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: 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.

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: 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: 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 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.

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 relates to a method and an image processor for reducing chroma noise in digital-image data. An embodiment performs noise reduction in the color-filter-array domain prior to demosaicing in order to prevent spreading of noise in subsequent stages of the image-processing pipeline. Peaks in the CFA data are attenuated in order to prevent any undesired color cast. Specifically, any correction to a certain pixel is made in accordance with the amplitude of digital gains applied, as well as with the local luminance and the contribution of the current color channel to the local luminance. In this manner, corrections are restricted to image areas that are subject to high digital amplification, that are comparatively dark, and that are not dominated by the current color channel.

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.

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 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.

Abstract: A method of filtering an image filter is disclosed. The filter is provided for a digital camera including image sensors sensitive to light, a color filter placed over sensitive elements of the sensors and patterned according to a Bayer mosaic pattern layout and an interpolation algorithm joining together the digital information provided by differently colored adjacent pixels in said Bayer pattern. The filter is adaptive and includes a noise level computation block for operating directly on a said Bayer pattern data set of for each color channel thus removing noise while simultaneously preserving picture detail.

Abstract: An embodiment relates to a method and an image processor for reducing chroma noise in digital-image data. An embodiment performs noise reduction in the color-filter-array domain prior to demosaicing in order to prevent spreading of noise in subsequent stages of the image-processing pipeline. Peaks in the CFA data are attenuated in order to prevent any undesired color cast. Specifically, any correction to a certain pixel is made in accordance with the amplitude of digital gains applied, as well as with the local luminance and the contribution of the current color channel to the local luminance. In this manner, corrections are restricted to image areas that are subject to high digital amplification, that are comparatively dark, and that are not dominated by the current color channel.

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: A method for estimating the white Gaussian noise level that corrupts a digital image by discriminating homogeneous blocks from blocks containing a textured area and skipping these last blocks when evaluating the noise standard deviation.

Abstract: A method of filtering an image filter is disclosed. The filter is provided for a digital camera including image sensors sensitive to light, a color filter placed over sensitive elements of the sensors and patterned according to a Bayer mosaic pattern layout and an interpolation algorithm joining together the digital information provided by differently colored adjacent pixels in said Bayer pattern. The filter is adaptive and includes a noise level computation block for operating directly on a said Bayer pattern data set of for each color channel thus removing noise while simultaneously preserving picture detail.

Abstract: A method of filtering an image filter is disclosed. The filter is provided for a digital camera including image sensors sensitive to light, a color filter placed over sensitive elements of the sensors and patterned according to a Bayer mosaic pattern layout and an interpolation algorithm joining together the digital information provided by differently colored adjacent pixels in said Bayer pattern. The filter is adaptive and includes a noise level computation block for operating directly on a said Bayer pattern data set of for each color channel thus removing noise while simultaneously preserving picture detail.