Embodiments may include novel techniques to improve detection of objects in images, for example, in Digital Breast Tomography and that are applicable to ensembles of detectors. For example, a method may comprise generating a plurality of candidate bounding boxes for each of a plurality of image slices of imaged tissue, each generated candidate bounding box having a probability score, collecting at least some of the generated candidate bounding boxes for each slice, generating a heat map of the filtered candidate bounding boxes and filtering the candidate bounding boxes in the heat map based on a first threshold of values in the heat map, performing Non-Maximum Suppression on the heat map filtered candidate bounding boxes, and outputting at least one bounding box based on the probability score.

The present disclosure relates to a tag and a method, performed by the tag, of transmitting a response signal to a tag search signal. Specifically, the disclosed method of transmitting a response signal includes operations of receiving, from at least one of a plurality of slave nodes, the tag search signal including identification data for identifying the tag, charging an energy storage element in the tag by using the received tag search signal, obtaining the identification data for identifying the tag from the received tag search signal, determining whether the obtained identification data matches identification information previously stored in the tag, and outputting a response signal to the tag search signal when the energy storage element is charged greater than a predetermined value and the obtained identification data matches the identification information previously stored in the tag.

Methods and systems for adaptive, template-independent handwriting extraction from images using machine learning models and without manual localization or review. For example, the system may receive an input image, wherein the input image comprises native printed content and handwritten content. The system may process the input image with a model to generate an output image, wherein the output image comprises extracted handwritten content based on the native handwritten content. The system may process the output image to digitally recognize the extracted handwritten content. The system may generate a digital representation of the input image, wherein the digital representation comprises the native printed content and the digitally recognized extracted handwritten content.

The technology disclosed present systems and methods to produce an enhanced resolution image from images of a target using structured illumination microscopy (SIM). The method includes transforming at least three images of the target captured by a sensor in a spatial domain into a Fourier domain to produce at least three frequency domain matrices that each include first blocks of complex coefficients and redundant second blocks of complex coefficients that are conjugates to the first blocks. The method includes reducing computing resources required to produce the enhanced resolution image by using first blocks of complex coefficients to produce at least three phase-separated half-matrices in the Fourier domain. The method includes performing one or more intermediate transformation on the phase-separated half-matrices to produce realigned shifted half-matrices. The method includes calculating complex coefficients of second blocks in the Fourier domain to produce full matrices from half-matrices.

The present invention discloses system and method for fake face identification. The system is a multi-spectral sensing based bio-security system. The system uses CNN module along with thermal sensors to detect human face and also detects the human temperature. The system authenticates a human face and in case of temperature generates an alarm as an alert.

A system may transform sensor data from a sensor domain to an image domain using data-driven manifold learning techniques which may, for example, be implemented using neural networks. The sensor data may be generated by an image sensor, which may be part of an imaging system. Fully connected layers of a neural network in the system may be applied to the sensor data to apply an activation function to the sensor data. The activation function may be a hyperbolic tangent activation function. Convolutional layers may then be applied that convolve the output of the fully connected layers for high level feature extraction. An output layer may be applied to the output of the convolutional layers to deconvolve the output and produce image data in the image domain.

Methods and systems are disclosed using camera devices for deep channel and Convolutional Neural Network (CNN) images and formats. In one example, image values are captured by a color sensor array in an image capturing device or camera. The image values provide color channel data. The captured image values by the color sensor array are input to a CNN having at least one CNN layer. The CNN provides CNN channel data for each layer. The color channel data and CNN channel data is to form a deep channel image that stored in a memory. In another example, image values are captured by sensor array. The captured image values by the sensor array are input a CNN having a first CNN layer. An output is generated at the first CNN layer using the captured image values by the color sensor array. The output of the first CNN layer is stored as a feature map of the captured image.

A system and method are provided for obtaining a pupil response profile for a subject. The method include: obtaining scan data as frames of a pupil response over time prior to, during and after exposure to a flash of a light source; locating a candidate pupil to be measured from the scan data; image processing the scan data to obtain a set of pupil candidate measurements to generate a graph of pupil measurements against time; filtering the graph to produce a final set of pupil measurements forming a pupil response profile. The method may also include: measuring profile parameters from the pupil response profile; and using the profile parameters to determine aspects of the pupil response.

This disclosure provides systems, methods, and apparatus detecting defects in a substrate. An image of the substrate is compared with a reference image to identify potential defects. Images corresponding to the potential defects are processed sequentially by a set of classifiers to generate a set of images that include a defect. The set of classifiers can be arranged to have increasing accuracy. A subset of the images corresponding to the potential defects is processed by a type classifier that can determine the type, size, and location of the defect in the images. The defects can be further processed to determine the severity of the defects based on the location of the defects on the substrate.

The invention provides methods and systems for generating an ultrasound image. In a method, the generation of an ultrasound image comprises: obtaining channel data, the channel data defining a set of imaged points; for each imaged point: isolating the channel data; performing a spatial spectral estimation on the isolated channel data; and selectively attenuating the spatial spectral estimation channel data, thereby generating filtered channel data; and summing the filtered channel data, thereby forming a filtered ultrasound image. In some examples, the method comprises aperture extrapolation. The aperture extrapolation improves the lateral resolution of the ultrasound image. In other examples, the method comprises transmit extrapolation. The transmit extrapolation improves the contrast of the image. In addition, the transmit extrapolation improves the frame rate and reduces the motion artifacts in the ultrasound image. In further examples, the aperture and transmit extrapolations may be combined.