Stmicroelectronics S.r.l.

Sgs-thomson Microelectronics S.r.l.

Stmicroelectronics Gmbh

Stmicroelectronics International N.v.

Rai Radiotelevisione Italiana S.p.a.

Microelectronics S.r.l.

Danilo Pietro Pau

Emiliano Mario Piccinelli

Fabrizio Simone Rovati

Massimiliano Barone

Daniele Alfonso

Antonio Borneo

Emanuele Plebani

Pierluigi Gardella

Arcangelo Ranieri Bruna

Mirko Falchetto

Luca Pezzoni

Daniele Sirtori

Daniele Lavigna

Roberta Bruni

Giorgio Betti

Alessandro Cremonesi

Angelo Dati

Viviana D'Alto

Roberto Valerio Sannino

Raimondo Schettini

Roberto Sannino

Edoardo Gallizio

Simon Goda

Stephen Hill

Maria Luisa Sacchi

Amedeo Zuccaro

Paolo Pasteris

Elio Privitera

Angelo Schiavone

Pier Luigi Gardella

Gary Sweet

Fabrizio Basso

Vittorio Zaccaria

Filippo Santinello

Luca Bortot

Pasqualina Fragneto

Andrea Lorenzo Vitali

Angelo Bosco

Luca Fanucci

Luigi Di Stefano

Daniele Bagni

Andrea Pagni

Fabio Giuseppe De Ambroggi

Sergio Saponara

Luca Celetto

Tiziana Teresa Signorelli

Simone Bianco

Gianluca Filippini

Cristina Silvano

Roberto Zafalon

Anna Valsasna

Emiliano Mario Angelo Piccinelli

Mathieu Robart

Mariagiovanna Sami

Donatella Sciuto

Floriana Guido

Alberto Messina

Ettore Napoli

Andrea Cenciotti

Vilim Bartolucci

Giorgio Lopez

Antonio Maria Borneo

Alioscia Petrelli

Giovanni Condello

Davide Mazzini

Daniele Masala

Xinfeng Bao

Filippo Malaguti

Alberto Margari

Luigi Distefano

Samuele Salti

Federico Tombari

Bruno Poutet

Luca Paliotto

Davide Concion

Fabrizio Lucini

Andrea Capasso

Andrea Graziani

Gary James Sweet

Stephen Adrian Hill

Simon James Goda

A device includes a memory and processing circuitry coupled to the memory. The processing circuitry, in operation, generates an indication of a predicted difference in a direction of arrival (DoA) of a signal using a trained autoregressive model. A predicted indication of a DoA of the signal is generated based on a previous indication of the DoA of the signal and the indication of the predicted difference in the DoA of the signal. The processing circuitry actuates or controls an antenna array based on predicted indications of the DoA of the signal.

Method, device and system of prediction of direction of arrival of a signal

A classification device receives sensor data from a set of sensors and generates, using a context classifier having a set of classifier model parameters, a set of raw predictions based on the received sensor data. Temporal filtering and heuristic filtering are applied to the raw predictions, producing filtered predictions. A prediction error is generated from the filtered predictions, and model parameters of the set of classifier model parameters are updated based on said prediction error. The classification device may be a wearable device.

Learning method, corresponding system, device and computer program product to update classifier model parameters of a classification device

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.

Method, system, and computer program product to employ a multi-layered neural network for classification

An image processing system has one or more memories and image processing circuitry coupled to the one or more memories. The image processing circuitry, in operation, compares a first image to feature data in a comparison image space using a matching model. The comparing includes: unwarping keypoints in keypoint data of the first image; and comparing the unwarped keypoints and descriptor data associated with the first image to the feature data of the comparison image. The image processing circuitry determines whether the first image matches the comparison image based on the comparing.

Keypoint unwarping for machine vision applications

Methods, microprocessors, and systems are provided for implementing an artificial neural network. Data buffers in virtual memory are coupled to respective processing layers in the artificial neural network. An ordered visiting sequence of layers of the artificial neural network is obtained. A virtual memory allocation schedule is produced as a function of the ordered visiting sequence of layers of the artificial neural network, the schedule including a set of instructions for memory allocation and deallocation operations applicable to the data buffers. A physical memory configuration dataset is computed as a function of the virtual memory allocation schedule for the artificial neural network, the dataset including sizes and addresses of physical memory locations for the artificial neural network.

Automatic memory management method, corresponding micro-controller unit and computer program product

Local descriptors are extracted from digital image information and digital depth information related to digital images. The local descriptors convey appearance description information and shape description information related to the digital images. Global representations of the one or more digital images are generated based on the extracted local descriptors, and are hashed. Visual search queries are generated based on the hashed global representations. The visual search queries include fused appearance description information and shape description information conveyed in the local descriptors. The fusing may occur before the global representations are generated, before the hashing or after the hashing.

Method for visual search, corresponding system, apparatus and computer program product

Apparatus and methods to unwarp at least portions of distorted, electronically-captured images are described. Keypoints, instead of an entire image, may be unwarped and used in various machine-vision algorithms, such as object recognition, image matching, and 3D reconstruction algorithms. When using unwarped keypoints, the machine-vision algorithms may perform reliably irrespective of distortions that may be introduced by one or more image capture systems.

