A structure that forms a visual representation may include a first outer layer, a second outer layer, and an interlayer being disposed between the first outer layer and the second outer layer. The interlayer may have a first side adjacent to the first outer layer and a second side adjacent to the second outer layer. The interlayer includes a plurality of cuts extending from the first side of the interlayer towards the second side of the interlayer. Each of the plurality of cuts may have an angle with respect to a plane formed by a surface of the first side of the interlayer. Each angle for at least a portion of the plurality of cuts is based on one or more pixel values of at least one image that forms the basis of the visual representation.

The present disclosure is related to an optical encoder which is configured to provide precise coding reference data by feature recognition technology. To apply the present disclosure, it is not necessary to provide particular dense patterns on a working surface. The precise coding reference data can be generated by detecting surface features of the working surface.

The present disclosure relates to systems, methods, and non-transitory computer readable media for selecting hyper-parameter sets by utilizing a modified Bayesian optimization approach based on a combination of accuracy and training efficiency metrics of a machine learning model. For example, the disclosed systems can fit accuracy regression and efficiency regression models to observed metrics associated with hyper-parameter sets of a machine learning model. The disclosed systems can also implement a trade-off acquisition function that implements an accuracy-training efficiency balance metric to explore the hyper-parameter feature space and select hyper-parameters for training the machine learning model considering a balance between accuracy and training efficiency.

A method for providing assistance in driving includes capturing an image of a second moving vehicle when a first moving vehicle is moving and obtaining basic information of the second moving vehicle according to the image thereof, the basic information of the second moving vehicle comprising weight information of the second moving vehicle. Driving information of the first moving vehicle is obtained, and a safe distance between the first moving vehicle and the second moving vehicle is determined according to the driving information of the first moving vehicle and the basic information of the second moving vehicle. The current distance between the first moving vehicle and the second moving vehicle is detected, and a warning is output if the distance between the first moving vehicle and the second moving vehicle is less than the safe distance.

The disclosure relates to systems, methods and computer readable media for providing network-based identification, generation and management of a unique damage (finger) print of vehicle(s) by geodetic mapping of stable key points onto a ground truth 3D model of the vehicle, and vehicle parts—identified from the raw images using supervised and unsupervised machine learning. Specifically, the disclosure relates to System and methods for the generation of unique damage print on a vehicle that is obtained from captured images of the damaged vehicle, photogrammetrically localized to a specific vehicle part, and the computer programs enabling the method, the damage print configured to be used, for example, in fraud detection in insurance claims.

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.

Computer and mobile device-based systems and computer-implemented methods are described for automated medication adherence improvement for patients in medication-assisted treatments. The computer and mobile device-based systems includes modules and components to help patients in identifying prescribed medications, logging medication events, and to provide patients with personalized and targeted adherence enhancing interventions consisting of short questions, tips, advices, suggestions, strategies etc. by applying data mining and statistical analysis techniques on the individual and population-level data collected primarily from the same system.

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.