataset that prioritizes both the first and second low-frequency components above both the first and second high-frequency components. Corresponding methods and systems are also disclosed.

Methods of performing a complex Fourier transform of a complex data set corresponding to an image are disclosed. The methods comprise receiving a complex data set and performing a first 1D complex Fourier transform in the complex data set in Cartesian form; converting the complex data set into polar form and compressing the complex data set in polar form; performing a row-column transformation of the complex data set; decompressing the complex data set and converting the complex data set back into Cartesian form; and performing a second 1D Fourier transform in the complex data set in Cartesian form, wherein the second 1D complex Fourier transform is orthogonal to the first 1D complex Fourier transform. Corresponding systems are also disclosed, as are application to the iterative computation of computer-generated holograms.

A method of classifying motion from video frames involves generating motion frames indicative of changes in pixel values between pairs of frames. The method also involves determining one-dimensional feature values based on the video frames or motion frames, such as the statistical values or linear transformation coefficients. Each one-dimensional feature value may be stored in a buffer, from which additional temporal feature values can be extracted indicative of the change of the one-dimensional feature values across a set of frames. A classifier may receive the one-dimensional feature values and the additional temporal feature values as inputs, and determine the class of motion present in the video frames. Some classes of motion, such as irrelevant motion, may be considered irrelevant to the execution of certain motion-triggered actions, such that the method may involve suppressing the performance of a motion-triggered action based on the determined class of motion.

A method for deblurring a target image includes receiving a burst of images that includes the target image; partitioning respective images of the burst of images into respective patches; converting, to a frequency domain, the respective patches into respective transform patches; selecting a first set of corresponding transform patches from the respective transform patches, where the first set of the corresponding transform patches includes a respective transform patch for a respective image of the burst of images; obtaining, using a neural network, respective weight maps for the corresponding transform patches; obtaining a deblurred transform patch by combining the first set of corresponding transform patches using the respective weight maps; obtaining a first deblurred patch by converting the deblurred transform patch to a pixel domain; and obtaining a deblurred image of the target image using the first deblurred patch.

The present invention provides a method and a device for detecting interest points in an image. The method includes: acquiring an original input image; performing down-sampling processing on the original input image, so as to obtain a plurality of sampling images with different resolutions; dividing each sampling image into a plurality of small image blocks; performing filtering processing on the plurality of small image blocks in each sampling image in sequence by using Laplacian-of-Gaussian filters, so as to obtain filtered images of the plurality of small image blocks in each sampling image; and acquiring interest points in an image in filtered images of the plurality of small image blocks in each sampling image. The present invention is used for solving the problems of more memory consumption and a low detection speed in the prior art.

First visual information defining the visual content in a first projection may be accessed. Second visual information defining lower versions of the visual content in the first projection may be accessed. A transformation of the visual content from the first projection to a second projection may be determined. The transformation may include a visual compression of a portion of the visual content in the first projection. The portion may be identified. An amount of the visual compression of the portion may be determined. One or more lower resolution versions of the visual content may be selected. The visual content may be transformed using the one or more lower resolution versions of the visual content.

Techniques for representing a scene or map based on statistical data of captured environmental data are discussed herein. In some cases, the data (such as covariance data, mean data, or the like) may be stored as a multi-resolution voxel space that includes a plurality of semantic layers. In some instances, individual semantic layers may include multiple voxel grids having differing resolutions. Multiple multi-resolution voxel spaces may be merged to generate combined scenes based on detected voxel covariances at one or more resolutions.

In some embodiments, a computing device uses a blue noise sampling operation to identify source pixels from an input image defining respective pixel sets. Each pixel set is associated with a respective weight matrix for a down-scaling operation. The blue noise sampling operation causes an overlap region between first and second pixel sets. The computing device assigns an overlap pixel in the overlap region to the first weight matrix based on the overlap pixel being closer to the first source pixel. The computing device modifies the second weight matrix to exclude the overlap pixel from a portion of the down-scaling operation involving the second weight matrix. The computing device performs the down-scaling operation on the input image by combining the first pixel set into a first target pixel with the first weight matrix and combining the second pixel set into a second target with the modified second weight matrix.