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.

An image processing apparatus for coupling to an imaging apparatus that generates a captured image covering substantially a 360-degree field of view and for transmitting an output image to an image forming apparatus includes a setting unit configured to select a type of a polyhedron that is to be constructed by folding a development printed on a medium according to the output image, a converter configured to convert coordinates in the captured image into coordinates in the development that is to be printed on the medium and folded to construct the polyhedron, such that a zenith in the captured image is printed at a topmost point of the polyhedron constructed by folding the development printed on the medium, and an image generator configured to generate the output image based on the converted coordinates.

The current document is directed to a dimensional shuffle transform (“DST”) that maps a 3D space to a one-dimensional space that preserves 3D neighborhoods within 1D neighborhoods within an implicit recursive hierarchical structure. The search for points in a 3D subspace is reduced, by the DST, to one or more searches in the transformed 1D space. The search is performed by either recursive decomposition of the 3D region indexed by the transform into subspaces, exploiting the transformed space structure, or by direct indexing into the region of interest. The searches over the subspaces generated by recursive decomposition are independent from one another, providing many opportunities for a variety of parallel, DST-enabled search methods.

An agricultural harvesting machine includes crop processing functionality configured to engage crop in a field, perform a crop processing operation on the crop, and move the processed crop to a harvested crop repository, and a control system configured to identify a weed seed area indicating presence of weed seeds, and generate a control signal associated with a pre-emergence weed seed treatment operation based on the identified weed seed area.

Texture extraction is disclosed.

An electronic device is described, that includes a processing logic configured to receive input image data and generate output image data having a different format from the input image data using an artificial neural network model. The artificial neural network model includes a plurality of encoding layer units, including a plurality of layers located at a plurality of levels, respectively. The artificial neural network model also includes a plurality of decoding layer units including a plurality of layers and configured to form skip connections with the plurality of encoding layer units at the same levels. A first encoding layer unit of a first level receives a first input feature map and outputs a first output feature map. A first output feature map is based on the first input feature map, to a subsequent encoding layer unit and a decoding layer unit at the first level.

In an aspect, an image processing apparatus includes a processor and a memory in which a plurality of images obtained by capturing images of a building and a three-dimensional model of the building are stored in association with each other. The processor is configured to perform a development process to perform development of the three-dimensional model into a two-dimensional image, an extraction process to extract defect information of the building on the basis of the plurality of images, a mapping process to perform mapping of the defect information to the two-dimensional image, and an output process to output the two-dimensional image to which the mapping is performed.

A building appraisal system conducted by a remote inspector located away from a building. A remote user connected to a user on site can share images, measurements and other data to conduct an examination of the building such as an appraisal. A processor coupled to an image sensor can be configured to receive a gross floor area of the building. Images of an interior room of the building are stored in memory. The processor determines a planar surface in the images corresponding to a floor surface of the interior room and a plurality of corners in the images forming vertices of a bounded floor area on the floor surface. The processor can compute an adjusted floor area of the building that includes the bounded floor area subtracted from the gross floor area.

There is provided an image processing device for observing an inside of an eye in a wider range. The image processing device includes an integration processing unit that integrates ophthalmologic images acquired by a plurality of types of ophthalmologic image capturing devices by correlating intraocular positions and generates one integrated image indicating intraocular information in a range wider than a range indicated by each of the ophthalmologic images.

A method for building a map includes: acquiring an original grayscale map, preprocessing the original grayscale map to obtain a preprocessed map, binarizing the preprocessed map to obtain a binarized map, performing a boundary filling to the preprocessed map and the binarized map to obtain a boundary-filled preprocessed map and a boundary-filled binarized map, performing a boundary thinning to the boundary-filled binarized map to obtain a thinned binarized map, and performing a boundary thinning to the boundary-filled preprocessed map, according to the thinned binarized map, to obtain a thinned preprocessed map.