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Carlo D Mutto

from Sunnyvale, CA
Age ~40

Carlo Mutto Phones & Addresses

  • 482 Texas Oak Ter, Sunnyvale, CA 94086
  • Mountain View, CA
  • Durham, NC

Work

Company: A^3 by airbus Aug 2019 Position: Principal engineer

Education

School / High School: Duke University 2012 to 2012

Skills

Computer Vision • Algorithms • Matlab • Image Processing • C++ • Machine Learning • Opencv • Latex • Programming • Signal Processing • Robotics • Pattern Recognition • Java • Linux • Python • Simulink • Semiconductors • Javascript • C • Statistics • Opengl • Artificial Intelligence • Visual C • Visual Studio • Subversion • Git • Digital Image Processing • Os X

Languages

Italian • English

Industries

Aviation & Aerospace

Resumes

Resumes

Carlo Mutto Photo 1

Principal Engineer

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Location:
2200 Geng Rd, Palo Alto, CA 94303
Industry:
Aviation & Aerospace
Work:
A^3 By Airbus
Principal Engineer

Aquifi, Inc May 2014 - Jul 2019
Computer Vision Architect, R and D Lead

Aquifi, Inc Dec 2017 - Jun 2019
Chief Technology Officer

Imimtek, Inc. Oct 2012 - Apr 2014
Computer Vision Architect

University of Padova Jan 2010 - Mar 2013
Information Engineering Ph.d Student
Education:
Duke University 2012 - 2012
Università Degli Studi Di Padova 2004 - 2012
Doctorates, Doctor of Philosophy
Boston University
Skills:
Computer Vision
Algorithms
Matlab
Image Processing
C++
Machine Learning
Opencv
Latex
Programming
Signal Processing
Robotics
Pattern Recognition
Java
Linux
Python
Simulink
Semiconductors
Javascript
C
Statistics
Opengl
Artificial Intelligence
Visual C
Visual Studio
Subversion
Git
Digital Image Processing
Os X
Languages:
Italian
English

Publications

Us Patents

Systems And Methods For Initializing Motion Tracking Of Human Hands

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US Patent:
8615108, Dec 24, 2013
Filed:
Jul 22, 2013
Appl. No.:
13/948117
Inventors:
Britta Hummel - Berkeley CA, US
Carlo Dal Mutto - Mountain View CA, US
Giuliano Pasqualotto - Mansue, IT
Assignee:
Imimtek, Inc. - Sunnyvale CA
International Classification:
G06K 9/00
G06K 9/48
G06K 9/62
US Classification:
382103, 382199, 382209
Abstract:
Systems and methods for initializing motion tracking of human hands within bounded regions are disclosed. One embodiment includes: a processor; reference and alternate view cameras; and memory containing a plurality of templates that are rotated and scaled versions of a base template. In addition, a hand tracking application configures the processor to: obtain reference and alternate view frames of video data; generate a depth map; identify at least one bounded region within the reference frame of video data containing pixels having distances from the reference camera that are within a specific range of distances; determine whether any of the pixels within the at least one bounded region are part of a human hand; track the motion of the part of the human hand in a sequence of frames of video data obtained from the reference camera; and confirm that the tracked motion corresponds to a predetermined initialization gesture.

Systems And Methods For Keypoint Detection With Convolutional Neural Networks

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US Patent:
20230054821, Feb 23, 2023
Filed:
Jun 3, 2022
Appl. No.:
17/831818
Inventors:
- Salt Lake City UT, US
Carlo Dal Mutto - Sunnyvale CA, US
Kinh Tieu - Sunnyvale CA, US
International Classification:
G06K 9/62
G06N 3/08
G06T 7/00
G06T 7/246
G06N 3/04
G06V 10/44
G06V 10/46
G06V 10/75
Abstract:
A keypoint detection system includes: a camera system including at least one camera; and a processor and memory, the processor and memory being configured to: receive an image captured by the camera system; compute a plurality of keypoints in the image using a convolutional neural network including: a first layer implementing a first convolutional kernel; a second layer implementing a second convolutional kernel; an output layer; and a plurality of connections between the first layer and the second layer and between the second layer and the output layer, each of the connections having a corresponding weight stored in the memory; and output the plurality of keypoints of the image computed by the convolutional neural network.

Systems And Methods For Text And Barcode Reading Under Perspective Distortion

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US Patent:
20200380229, Dec 3, 2020
Filed:
Dec 30, 2019
Appl. No.:
16/730920
Inventors:
- Palo Alto CA, US
Carlo Dal Mutto - Sunnyvale CA, US
Jason Trachewsky - Menlo Park CA, US
International Classification:
G06K 7/14
G06T 7/521
G06T 7/593
Abstract:
A method for automatically recognizing content of labels on objects includes: capturing visual information of an object using a scanning system including one or more cameras, the object having one or more labels on one or more exterior surfaces; detecting, by a computing system, one or more surfaces of the object having labels; rectifying, by the computing system, the visual information of the one or more surfaces of the object to compute one or more rectified images; and decoding, by the computing system, content of a label depicted in at least one of the one or more rectified images.