Digital image processing circuitry clusters a set of images into a set of first clusters of images and a set of unclustered images. The set of first clusters are merged, generating a set of second clusters of images. Images in the set of unclustered images are assigned to one of a cluster of the set of second clusters of images and an outlier image cluster. The clustered images may be partitioned into subclusters based on detection of objects in the images.

Method of clustering digital images, corresponding system, apparatus and computer program product

First and second video frames in a flow of digital video frames are encoded by extracting respective sets of keypoints and descriptors, each descriptor including a plurality of orientation histograms regarding a patch of pixels centered on the respective keypoint. Once a pair of linked descriptors has been identified, one for each frame, which have a minimum distance from among the distances between any one of the descriptors of the first frame and any one of the descriptors of the second frame, the differences of the histograms of the descriptors linked in the pair are calculated, and the descriptors linked in the pair are encoded as the set including one of the linked descriptors and the histogram differences by subjecting the histogram differences to a thesholding setting at zero all the differences below a certain threshold, to quantization, and to run-length encoding.

Method, apparatus, and computer readable device for encoding and decoding of images using pairs of descriptors and orientation histograms representing their respective points of interest

A laserbeam light source is controlled to avoid light sensitive regions around the laserbeam light source. One or more laserlight-sensitive regions are identified based on images of an area around the laserbeam light source, and indications of positions corresponding to the laserlight-sensitive regions are generated. The laserbeam light source is controlled based on the indications of the positions. The laserbeam light source may be controlled to deflect a laserlight beam away from laserlight-sensitive regions, to reduce an intensity of a laserlight beam directed towards a laserlight-sensitive region, etc. Motion estimation may be used to generate the indications of positions corresponding to the laserlight-sensitive regions.

Laser beam control method, corresponding device, apparatus and computer program product

A method for extraction of descriptors from video content, includes the following steps: a Key Frame Extracting step, applying a local descriptors-based approach to select pictures of the incoming video as key frames that are representative of a temporal region of the video which is visually homogeneous; a Content Analysis step, analysing the content of the key frames and classifying image patches of the key frames as interesting or not for the extraction of descriptors; a Descriptors Extracting step, extracting compact descriptors from the selected key frames, and defining a set of surrounding images also on the basis of input received from the Content Analysis step; a Temporal Coding step, multiplexing information about the time points at which said key frames have been extracted in the Key Frame Extracting step with the compact descriptors extracted in the Descriptors Extracting step, obtaining the descriptors.

Method and an apparatus for the extraction of descriptors from video content, preferably for search and retrieval purpose

In an embodiment, digital video frames in a flow are subjected to a method of extraction of features including the operations of: extracting from the video frames respective sequences of pairs of keypoints/descriptors limiting to a threshold value the number of pairs extracted for each frame; sending the sequences extracted from an extractor module to a server for processing with a bitrate value variable in time; receiving the aforesaid bitrate value variable in time at the extractor as target bitrate for extraction; and limiting the number of pairs extracted by the extractor to a threshold value variable in time as a function of the target bitrate.

Method for extracting features from a flow of digital video frames, and corresponding system and computer program product

Searches performed in a data base using image descriptors of query images are managed via a mobile communication device, such as a smartphone, a tablet, etc., by: extracting at the mobile device grayscale and color descriptors of query images, sending the grayscale descriptors as compressed grayscale descriptors of query images from the mobile device to a server for searching in the data base, receiving at the mobile device results of the search, using color descriptors of query images in disambiguating the results by: i) sending the color descriptors as compressed color descriptors of query images from the mobile device to the server and receiving at the mobile device disambiguated search results from the server, or ii) receiving at the mobile device non-disambiguated search results from the server and disambiguating the search results by means of the color descriptors extracted at the mobile device to produce disambiguated search results.

Method for object recognition, corresponding system, apparatus and computer program product

An image processing system includes a first processor that acquires frames of image data. For each frame of data, the first processor generates a Gaussian pyramid for the frame of data, extract histogram of oriented gradient (HOG) descriptors for each level of the Gaussian pyramid, compresses the HOG descriptors, and sends the compressed HOG descriptors. A second processor is coupled to the first processor and is configured to receive the compressed HOG descriptors, aggregate the compressed HOG descriptors into windows, compare data of each window to at least one stored model, and generate output based upon the comparison.

Image processing system for extraction of contextual information and associated methods

Image-processing apparatus and methods to adaptively vary an interest point threshold value and control a number of interest points identified in an image frame are described. Sub-regions of an image frame may be processed in a sequence, and an interest point threshold value calculated for each sub-region. The calculated value of the interest point threshold may depend upon pre-selected values and values determined from the processing of one or more prior sub-regions. By using adaptive thresholding, a number of interest points detected for each frame in a sequence of image frames may remain substantially constant, even though objects within the frames may vary appreciably.

Adaptive interest rate control for visual search

Growing community of inventors

Sesto San Giovanni, Italy

Danilo Pietro Pau