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 small defect detection apparatus installed online in a glass sheet processing system includes a line scan camera, a background screen including contrasting elements arranged in a pre-defined pattern, an upstream conveyor and a downstream conveyor, wherein the upstream conveyor and downstream conveyor are positioned end-to-end, spaced apart by a selected size gap such that the camera may acquire multiple images of the background screen as the unsupported portion of the glass sheet is conveyed over the gap, and a computer programmed to execute logic for receiving the set of image data comprising multiple images of the background screen and identifying small defects in the glass from the data. The system may also include a glass sheet part identifier and a programmable control including logic for analyzing acquired image data and selecting an area of interest on the glass sheet for the analysis.

A method, system and computer program product for recognizing a face are provided, comprising: acquiring an image for the face; detecting a set of first feature points representing detail features of the image; extracting, for each first feature point in the set of first feature points, a first descriptor describing feature information on the first feature point; acquiring, for each second feature point in a set of second feature points, a second descriptor describing feature information on the second feature point; detecting matched feature point pairs between the set of first feature points and the set of second feature points, based on the first descriptor and the second descriptor; calculating the number of the matched feature point pairs; and recognizing the image as being consistent with the registered image, if the number of the matched feature point pairs is larger than a first preset threshold.

A method, system and mobile device can be used by a subject in diagnosing a disease or virus or other illness. The system can capture and analyze olfactory information providing a diagnosis based on the olfactory information. The system can also capture and output biometric data corresponding to the subject and can further include at least one camera or video sensor that can result in a diagnosis or a microphone or other acoustic sensor that can result in another diagnosis. Other sensors providing other corresponding diagnoses can be included. A sensor fusion component can receive and combine the biometric data and the various diagnoses results and further determine a confidence score. An event record creator compiles the biometric data and the confidence scores to create an event record having a higher confidence score with respect to a final diagnosis result. A data storage device stores the event record.

Methods and kits for determining a propensity to develop Type 1 diabetes mellitus (T1DM) and for Type 2 diabetes mellitus (T2DM) in an individual by measuring an asymmetry of at least one captured fingerprint from the individual are described.

A method and system architecture for designing a compressive sensing matrix for machine learning includes receiving an image associated with a classification task and; generating a sensing matrix. The sensing matrix includes an array of nonzero elements of the image. A prism array of prism elements is in communication with the sensing matrix. A row of values corresponding with an input angle of the prism array is mapped to a respective column corresponding with a detector. Then the detector detects light refracted at an output angle dictated by the physical shape of the prism element. A physical model of the detector is fabricated and generates a compressed representation of the image. A machine learning classification algorithm is applied to the compressed representation of the image and generates an optimized non-invertible final determination of the image.

A small defect detection apparatus installed online in a glass sheet processing system includes a line scan camera, a background screen including contrasting elements arranged in a pre-defined pattern, an upstream conveyor and a downstream conveyor, wherein the upstream conveyor and downstream conveyor are positioned end-to-end, spaced apart by a selected size gap such that the camera may acquire multiple images of the background screen as the unsupported portion of the glass sheet is conveyed over the gap, and a computer programmed to execute logic for receiving the set of image data comprising multiple images of the background screen and identifying small defects in the glass from the data. The system may also include a glass sheet part identifier and a programmable control including logic for analyzing acquired image data and selecting an area of interest on the glass sheet for the analysis.

A learning device generates learned data to be used for anomaly indication detection. The learning device includes a preprocessing unit that subtracts, from a value at each of points in one cycle of normal data, an average of values at corresponding points in normal data from one to N cycles ago in computing difference values with respect to the preceding N cycles, the one cycle corresponding to a specified time length, N being an integer greater than or equal to 2, the normal data being data from a normal state; and a first waveform analysis unit that generates, through waveform similarity analysis using the difference values with respect to the preceding N cycles, normal waveforms and a normality determination threshold to be used in determining whether or not there is normality as the learned data.

A system and method of combining different sensor data for higher reliable diagnosis information on a portable mobile device includes using a camera for imaging a region of interest of a subject to obtain an image signal, a microphone for capturing acoustic information from the subject, and one or more processors. The one or more processors can be configured to spectrally analyze the image signal, estimate a first vital-sign of the subject corresponding to a diagnosis using the image signal, analyze the acoustic information, estimate a second vital-sign of the subject corresponding to the diagnosis using the acoustic information, and combine the first vital sign with the second vital-sign to provide a higher confidence level diagnostic of the diagnosis.

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.