An information processing device acquires an image captured by a transmission electron microscope. The information processing device, for each partial region in the image, executes a two-dimensional Fourier transform on an image of the partial region. The information processing device, based on results obtained by executing the two-dimensional Fourier transform on each of the partial regions, performs clustering of frequency strengths obtained from the results of the two-dimensional Fourier transform. The information processing device determines regions of different crystallinity in the image, based on results of the clustering.

Concepts for classifying data are presented. Data to be classified is processed in accordance with a data decomposition algorithm so as to generate a plurality of data components, wherein each data component is associated with a respective different value or range of data transience. A subset of the data to be classified based on the plurality of data components. The selected subset of the obtained data is provided to a data classification process for classifying the data.

Systems and methods for detecting and aligning land property boundaries on aerial imagery are provided. The system receives an aerial imagery having land properties. The system applies a feature encoder having a plurality of levels to the aerial imagery. A first level of the plurality of levels includes a convolution block and a discrete wavelet transform layer. The discrete wavelet transform layer decomposes an input feature tensor to the first level into a low-frequency band and a high-frequency band. The high-frequency band is cached and processed with side-convolutional blocks before the high-frequency band are passed to a feature decoder. The system applies the feature decoder to an output of the feature encoder based at least in part on one of inverse discrete wavelet transform layers. The system determines boundaries of the one or more land properties based at least in part on a boundary cross-entropy loss function.

A CNN processing device includes: a kernel storage unit configured to store kernels used in a convolution operation; a table storage unit configured to store a Fourier base function used in the convolution operation; and a convolution operation unit configured to model an element g in coefficients G of the kernels in a convolutional neural network (CNN) using N-order (N is an integer equal to or greater than 1) Fourier series expansion and to perform a convolution operation on processing target information that is information on a processing target through a CNN method using the kernels and the Fourier base function.

Provided in a fingerprint image preprocessing method including receiving an input fingerprint image, performing a short-time Fourier transform (STFT) on the input fingerprint image to obtain a transformed fingerprint image, comparing the input fingerprint image and the transformed fingerprint image, and generating a combined image by combining the input fingerprint image and the transformed fingerprint image based on a result of the comparing.

In the invention for generating a set of shape descriptors for a set of two or three dimensional geometric shapes in order to arrive at an unified efficient low-dimensional representation of the complete set of shapes to enable memory and disk efficient storage, indexing, referencing, and making the complete set available for further processing, at first a set of N feature locations having a distance from the shapes is read. Further, a set of M wave numbers is read and a parameter controlling degree of locality of the features. Then, for each shape s in the set of shapes {S.sub.s, s=1, . . . , N.sub.s} and for each of the N feature locations and M wave numbers a feature descriptor is calculated according to

[00001]$f_s\left(n,m\right)={\left({.Math.{\stackrel{.fwdarw.}{R}}_n.Math.}_{}\right)}^{}.Math.e^{-{\mathrm{ik}}_m.Math.R_n}.Math.C.Math.{}_{\mathrm{shape}.Math..Math.s}.Math.d^3.Math.\stackrel{.fwdarw.}{s}.Math.\frac{e^{{\mathrm{ik}}_m.Math.{.Math.\stackrel{.fwdarw.}{s}-{\stackrel{.fwdarw.}{R}}_n.Math.}_2}}{{\left({.Math.\stackrel{.fwdarw.}{s}-{\stackrel{.fwdarw.}{R}}_n.Math.}_{}\right)}^{}},$

where the integral is summing all contributions from each point of shape s. The calculated feature descriptors are then assigned to elements of an M.Math.N dimensional vector as the shape descriptor for shape s

{right arrow over (F)}.sub.s=(f.sub.s(n=

This analytical data analysis method uses machine learning of analysis result data (31) measured by an analyzer (1), and includes generating simulated data (32) in which a data variation has been added to the analysis result data (31) within a range that does not affect identification, performing the machine learning using the generated simulated data (32), and performing discrimination using a discrimination criterion (23b) obtained through the machine learning.

Concepts for classifying data are presented. Data to be classified is processed in accordance with a data decomposition algorithm so as to generate a plurality of data components, wherein each data component is associated with a respective different value or range of data transience. A subset of the data to be classified based on the plurality of data components. The selected subset of the obtained data is provided to a data classification process for classifying the data.

An information processing device acquires an image captured by a transmission electron microscope. The information processing device, for each partial region in the image, executes a two-dimensional Fourier transform on an image of the partial region. The information processing device, based on results obtained by executing the two-dimensional Fourier transform on each of the partial regions, performs clustering of frequency strengths obtained from the results of the two-dimensional Fourier transform. The information processing device determines regions of different crystallinity in the image, based on results of the clustering.

An object detection method, for detecting a target object, comprising: capturing at least two detection portions with a first aspect ratio from an input image with a second aspect ratio; confirming whether any object is detected in each of the detection portions and obtaining corresponding boundary boxes for detected objects; and wherein the first aspect ratio is different to the second aspect ratio.