Alpvision S.a.

European Central Bank (Ecb)

European Central Bank

Frederic Jordan

Martin Kutter

Celine Di Venuto Dayer, V

Nicolas Rudaz

Yves Delacretaz

Jean-Claude Gilles

Pierre Durant

Celine Di Venuto

Roland Meylan

The present application concerns the visual identification of materials or documents for tracking or authentication purposes. It describes methods to automatically authenticate an object by comparing some object images with reference images, the object images being characterized by the fact that visual elements used for comparison are non-disturbing for the naked eye. In some described approaches it provides the operator with visible features to locate the area to be imaged. It also proposes ways for real-time implementation enabling user friendly detection using mobile devices like smart phones.

Means for using microstructure of materials surface as a unique identifier

A Biological Neural Network (BNN) core unit comprising a neural cell culture, an input stimulation unit, an output readout unit may be controlled through its various life cycles to provide data processing functionality. An automation system comprising an environmental and chemical controller unit adapted to operate with the BNN stimulation and readout data interfaces facilitates the monitoring and adaptation of the BNN core unit parameters. Pre-processing and post-processing of the BNN interface signals may further facilitate the training and reinforcement learning by the BNN. Multiple BNN core units may also be assembled together as a stack. The proposed system provides a BNN Operating System as a core component for a wetware server to receive, process and transmit data for different client applications without exposing the BNN core unit components to the client user while requiring significantly less energy than conventional silicon-based hardware and software information processing for high-level cognitive computing tasks.

Cognitive computing methods and systems based on biological neural networks

A machine learning system may automatically produce classifier algorithms and configuration parameters by selecting them into a set of predetermined unitary algorithms and associated parametrization values. Multiple digital representations of input object items may be produced by varying the position and orientation of the object to be classified and/or of the sensor to capture a digital representation of the object, and/or by varying a physical environment parameter which changes the digital representation capture of the object by the sensor. A robot arm or a conveyor may vary the object and/or the sensor positions and orientations. The machine learning system may employ generic programming to facilitate the production of classifiers suitable for the classification of multiple digital representations of input object items. The machine learning system may automatically generate reference template signals as configuration parameters for the unitary algorithms to facilitate the production of classifiers suitable for the classification of multiple digital representations of input object items.

Authentication machine learning from multiple digital presentations

The present invention proposes a method for generating a security bi-level image used to form one of the inks of a banknote, said image comprising an original bi-level image and a security pattern, said security pattern being obtained in the spatial domain by the inverse Fourier transform of the combination in the frequency domain between the Fourier transform of an auxiliary image and a two-dimensional sweep, said two-dimensional sweep being a circularly symmetric, two-dimensional pattern created by sweeping a self-similar, one-dimensional function along a 360-degree arc, such as said security pattern being detectable from the maximum value of the cross-correlation of said one-dimensional function with the Fourier transform of one line of said banknote, said method comprising the step of: &#x2014;determining a distance map of the original bi-level image, &#x2014;generating a merged image by linearly interpolating at least a part of said distance map with said security pattern, &#x2014;thresholding the merged image to obtain the security bi-level image, &#x2014;applying the security bi-level image on a support.

Method for generating a security bi-level image for a banknote

Authentication of an article of manufacture using an image of the microstructure of it surface

A method and apparatus for the visual identification of materials for tracking an object comprises parameter setting, acquisition and identification phases. The parameter setting phase comprises the steps of defining acquisition parameters for the objects. The acquisition phase comprises the steps of digitally acquiring two-dimensional template image of an object, applying a flattening function and generating downsampled template version of the flattened template and storing it in a reference database with the flattened template. The identification phase comprises the steps of digitally acquiring a snapshot image, applying the flattening function and generating one downsampled version, cross-correlating the downsampled version of the flattened snapshot with the corresponding downsampled templates of the reference database, and selecting templates according to the value of the signal to noise ratio, for the selected templates, cross-correlating the flattened snapshot image with the reference flattened template, and identifying the object by finding the best corresponding template.

The present application aims to propose a solution for embedding invisible mark suitable for very large production volumes. This is achieved by a method for applying an invisible mark on a media based on a pattern, and comprising the following steps: determining an area to apply this mark, applying a transparent or semi-transparent substance, on the media, said substance being modulated with the active dots to form the pattern by modifying the quantity of the substance at each active dot.

Growing community of inventors

Les Paccots, Switzerland

Frederic Jordan