Search

Alessandro Bissacco

from Los Angeles, CA
Age ~50
Get Report
Related to:
Lorenza Della Donna, 45A Bissacco
Connected to:
Anthony M Bissaco, 37Michael Bissada, 28Constant B Bissahoyo, 49Carl Bissantz, 71Debra M Bissantz, 67Mitchell Z Bisschop, 46Stephanie S Bisselberg, 54Annette W Bissell, 62Audrey E Bissell, 94Billy R Bissell, 76

Alessandro Bissacco Phones & Addresses

    s
  • 1253 Granville Ave #101, Los Angeles, CA 90025
  • 1619 Armacost Ave, Los Angeles, CA 90025 (310) 820-4542
  • West Hollywood, CA
  • 1619 Armacost Ave APT 3, Los Angeles, CA 90025 (310) 820-4542

Education

Degree: High school graduate or higher

Skills

Software Engineering • C • C++ • Java • Algorithms • Computer Science • Java Enterprise Edition • Machine Learning • Software Development

Industries

Research

Resumes

Resumes

Alessandro Bissacco Photo 1

Alessandro Bissacco

View page
Location:
Los Angeles, CA
Industry:
Research
Skills:
Software Engineering
C
C++
Java
Algorithms
Computer Science
Java Enterprise Edition
Machine Learning
Software Development

Publications

Us Patents

Fast Human Pose Estimation Using Appearance And Motion Via Multi-Dimensional Boosting Regression

View page
US Patent:
7778446, Aug 17, 2010
Filed:
Dec 5, 2007
Appl. No.:
11/950662
Inventors:
Ming-Hsuan Yang - Mountain View CA, US
Alessandro Bissacco - Los Angeles CA, US
Assignee:
Honda Motor Co., Ltd - Tokyo
International Classification:
G06K 9/00
US Classification:
382103, 382115
Abstract:
Methods and systems are described for three-dimensional pose estimation. A training module determines a mapping function between a training image sequence and pose representations of a subject in the training image sequence. The training image sequence is represented by a set of appearance and motion patches. A set of filters are applied to the appearance and motion patches to extract features of the training images. Based on the extracted features, the training module learns a multidimensional mapping function that maps the motion and appearance patches to the pose representations of the subject. A testing module outputs a fast human pose estimation by applying the learned mapping function to a test image sequence.

Systems And Methods For Selecting Interest Point Descriptors For Object Recognition

View page
US Patent:
8086616, Dec 27, 2011
Filed:
Mar 16, 2009
Appl. No.:
12/404857
Inventors:
Alessandro Bissacco - Los Angeles CA, US
Ulrich Buddemeier - Sebastopol CA, US
Hartmut Neven - Malibu CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06F 7/00
US Classification:
707758
Abstract:
Systems and methods for selecting interest point descriptors for object recognition. In an embodiment, the present invention estimates performance of local descriptors by (1) receiving a local descriptor relating to an object in a first image; (2) identifying one or more nearest neighbor descriptors relating to one or more images different from the first image, the nearest neighbor descriptors comprising nearest neighbors of the local descriptor; (3) calculating a quality score for the local descriptor based on the number of nearest neighbor descriptors that relate to images showing the object; and (4) determining, on the basis of the quality score, if the local descriptor is effective in identifying the object.

Object Detection With False Positive Filtering

View page
US Patent:
8345921, Jan 1, 2013
Filed:
May 11, 2009
Appl. No.:
12/453432
Inventors:
Andrea Frome - Berkeley CA, US
German Cheung - San Francisco CA, US
Ahmad Abdulkader - San Jose CA, US
Marco Zennaro - San Francisco CA, US
Bo Wu - Mountain View CA, US
Alessandro Bissacco - Los Angeles CA, US
Hartmut Neven - Malibu CA, US
Luc Vincent - Palo Alto CA, US
Hartwig Adam - Los Angeles CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06K 9/00
US Classification:
382103, 382105, 382118, 382181
Abstract:
Embodiments of this invention relate to detecting and blurring images. In an embodiment, a system detects objects in a photographic image. The system includes an object detector module configured to detect regions of the photographic image that include objects of a particular type at least based on the content of the photographic image. The system further includes a false positive detector module configured to determine whether each region detected by the object detector module includes an object of the particular type at least based on information about the context in which the photographic image was taken.

Self-Similar Descriptor Filtering

View page
US Patent:
8520949, Aug 27, 2013
Filed:
Jun 22, 2009
Appl. No.:
12/489345
Inventors:
Alessandro Bissacco - Los Angeles CA, US
Ulrich Buddemeier - Sebastopol CA, US
Hartmut Neven - Malibu CA, US
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06K 9/00
US Classification:
382190, 382305
Abstract:
According to an embodiment, a method for filtering feature point matches for visual object recognition is provided. The method includes identifying local descriptors in an image and determining a self-similarity score for each local descriptor based upon matching each local descriptor to its nearest neighbor descriptors from a descriptor dataset. The method also includes filtering feature point matches having a number of local descriptors with self-similarity scores that exceed a threshold. According to another embodiment, the filtering step may further include removing feature point matches. According to a further embodiment, a system for filtering feature point matches for visual object recognition is provided. The system includes a descriptor identifier, a self-similar descriptor analyzer and a self-similar descriptor filter.

