A method and system for temporal frequency analysis for identification of unmanned aircraft systems. The method includes obtaining a sequence of video image frames and providing a pixel from an output frame of the video; generating a fluctuating pixel value vector; examining the fluctuating pixel value vector over a period of time; obtaining the frequency information present in the pixel fluctuations; summing the frequency coefficients for the vectorized pixel values from the fluctuating pixel value vector; obtaining an image representing a two dimensional space based on the summed center frequency coefficients; generating a series of still frames equal to a summation of the center frequency coefficients for pixel variations; and combining the temporal information into spatial locations in a matrix to provide a single image containing the spatial and temporal information present in the sequence of video image frame.

Provided are systems (e.g., a modified fundus camera system) and methods for measuring ocular distortion, the methods comprising projecting an image of a known target pattern having characteristic features onto an area of a retinal plane/surface to provide a distorted retinal image of the target pattern across the area of the retinal surface, recording the distorted retinal image of the target pattern using an image sensor to provide a captured distorted retinal image of the target pattern across the area of the retinal surface, identifying the characteristic features of the captured distorted retinal image, and comparing the identified characteristic features of the captured distorted retinal image of the target pattern across the area of the retinal surface to corresponding characteristic features of the known target pattern to provide a map of ocular distortion across the area of the retinal surface. Also provided are systems and methods for measuring retinal shape.

Various methods and systems are provided for thermal monitoring of tissue with an ultrasound imaging system. In one embodiment, a method comprises acquiring, via an ultrasound probe, an ultrasound image, selecting at least one region of interest in the ultrasound image, extracting features from the at least one region of interest, classifying a thermal state of tissue in the at least one region of interest based on the features, and outputting, via a display device, the thermal state of the tissue. In this way, ultrasound imaging may be used for real-time thermal monitoring of tissue during an ablation procedure, thereby improving the accuracy and efficacy of the ablation procedure.

The present invention provides an image analysis device which is capable of automatically responding to various environmental variations caused by a camera installation condition or an environmental factor without consuming unnecessary calculation resources. The image analysis device is provided with: a plurality of process execution units which are capable of executing different processes on an input image; an analysis unit which analyzes, on the basis of the image, an image variation caused by external environment; and a process selection unit which selects, on the basis of the analyzed variation, at least one from among the plurality of process execution units.

A system for determining the importance of encoded image components for artificial intelligence tasks includes an image capture or storage unit, a processor and a communication interface. The processor can receive components of transformed domain image data from the one or more image capture or storage units across the communication interface. The processor can be configured to determine the relative importance of the components of the transformed domain image data for an artificial intelligence task.

A CMOS image sensor for a code reader in an optical code recognition system incorporates a digital processing circuit that applies a calculation process to the capture image data as said data acquired by the sequential readout circuit of the sensor, in order to calculate a macro-image from the capture image data, which corresponds to location information of code(s) in the capture image, and transmit this macro-image in the image frame following the capture image data, in the footer of the frame.

Systems and methods are described for generating pixel image data, using a lensless camera, based on light that travels through a mask that with pattern masking the lensless camera. The system applies a transformation function to the pixel image data to generate frequency domain image data. The system inputs the frequency domain image data into a machine learning model, wherein the machine learning model does not have access to data that represents the pattern of the mask. The model is trained using a set of images with the feature that are captured by the flat, lensless camera through the mask. The system processes the frequency domain image data using the machine learning model to determine whether the pixel image data depicts the image feature. The system further performs an action based on determining that the pixel image data depicts the image feature.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to calculate the information content of data and information, which in some embodiments are based on new metrics integrating the real space and Fourier space properties of the data or information collected. Among other things, these systems, devices, components and methods provide an assessment of a full information collection chain; the information content of data in a harvesting experiment; global and local resolution; and the information content within objects of interest. Information and data metrics are measured in “bits”. The disclosed systems, devices, components and methods fall within the fields of information processing, information theory, digital signal processing, image processing, image analysis, channel capacity, signal transducers, and analogous fields, and include within their scope computing devices exploiting the new signal processing techniques and algorithms.

A liveness detection method includes: obtaining a reflected audio signal and video data of a object in response to receiving a liveness detection request; performing signal processing and time-frequency analysis on the reflected audio signal to obtain time-frequency information of a processed audio signal, and extracting motion trajectory information of the object from the video data; respectively extract features from the time-frequency information and the motion trajectory information to obtain an audio feature and a motion feature of the object; calculating first global attention information of the object according to the audio feature, and calculating second global attention information of the object according to the motion feature; and fusing the first global attention information with the second global attention information to obtain fused global information, and determining a liveness detection result of the object based on the fused global information.

An image matching device includes a weight determination unit and a matching unit. Based on a synthesized frequency characteristic obtained by synthesizing a frequency characteristic of a first image with a frequency characteristic of a second image and an ideal synthesized frequency characteristic that is an ideal one obtained by synthesizing the frequency characteristic of the first image with the frequency characteristic of the second image, the weight determination unit determines a weight relating to frequency in performing matching of the first image and a third image. The matching unit performs matching of the first image and the third image based on the determined weight.