Abstract: Computerized methods and systems, including computer programs encoded on a computer storage medium, may detect event shown within digital video content captured by one or more video cameras, and correlate these detected events to real-world conditions that may not be captured within the digital video data. For example, a computing system may detect events shown within digital video content captured by one or more video cameras, and may obtain data that identifies at least one external event. The computer system may establish a predictive model that correlates values of event parameters that characterize the detected and external events during a first time period, and may apply the predictive model to an event parameter that characterizes an additional event detected during a second time period. Based on an outcome of the predictive model, the computing system may determine an expected value of the external event parameter during the second time period.

Abstract: The invention relates to methods for evaluation a level of brightness in the area of interest of the digital x-ray image for medical applications by means of the image histogram using a neural network. The calculations comprise of: image acquisition, image histogram calculation, converting histogram values into input arguments of the neural network and output values of the neural network acquiring. As input arguments of the neural network the histogram values calculated with the given bin width and normalized to unity are used. The level of brightness is calculated as a linear function of the output value of the neural network. Neural network learning is performed using a learning set calculated on the base of the given image database; as a set of target values the levels of brightness calculated for each image over the area of interest and scaled to the range of the activation function of a neuron in the output layer of the neural network are used.