Abstract:
An embodiment relates to a method for converting a digital image from a first color space to a second color space. The first color space is associated with an electronic source device, and the second color space is associated with a projection apparatus of digital images on a screen and coupled to the source device. The method includes: performing a first conversion on a first triad of parameters associated to the first color space by a first conversion matrix to map such a first triad in a third triad of parameters; the third triad is representative of a color space independent from the first and the second color spaces; performing a second conversion on the third triad of parameters by a second conversion matrix to map such a third triad of parameters in a second triad of parameters representative of the second color space. An embodiment of the step of performing the second conversion includes a step of computing the coefficients of the second conversion matrix based on at least one first piece of information representative of a variable distance between the projection apparatus and the screen.
Abstract:
An embodiment relates to a method for the detection of texture of a digital image, including providing a raw data image of the image by means of Bayer image sensors, determining noise in at least a region of the raw data image and determining the texture based on the determined noise without using a high pass or low pass filter.
Abstract:
An embodiment method of processing at least one sensing signal comprising a time-series of signal samples comprises high-pass filtering the time series of signal samples to produce a filtered time series; applying delay embedding processing to the filtered time series; producing a first matrix by storing the set of time-shifted time series as an ordered list of entries in the first matrix; applying a first truncation to produce a second matrix by truncating the entries in the ordered list of entries at one end of the first matrix to remove a number of items equal to the product of the first delay embedding parameter decreased by one times the second delay embedding parameter; applying entry-wise processing to the second matrix, and forwarding a set of estimated kernel densities and/or a set of images generated as a function of the set of estimated kernel densities to a user circuit.
Abstract:
A neural network classifies an input signal. For example, an accelerometer signal may be classified to detect human activity. In a first convolutional layer, two-valued weights are applied to the input signal. In a first two-valued function layer coupled at input to an output of the first convolutional layer, a two-valued function is applied. In a second convolutional layer coupled at input to an output of the first two-valued functional layer, weights of the second convolutional layer are applied. In a fully-connected layer coupled at input to an output of the second convolutional layer, two-valued weights of the fully connected layer are applied. In a second two-valued function layer coupled at input to an output of the fully connected layer, a two-valued function of the second two-valued function layer is applied. A classifier classifies the input signal based on an output signal of second two-valued function layer.
Abstract:
Image defects in digital images are easily detectable by the human eye but may be difficult to detect in a computer-implemented fashion. In an embodiment of a digital-image-acquisition device, defects are removed on the CFA domain before color interpolation takes place. In order to allow cancellation of couplets of defective pixels, a two pass embodiment is presented. Such embodiment presents methods and systems that can remove both couplets and singlets without damaging the image. The system includes a ring corrector that detects a defect in the ring of pixels that surround a central pixel, a singlet corrector that detects and corrects the central pixel and removes a couplet if the ring corrector is activated, whereas if the ring corrector is switched off, the singlet corrector only removes singlets, and a peak-and-valley detector that avoids overcorrection by avoiding correcting signal peaks or valleys in case of spikes or drops in signal.
Abstract:
An embodiment method comprises applying domain transformation processing to a time-series of signal samples, received from a sensor coupled to a dynamical system, to produce a dataset of transformed signal samples therefrom, buffering the transformed signal samples, obtaining a data buffer having transformed signal samples as entries, computing statistical parameters of the data buffer, producing a drift signal indicative of the evolution of the dynamical system as a function of the computed statistical parameters, selecting transformed signal samples buffered in the data buffer as a function of the drift signal, applying normalization processing to the buffered transformed signal samples, applying auto-encoder artificial neural network processing to a dataset of resealed signal samples, and producing a dataset of reconstructed signal samples and calculating an error of reconstruction. The error of reconstruction reaching or failing to reach a threshold value is indicative of the evolution of dynamical system over time.
Abstract:
Color images designed to be displayed, for example, with a projector such as a laser pico projector, are subjected to gamut extension in respective iso-hue paths in the CIE1931xyY color space, operating for example, as follows: a plurality of iso-hue curves in the CIE1931xyY color space is determined; for the points subjected to gamut extension, the closest iso-hue curves are identified; and extension paths to be used for the operation of gamut extension are interpolated from said closest iso-hue curves.
Abstract:
Color images designed to be displayed, for example, with a projector such as a laser pico projector, are subjected to gamut extension in respective iso-hue paths in the CIE1931xyY color space, operating for example, as follows: a plurality of iso-hue curves in the CIE1931xyY color space is determined; for the points subjected to gamut extension, the closest iso-hue curves are identified; and extension paths to be used for the operation of gamut extension are interpolated from said closest iso-hue curves.
Abstract:
An embodiment relates to a method for the detection of texture of a digital image, including providing a raw data image of the image by means of Bayer image sensors, determining noise in at least a region of the raw data image and determining the texture based on the determined noise without using a high pass or low pass filter.