Other

Cisco Technology, Inc.

Stmicroelectronics S.r.l.

Stmicroelectronics International N.v.

3dflow Srl

Luca Magri

Stefano Piciaccia

Emiliano Mario Piccinelli

Pasqualina Fragneto

Alberto Arnaldo Tanzi

Subarna Tripathi

Beatrice Rossi

Giacomo Boracchi

Prasenjeet Acharjee

Simone Fantoni

Pietro Invernizzi

Roberto Toldo

Enrico Sozio

Yash Singh

Cesare Alippi

Andrea Fusiello

Antonino Maria Rizzo

In some aspects, the techniques described herein relate to a method that includes: obtaining optical channel spectrum data that includes amplified spontaneous emission data and channel data associated with optical signals propagated through an optical fiber; fitting an amplified spontaneous emission trend to the amplified spontaneous emission data; fitting a channel trend to the channel data; jointly optimizing the amplified spontaneous emission trend and the channel trend to determine an optimized channel trend; and determining an anomaly in the channel data based upon the optimized channel trend.

Automatic trend identification and anomaly detection in optical channel monitoring spectrum

A method based on Structure from Motion for processing a plurality of sparse images acquired by one or more acquisition devices to generate a sparse 3D points cloud and of a plurality of internal and external parameters of the acquisition devices includes the steps of collecting the images; extracting keypoints therefrom and generating keypoint descriptors; organizing the images in a proximity graph; pairwise image matching and generating keypoints connecting tracks according maximum proximity between keypoints; performing an autocalibration between image clusters to extract internal and external parameters of the acquisition devices, wherein calibration groups are defined that contain a plurality of image clusters and wherein a clustering algorithm iteratively merges the clusters in a model expressed in a common local reference system starting from clusters belonging to the same calibration group; and performing a Euclidean reconstruction of the object as a sparse 3D point cloud based on the extracted parameters.

Method for 3D modelling based on structure from motion processing of sparse 2D images

An embodiment is a computer-implemented method for detecting a straight line in a digital image comprising a plurality of pixels comprising the steps: detecting an edge in the digital image, generating a first straight line which passes through a first pixel of the detected edge, generating a second straight line which passes through a second pixel of the detected edge, which is different from the first pixel, determining at least two intersections with a boundary of the digital image for each generated straight line, determining a set of two parameter values for each generated straight line based on the respective determined at least two intersections, wherein the set of two parameter values uniquely determines the respective generated straight line, and detecting the straight line in the digital image based on the determined sets of two parameter values.

Method for detecting a straight line in a digital image

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Milan, Italy

Luca Magri