His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Feature extraction and Machine learning. His Artificial intelligence research is multidisciplinary, relying on both Context and Human–computer interaction. The concepts of his Computer vision study are interwoven with issues in Chromatic scale, Boosting and Hidden Markov model.
His Pattern recognition research integrates issues from Feature model and Histogram. His Histogram study incorporates themes from Matching and Epitome. His Feature extraction research incorporates elements of RGB color model, Feature selection and Benchmark.
His primary areas of investigation include Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Feature extraction. His Artificial intelligence study often links to related topics such as Natural language processing. His work on Pixel, Object detection, Feature and Viewing frustum as part of general Computer vision research is often related to Pedestrian detection, thus linking different fields of science.
His studies deal with areas such as Cognitive neuroscience of visual object recognition and Representation as well as Pattern recognition. His biological study spans a wide range of topics, including Hidden Markov model and Set. Much of his study explores Feature extraction relationship to Histogram.
His scientific interests lie mostly in Artificial intelligence, Computer vision, Pattern recognition, Machine learning and Deep learning. Many of his studies on Artificial intelligence apply to Natural language processing as well. His work in the fields of RGB color model, Viewing frustum and Rendering overlaps with other areas such as Light sensing and Light switch.
The study incorporates disciplines such as Texel, Texture, Representation and Ranking in addition to Pattern recognition. His study on Convolutional neural network and Precision and recall is often connected to Generalization as part of broader study in Machine learning. His Deep learning research is multidisciplinary, incorporating elements of Big Five personality traits, Cognitive psychology, Position paper and Profiling.
Artificial intelligence, Machine learning, Computer vision, Feature extraction and Trajectory are his primary areas of study. His Artificial intelligence study typically links adjacent topics like Big Five personality traits. His work carried out in the field of Big Five personality traits brings together such families of science as Image, Computational aesthetics and Natural language processing.
His research in Computer vision intersects with topics in Statistical classification and Dimensionality reduction. Many of his studies involve connections with topics such as Ranking and Feature extraction. Marco Cristani has researched Visualization in several fields, including Precision and recall, Template matching, Cluster analysis and Image processing.
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.
Person re-identification by symmetry-driven accumulation of local features
M. Farenzena;L. Bazzani;A. Perina;V. Murino.
computer vision and pattern recognition (2010)
Custom Pictorial Structures for Re-identification
Dong Seon Cheng;Marco Cristani;Michele Stoppa;Loris Bazzani.
british machine vision conference (2011)
Symmetry-driven accumulation of local features for human characterization and re-identification
Loris Bazzani;Marco Cristani;Vittorio Murino.
Computer Vision and Image Understanding (2013)
Sparse points matching by combining 3D mesh saliency with statistical descriptors
Umberto Castellani;Marco Cristani;Simone Fantoni;Vittorio Murino.
Computer Graphics Forum (2008)
Infinite Feature Selection
Giorgio Roffo;Simone Melzi;Marco Cristani.
international conference on computer vision (2015)
Multiple-Shot Person Re-identification by HPE Signature
Loris Bazzani;Marco Cristani;Alessandro Perina;Michela Farenzena.
international conference on pattern recognition (2010)
Social interaction discovery by statistical analysis of F-formations.
Marco Cristani;Loris Bazzani;Giulia Paggetti;Andrea Fossati.
british machine vision conference (2011)
Re-identification with RGB-D sensors
Igor Barros Barbosa;Marco Cristani;Alessio Del Bue;Loris Bazzani.
international conference on computer vision (2012)
Multiple-shot person re-identification by chromatic and epitomic analyses
Loris Bazzani;Marco Cristani;Alessandro Perina;Vittorio Murino.
Pattern Recognition Letters (2012)
Background subtraction for automated multisensor surveillance: a comprehensive review
Marco Cristani;Michela Farenzena;Domenico Bloisi;Vittorio Murino.
EURASIP Journal on Advances in Signal Processing (2010)
Profile was last updated on December 6th, 2021.
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