2017 - IEEE Fellow For contributions to computer vision, image processing, and multimedia
2005 - Hellman Fellow
His main research concerns Artificial intelligence, Pattern recognition, Computer vision, Object detection and Machine learning. While working in this field, Nuno Vasconcelos studies both Artificial intelligence and Detector. His work on Discriminant and Classifier is typically connected to Fisher kernel and Gaussian process as part of general Pattern recognition study, connecting several disciplines of science.
His work carried out in the field of Computer vision brings together such families of science as Salient and Salience. His research in Object detection intersects with topics in Artificial neural network, Cognitive neuroscience of visual object recognition and Feature. His work on LogitBoost and Feature as part of general Machine learning study is frequently linked to Sample and Facial expression recognition, therefore connecting diverse disciplines of science.
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Image retrieval. His studies in Object detection, Feature extraction, Cognitive neuroscience of visual object recognition, Contextual image classification and Image segmentation are all subfields of Artificial intelligence research. Nuno Vasconcelos has researched Cognitive neuroscience of visual object recognition in several fields, including Artificial neural network and Inference.
His Pattern recognition study integrates concerns from other disciplines, such as Kadir–Brady saliency detector, Image and Robustness. His Machine learning study combines topics from a wide range of disciplines, such as Classifier and Bayes' theorem. The study incorporates disciplines such as Divergence-from-randomness model, Information retrieval and Data mining in addition to Image retrieval.
Nuno Vasconcelos mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Deep learning and Classifier. In his study, Identification is strongly linked to Computer vision, which falls under the umbrella field of Artificial intelligence. His Machine learning research is multidisciplinary, incorporating elements of Entropy, Entropy and Embedding.
His Pattern recognition study combines topics in areas such as Fisher vector, Feature and Statistical model. His Deep learning research includes themes of Feature learning, Transfer of learning, Voxel, Convolutional neural network and Data set. The various areas that he examines in his Classifier study include Discriminant, Optimization problem and Stochastic gradient descent.
Artificial intelligence, Deep learning, Detector, Object detection and Pattern recognition are his primary areas of study. His Artificial intelligence study combines topics in areas such as Machine learning and Adaptation. His study in Deep learning is interdisciplinary in nature, drawing from both Transfer of learning, Algorithm, Feature learning and Convolutional neural network.
His study on Object detection is mostly dedicated to connecting different topics, such as Overfitting. His research integrates issues of Stochastic gradient descent, Optimization problem and Resampling in his study of Pattern recognition. His Computer vision research is multidisciplinary, incorporating elements of Training set, Benchmark and Code.
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.
Supervised Learning of Semantic Classes for Image Annotation and Retrieval
G. Carneiro;A.B. Chan;P.J. Moreno;N. Vasconcelos.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
A new approach to cross-modal multimedia retrieval
Nikhil Rasiwasia;Jose Costa Pereira;Emanuele Coviello;Gabriel Doyle.
acm multimedia (2010)
Anomaly detection in crowded scenes
Vijay Mahadevan;Weixin Li;Viral Bhalodia;Nuno Vasconcelos.
computer vision and pattern recognition (2010)
Privacy preserving crowd monitoring: Counting people without people models or tracking
A.B. Chan;Z.-S.J. Liang;N. Vasconcelos.
computer vision and pattern recognition (2008)
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection
Zhaowei Cai;Quanfu Fan;Rogério Schmidt Feris;Nuno Vasconcelos.
european conference on computer vision (2016)
Cascade R-CNN: Delving Into High Quality Object Detection
Zhaowei Cai;Nuno Vasconcelos.
computer vision and pattern recognition (2018)
Anomaly Detection and Localization in Crowded Scenes
Weixin Li;Vijay Mahadevan;Nuno Vasconcelos.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2014)
A Kullback-Leibler Divergence Based Kernel for SVM Classification in Multimedia Applications
Pedro J. Moreno;Purdy P. Ho;Nuno Vasconcelos.
neural information processing systems (2003)
Spatiotemporal Saliency in Dynamic Scenes
V. Mahadevan;N. Vasconcelos.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
A.B. Chan;N. Vasconcelos.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2008)
Profile was last updated on December 6th, 2021.
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