H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 98 Citations 109,097 314 World Ranking 155 National Ranking 93

Research.com Recognitions

Awards & Achievements

2021 - IEEE Fellow For contributions to visual recognition algorithms and datasets

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer vision
  • Machine learning

His main research concerns Artificial intelligence, Computer vision, Pattern recognition, Cognitive neuroscience of visual object recognition and Machine learning. Object, Pattern recognition, Unsupervised learning, Contextual image classification and Feature are among the areas of Artificial intelligence where Pietro Perona concentrates his study. His studies in Computer vision integrate themes in fields like Algorithm, Detector and Cluster analysis.

His Pattern recognition research includes themes of Image processing, Face, Machine vision and Rotation. His studies deal with areas such as Motion, Segmentation, Minimum bounding box, Probabilistic logic and Pascal as well as Cognitive neuroscience of visual object recognition. His work deals with themes such as Caltech 101, Natural language processing, Object detection, Categorization and Crowdsourcing, which intersect with Machine learning.

His most cited work include:

  • Microsoft COCO: Common Objects in Context (10541 citations)
  • Scale-space and edge detection using anisotropic diffusion (10015 citations)
  • Microsoft COCO: Common Objects in Context (3312 citations)

What are the main themes of his work throughout his whole career to date?

Pietro Perona mostly deals with Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Object. Artificial intelligence is represented through his Cognitive neuroscience of visual object recognition, Motion estimation, Image, Unsupervised learning and Pattern recognition research. His Cognitive neuroscience of visual object recognition study focuses on 3D single-object recognition in particular.

Many of his studies involve connections with topics such as Computer graphics and Computer vision. The study incorporates disciplines such as Contextual image classification, Object detection and Probabilistic logic in addition to Pattern recognition. His work carried out in the field of Machine learning brings together such families of science as Classifier, Crowdsourcing, Training set and Categorization.

He most often published in these fields:

  • Artificial intelligence (70.41%)
  • Computer vision (38.07%)
  • Pattern recognition (19.95%)

What were the highlights of his more recent work (between 2014-2021)?

  • Artificial intelligence (70.41%)
  • Machine learning (18.58%)
  • Computer vision (38.07%)

In recent papers he was focusing on the following fields of study:

Pietro Perona spends much of his time researching Artificial intelligence, Machine learning, Computer vision, Pattern recognition and Class. His research in Pose, Benchmark, Contextual image classification, Visualization and Image are components of Artificial intelligence. His Benchmark research incorporates themes from Minimum bounding box and State.

He combines subjects such as Object and Artificial neural network with his study of Contextual image classification. His work on Boosting as part of general Machine learning study is frequently linked to Training, therefore connecting diverse disciplines of science. His Computer vision research incorporates elements of Sensory system and Looming.

Between 2014 and 2021, his most popular works were:

  • The iNaturalist Species Classification and Detection Dataset (230 citations)
  • Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection (175 citations)
  • Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning (156 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Statistics

His primary areas of investigation include Artificial intelligence, Machine learning, Training set, Pose and Class. Pietro Perona interconnects Generalization, Computer vision and Natural language processing in the investigation of issues within Artificial intelligence. His study in the field of Image warping is also linked to topics like Zoom.

He has included themes like Embedding, Robot and Monocular in his Machine learning study. His Artificial neural network research is multidisciplinary, incorporating elements of Entropy and Object detection. His work is dedicated to discovering how Object, Face are connected with Pattern recognition and Contextual image classification and other disciplines.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Scale-space and edge detection using anisotropic diffusion

P. Perona;J. Malik.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1990)

15332 Citations

Microsoft COCO: Common Objects in Context

Tsung-Yi Lin;Michael Maire;Serge J. Belongie;James Hays.
european conference on computer vision (2014)

11403 Citations

Microsoft COCO: Common Objects in Context

Tsung-Yi Lin;Michael Maire;Serge Belongie;Lubomir Bourdev.
arXiv: Computer Vision and Pattern Recognition (2014)

6948 Citations

A Bayesian hierarchical model for learning natural scene categories

L. Fei-Fei;P. Perona.
computer vision and pattern recognition (2005)

4455 Citations

Graph-Based Visual Saliency

Jonathan Harel;Christof Koch;Pietro Perona.
neural information processing systems (2006)

3844 Citations

Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Li Fei-Fei;Rob Fergus;Pietro Perona.
computer vision and pattern recognition (2004)

3809 Citations

Object class recognition by unsupervised scale-invariant learning

R. Fergus;P. Perona;A. Zisserman.
computer vision and pattern recognition (2003)

2872 Citations

Pedestrian Detection: An Evaluation of the State of the Art

P. Dollar;C. Wojek;B. Schiele;P. Perona.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)

2839 Citations

Self-Tuning Spectral Clustering

Lihi Zelnik-manor;Pietro Perona.
neural information processing systems (2004)

2211 Citations

Caltech-256 Object Category Dataset

Gregory Griffin;Alex Holub;Pietro Perona.
(2007)

2133 Citations

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Best Scientists Citing Pietro Perona

Qi Tian

Qi Tian

Huawei Technologies (China)

Publications: 155

Bernt Schiele

Bernt Schiele

Max Planck Institute for Informatics

Publications: 153

Shuicheng Yan

Shuicheng Yan

National University of Singapore

Publications: 133

Chunhua Shen

Chunhua Shen

University of Adelaide

Publications: 129

Trevor Darrell

Trevor Darrell

University of California, Berkeley

Publications: 129

Jitendra Malik

Jitendra Malik

University of California, Berkeley

Publications: 126

Alan L. Yuille

Alan L. Yuille

Johns Hopkins University

Publications: 126

Dacheng Tao

Dacheng Tao

University of Sydney

Publications: 118

Li Fei-Fei

Li Fei-Fei

Stanford University

Publications: 116

Kristen Grauman

Kristen Grauman

Facebook (United States)

Publications: 115

Xuelong Li

Xuelong Li

Northwestern Polytechnical University

Publications: 109

Joachim Weickert

Joachim Weickert

Saarland University

Publications: 106

Ling Shao

Ling Shao

Inception Institute of Artificial Intelligence

Publications: 103

Larry S. Davis

Larry S. Davis

University of Maryland, College Park

Publications: 101

Rama Chellappa

Rama Chellappa

Johns Hopkins University

Publications: 99

Antonio Torralba

Antonio Torralba

MIT

Publications: 93

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