2012 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to human behaviour understanding and multimedia
Nicu Sebe spends much of his time researching Artificial intelligence, Pattern recognition, Machine learning, Computer vision and Feature extraction. All of his Artificial intelligence and Feature, Facial expression, Support vector machine, Facial recognition system and Image retrieval investigations are sub-components of the entire Artificial intelligence study. The Classifier, Wavelet transform and Naive Bayes classifier research Nicu Sebe does as part of his general Pattern recognition study is frequently linked to other disciplines of science, such as Multi-task learning, therefore creating a link between diverse domains of science.
Nicu Sebe combines subjects such as Training set and Data mining with his study of Machine learning. His studies deal with areas such as Image processing, Discriminative model and Feature selection as well as Feature extraction. His research in Feature selection tackles topics such as Dimensionality reduction which are related to areas like Multimedia.
His primary areas of study are Artificial intelligence, Pattern recognition, Computer vision, Machine learning and Multimedia. Image, Feature extraction, Deep learning, Feature and Image retrieval are the core of his Artificial intelligence study. His Pattern recognition study incorporates themes from Object detection and Representation.
His research in Computer vision focuses on subjects like Facial expression, which are connected to Face and Facial recognition system. His research on Machine learning often connects related topics like Contextual image classification. Nicu Sebe has included themes like Field and Session, World Wide Web in his Multimedia study.
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Image, Deep learning and Machine learning. Nicu Sebe interconnects Generator and Computer vision in the investigation of issues within Artificial intelligence. His work in Pattern recognition addresses subjects such as Feature, which are connected to disciplines such as Feature learning.
His Image study also includes fields such as
Nicu Sebe focuses on Artificial intelligence, Pattern recognition, Image, Deep learning and Source code. The concepts of his Artificial intelligence study are interwoven with issues in Generator, Machine learning and Computer vision. His study in the fields of Ground truth and Feature under the domain of Machine learning overlaps with other disciplines such as Task analysis and Meta learning.
His work carried out in the field of Pattern recognition brings together such families of science as Frame and Representation. Nicu Sebe has researched Image in several fields, including Generative grammar, Face and Task. His research integrates issues of Classifier, Object detection, Benchmark and Monocular in his study of Deep learning.
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.
Content-based multimedia information retrieval: State of the art and challenges
Michael S. Lew;Nicu Sebe;Chabane Djeraba;Ramesh Jain.
ACM Transactions on Multimedia Computing, Communications, and Applications (2006)
Facial expression recognition from video sequences: temporal and static modeling
Ira Cohen;Nicu Sebe;Ashutosh Garg;Lawrence S. Chen.
Computer Vision and Image Understanding (2003)
Multimodal human-computer interaction: A survey
Alejandro Jaimes;Nicu Sebe.
Computer Vision and Image Understanding (2007)
Authentic facial expression analysis
N. Sebe;M. S. Lew;Y. Sun;I. Cohen.
Image and Vision Computing (2007)
A Survey on Learning to Hash
Jingdong Wang;Ting Zhang;Jingkuan Song;Nicu Sebe.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2018)
Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction
I. Cohen;F.G. Cozman;N. Sebe;M.C. Cirelo.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)
Combining Head Pose and Eye Location Information for Gaze Estimation
R. Valenti;N. Sebe;T. Gevers.
IEEE Transactions on Image Processing (2012)
Structural Resemblance to Emotional Expressions Predicts Evaluation of Emotionally Neutral Faces
Christopher P Said;Nicu Sebe;Alexander Todorov.
An eye fixation database for saliency detection in images
Subramanian Ramanathan;Harish Katti;Nicu Sebe;Mohan Kankanhalli.
european conference on computer vision (2010)
Affective multimodal human-computer interaction
Maja Pantic;Nicu Sebe;Jeffrey F. Cohn;Thomas Huang.
acm multimedia (2005)
Profile was last updated on December 6th, 2021.
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