A point cloud data processing apparatus 11 includes a processor configured to acquire first form information that indicates a feature of a form of a first object, specify an object region of a second object that is identified from an image and that corresponds to the first form information, select second-object point cloud data, in point cloud data, that corresponds to the object region, on the basis of the object region, acquire second form information that indicates a feature of a form of the second object, on the basis of the second-object point cloud data, and compare the first form information with the second form information and perform determination as to whether the second object is the first object.

A feature learning system (100) includes a similarity definition unit (101), a learning data generation unit (102), and a learning unit (103). The similarity definition unit (101) defines a degree of similarity between two classes related to two feature vectors, respectively. The learning data generation unit (102) acquires the degree of similarity, based on a combination of classes to which a plurality of feature vectors acquired as processing targets belong, respectively, and generates learning data including the plurality of feature vectors and the degree of similarity. The learning unit (103) performs machine learning using the learning data.

A method for generating a highly distinctive signature of a certain diamond, the method may include generating, based on one or more images of the certain diamond, a certain diamond signature of the certain diamond; finding, out of a group of reference diamonds, other diamonds having other diamond signatures; wherein the finding comprises calculating similarities between the certain diamond signature and reference diamond signatures of the reference diamonds of the group; and generating a new certain diamond signature that significantly differs from signatures of the other diamonds.

A measurement system includes a processor. Based on information measured by a camera that takes an image of a measurement target and an auxiliary object arranged on the measurement target, the processor acquires first point cloud data representing a three-dimensional geometry of the measurement target including the auxiliary object. Based on the first point cloud data and second point cloud data that is known and that represents a three-dimensional geometry of the measurement target, the processor eliminates point cloud data of the auxiliary object from the first point cloud data. The processor compares the first point cloud data, from which the point cloud data of the auxiliary object has been eliminated, with the second point cloud data. The processor displays information relating to a result of comparison on a display device.

There is provided a computerized system and method of generating a unique identification associated with a gemstone, usable for unique identification of the gemstone. The method comprises: obtaining one or more images of the gemstone, the one or more images captured at one or more viewing angles relative to the gemstone and to a light pattern, thus giving rise to a representative group of images; processing the representative group of images to generate a set of rotation-invariant values informative of rotational cross-correlation relationship characterizing the images in the representative group; and using the generated set of rotation-invariant values to generate a unique identification associated with the gemstone. The unique identification associated with the gemstone can be further compared with an independently generated unique identification associated with the gemstone in question, or with a class-indicative unique identification.

We propose a neural network-based base caller that detects and accounts for stationary, kinetic, and mechanistic properties of the sequencing process, mapping what is observed at each sequence cycle in the assay data to the underlying sequence of nucleotides. The neural network-based base caller combines the tasks of feature engineering, dimension reduction, discretization, and kinetic modelling into a single end-to-end learning framework. In particular, the neural network-based base caller uses a combination of 3D convolutions, 1D convolutions, and pointwise convolutions to detect and account for assay biases such as phasing and prephasing effect, spatial crosstalk, emission overlap, and fading.

Systems and methods change a user interface for the purpose of guiding a user in supplementing a product listing with an image to evidence the product's authenticity. Example embodiments include a machine-implemented method for accessing at least one database to retrieve an authenticity criterion mapped to a product and at least one reference image that depicts adequate detail of a product specimen to fulfill the authenticity criterion. The machine can further cause a user device to display the reference image to the user along with a suggestion that the user submit a candidate image depicting similar detail of the product. In some example embodiments, the method further includes retrieving the candidate image, confirming receipt of the candidate image, and displaying the candidate image, as well as adjusting a rank for a candidate specimen based on various factors.

In some aspects, the techniques described herein relate to systems, methods, and computer readable media for data pre-processing for stereo-temporal image sequences to improve three-dimensional data reconstruction. In some aspects, the techniques described herein relate to systems, methods, and computer readable media for improved correspondence refinement for image areas affected by oversaturation. In some aspects, the techniques described herein relate to systems, methods, and computer readable media configured to fill missing correspondences to improve three-dimensional (3-D) reconstruction. The techniques include identifying image points without correspondences, using existing correspondences and/or other information to generate approximated correspondences, and cross-checking the approximated correspondences to determine whether the approximated correspondences should be used for the image processing.

An object recognition method of a refrigerator is disclosed. The disclosed object recognition method of a refrigerator comprises the steps of: obtaining a captured image of a storage compartment of a refrigerator; checking the change in the imaging direction of an image capturing device which has captured the image of the storage compartment, when a change in the captured image is confirmed compared to a previously stored image; and performing an object recognition operation of the captured image when the imaging direction is maintained.

An electronic endoscope processor includes a converting means for converting each piece of pixel data that is made up of n (n≥3) types of color components and constitutes a color image of a biological tissue in a body cavity into a piece of pixel data that is made up of m (m≥2) types of color components, m being smaller than n; an evaluation value calculating means for calculating, for each pixel of the color image, an evaluation value related to a target illness based on the converted pieces of pixel data that are made up of m types of color components; and a lesion index calculating means for calculating a lesion index for each of a plurality of types of lesions related to the target illness based on the evaluation values calculated for the pixels of the color image.