System And Method For Three-Dimensional Scanning And For Capturing A Bidirectional Reflectance Distribution Function

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US Patent:
20190005711, Jan 3, 2019
Filed:
Aug 20, 2018
Appl. No.:
16/105784
Inventors:
- Palo Alto CA, US
Abbas Rafii - Palo Alto CA, US
Carlo Dal Mutto - Sunnyvale CA, US
Kinh Tieu - Sunnyvale CA, US
Giridhar Murali - Sunnyvale CA, US
Alvise Memo - Marcon (VE), IT
International Classification:
G06T 15/50
G06T 17/20
G06K 9/62
G06T 7/00
G06T 11/00
G06T 15/04
H04N 13/25
H04N 13/282
Abstract:
A method for generating a three-dimensional (3D) model of an object includes: capturing images of the object from a plurality of viewpoints, the images including color images; generating a 3D model of the object from the images, the 3D model including a plurality of planar patches; for each patch of the planar patches: mapping image regions of the images to the patch, each image region including at least one color vector; and computing, for each patch, at least one minimal color vector among the color vectors of the image regions mapped to the patch; generating a diffuse component of a bidirectional reflectance distribution function (BRDF) for each patch of planar patches of the 3D model in accordance with the at least one minimal color vector computed for each patch; and outputting the 3D model with the BRDF for each patch.

Systems And Methods For Inspection And Defect Detection Using 3-D Scanning

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US Patent:
20180322623, Nov 8, 2018
Filed:
May 8, 2018
Appl. No.:
15/974595
Inventors:
- Palo Alto CA, US
David Demirdjian - Boca Raton FL, US
Giulio Marin - Sunnyvale CA, US
Kinh Tieu - Sunnyvale CA, US
Francesco Peruch - Sunnyvale CA, US
Pietro Salvagnini - Sunnyvale CA, US
Giridhar Murali - Sunnyvale CA, US
Carlo Dal Mutto - Sunnyvale CA, US
Guido Cesare - Santa Cruz CA, US
International Classification:
G06T 7/00
G06T 15/20
G06T 7/55
G06T 17/20
G06N 5/04
G06N 3/04
G06N 3/08
Abstract:
A method for detecting defects in objects includes: controlling, by a processor, one or more depth cameras to capture a plurality of depth images of a target object; computing, by the processor, a three-dimensional (3-D) model of the target object using the depth images; rendering, by the processor, one or more views of the 3-D model; computing, by the processor, a descriptor by supplying the one or more views of the 3-D model to a convolutional stage of a convolutional neural network; supplying, by the processor, the descriptor to a defect detector to compute one or more defect classifications of the target object; and outputting the one or more defect classifications of the target object.

Systems And Methods For Keypoint Detection With Convolutional Neural Networks

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US Patent:
20180268256, Sep 20, 2018
Filed:
Mar 16, 2018
Appl. No.:
15/924162
Inventors:
- Palo Alto CA, US
Carlo Dal Mutto - Sunnyvale CA, US
Kinh Tieu - Sunnyvale CA, US
International Classification:
G06K 9/62
G06N 3/08
G06K 9/46
G06T 7/00
Abstract:
A keypoint detection system includes: a camera system including at least one camera; and a processor and memory, the processor and memory being configured to: receive an image captured by the camera system; compute a plurality of keypoints in the image using a convolutional neural network including: a first layer implementing a first convolutional kernel; a second layer implementing a second convolutional kernel; an output layer; and a plurality of connections between the first layer and the second layer and between the second layer and the output layer, each of the connections having a corresponding weight stored in the memory; and output the plurality of keypoints of the image computed by the convolutional neural network.

Systems And Methods For Defect Detection

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US Patent:
20180211373, Jul 26, 2018
Filed:
Jan 9, 2018
Appl. No.:
15/866217
Inventors:
- Palo Alto CA, US
Francesco Peruch - Sunnyvale CA, US
Giuliano Pasqualotto - Mountain View CA, US
Aryan Hazeghi - Palo Alto CA, US
Pietro Salvagnini - Sunnyvale CA, US
Carlo Dal Mutto - Sunnyvale CA, US
Jason Trachewsky - Menlo Park CA, US
Kinh Tieu - Sunnyvale CA, US
International Classification:
G06T 7/00
G06T 17/20
G06K 9/00
Abstract:
A method for detecting a defect in an object includes: capturing, by one or more depth cameras, a plurality of partial point clouds of the object from a plurality of different poses with respect to the object; merging, by a processor, the partial point clouds to generate a merged point cloud; computing, by the processor, a three-dimensional (3D) multi-view model of the object; detecting, by the processor, one or more defects of the object in the 3D multi-view model; and outputting, by the processor, an indication of the one or more defects of the object.

System And Method For Portable Active 3D Scanning

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US Patent:
20180130255, May 10, 2018
Filed:
Nov 6, 2017
Appl. No.:
15/805107
Inventors:
- Palo Alto CA, US
Giuliano Pasqualotto - Mountain View CA, US
Keith Blackstone - Pacifica CA, US
Carlo Dal Mutto - Sunnyvale CA, US
Abbas Rafii - Palo Alto CA, US
Jason Trachewsky - Menlo Park CA, US
Jackson Masters - Redwood City CA, US
International Classification:
G06T 17/20
H04N 5/33
H04N 9/07
H04N 5/225
G03B 17/55
H05K 7/20
Abstract:
A method for generating a three-dimensional model of an object, by a scanning system including a client-side device including: an acquisition system configured to capture images; and an interaction system including a display device and a network interface includes: capturing a plurality of images of the object by the acquisition system, the images being captured from a plurality of different poses of the acquisition system; computing depth maps from the images of the objects, each of the depth maps corresponding to one of the poses of the acquisition system; combining the depth maps to generate a combined point cloud; and displaying, on the display device, the combined point cloud or a 3D mesh model generated from the combined point cloud.
Carlo D Mutto from Sunnyvale, CA, age ~40 Get Report