2021 - IEEE Fellow For contributions to visual recognition algorithms and datasets
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.
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.
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.
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.
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Scale-space and edge detection using anisotropic diffusion
P. Perona;J. Malik.
IEEE Transactions on Pattern Analysis and Machine Intelligence (1990)
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin;Michael Maire;Serge J. Belongie;James Hays.
european conference on computer vision (2014)
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin;Michael Maire;Serge Belongie;Lubomir Bourdev.
arXiv: Computer Vision and Pattern Recognition (2014)
A Bayesian hierarchical model for learning natural scene categories
L. Fei-Fei;P. Perona.
computer vision and pattern recognition (2005)
Graph-Based Visual Saliency
Jonathan Harel;Christof Koch;Pietro Perona.
neural information processing systems (2006)
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)
Object class recognition by unsupervised scale-invariant learning
R. Fergus;P. Perona;A. Zisserman.
computer vision and pattern recognition (2003)
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)
Self-Tuning Spectral Clustering
Lihi Zelnik-manor;Pietro Perona.
neural information processing systems (2004)
Caltech-256 Object Category Dataset
Gregory Griffin;Alex Holub;Pietro Perona.
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