Detecting Humans Via Their Pose

View page
US Patent:
20070098254, May 3, 2007
Filed:
Oct 26, 2006
Appl. No.:
11/553388
Inventors:
Ming-Hsuan Yang - Sunnyvale CA, US
Alessandro Bissacco - Los Angeles CA, US
International Classification:
G06K 9/62
G06K 9/00
US Classification:
382159000, 382103000, 382228000
Abstract:
A method and system efficiently and accurately detects humans in a test image and classifies their pose. In a training stage, a probabilistic model is derived in an unsupervised or semi-supervised manner such that at least some poses are not manually labeled. The model provides two sets of model parameters to describe the statistics of images containing humans and images of background scenes. In a testing stage, the probabilistic model is used to determine if a human is present in the image, and classify the human's pose based on the poses in the training images. A solution is efficiently provided to both human detection and pose classification by using the same probabilistic model to solve the problems.

System And Method Of Determining Building Numbers

View page
US Patent:
20120008865, Jan 12, 2012
Filed:
Jul 12, 2011
Appl. No.:
13/181081
Inventors:
Bo Wu - Mountain View CA, US
Alessandro Bissacco - Los Angeles CA, US
Raymond W. Smith - Los Altos CA, US
Kong man Cheung - San Francisco CA, US
Andrea Frome - Berkeley CA, US
Shlomo Urbach - Rehovot, IL
Assignee:
Google Inc. - Mountain View CA
International Classification:
G06K 9/18
G06K 9/00
US Classification:
382182, 382190
Abstract:
A system and method is provided for automatically recognizing building numbers in street level images. In one aspect, a processor selects a street level image that is likely to be near an address of interest. The processor identifies those portions of the image that are visually similar to street numbers, and then extracts the numeric values of the characters displayed in such portions. If an extracted value corresponds with the building number of the address of interest such as being substantially equal to the address of interest, the extracted value and the image portion are displayed to a human operator. The human operator confirms, by looking at the image portion, whether the image portion appears to be a building number that matches the extracted value. If so, the processor stores a value that associates that building number with the street level image.

Extracting Image Data Using Three-Dimensional Models

View page
US Patent:
20180039857, Feb 8, 2018
Filed:
Jul 20, 2017
Appl. No.:
15/655849
Inventors:
- Mountain View CA, US
Henry Allan Rowley - Sunnyvale CA, US
Xiaohang Wang - Millburn NJ, US
Yakov Okshtein - Far Rockaway NY, US
Farhan Shamsi - Rego Park NY, US
Alessandro Bissacco - Los Angeles CA, US
International Classification:
G06K 9/62
Abstract:
Comparing extracted card data from a continuous scan comprises an optical character recognition (“OCR”) system for extracted data based on three-dimensional models. The system receives a digital scan of a physical card and obtains a plurality of images of the card from the digital scan of the physical card. The system performs an OCR algorithm on a three-dimensional model based on the images and determines if a confidence level of the results are above a preconfigured level. If the results are below the preconfigured levels, a second three dimensional model is created that includes additional received images. When results are over the preconfigured level, the results are accepted as an accurate extraction.

Client Side Filtering Of Card Ocr Images

View page
US Patent:
20170185833, Jun 29, 2017
Filed:
Mar 13, 2017
Appl. No.:
15/457946
Inventors:
- Mountain View CA, US
Alessandro Bissacco - Los Angeles CA, US
Glenn Merlind Berntson - Jersey City NJ, US
Marria Nazif - San Francisco CA, US
Justin Scheiner - Oceanside NY, US
Sam Shih - Long Island City NY, US
Mark Leslie Snyder - Oakland CA, US
Daniel Talavera - San Francisco CA, US
International Classification:
G06K 9/00
G06K 9/03
G06K 9/46
G06K 9/18
Abstract:
The technology of the present disclosure includes computer-implemented methods, computer program products, and systems to filter images before transmitting to a system for optical character recognition (“OCR”). A user computing device obtains a first image of the card from the digital scan of a physical card and analyzes features of the first image, the analysis being sufficient to determine if the first image is likely to be usable by an OCR algorithm. If the user computing device determines that the first image is likely to be usable, then the first image is transmitted to an OCR system associated with the OCR algorithm. Upon a determination that the first image is unlikely to be usable, a second image of the card from the digital scan of the physical card is analyzed. The optical character recognition system performs an optical character recognition algorithm on the filtered card.
Control profile
Alessandro Bissacco from Los Angeles, CA, age ~50 Get Report