Computer Vision: Models, Learning, and Inference
By Simon J. D. Prince. 2012
539 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/4844150f7aefd3c449c3bd81e94581ff0061037e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Nikolaus Kriegeskorte
- Michalis Vrigkas
- Yevhenii Prokopalo
- Christophoros Nikou
- Jake Goldenfein
- Anthony Larcher
- Ioannis A. Kakadiaris
- Sylvain Meignier
Citations in the corpus
- Phonetically-constrained PLDA modeling for text-dependent speaker verification with multiple short utterances (2013, background): “One advantage of PLDA is to natively takes into account the fact that multiple enrollment i-vectors are generated by the same speaker [7].”
- Deep neural networks: a new framework for modelling biological vision and brain information processing (2015, methodology): “The inversion of generative models has long been explored in both brain science and computer vision (Knill et al. 1996; Yuille & Kersten 2006; Prince 2012).”
- Evaluation of Lifelong Learning Systems (2020, methodology): “Today’s intelligent systems are used in many fields to process data for various tasks amongst which classification and regression (Prince, 2012; Bishop, 2006).”
- The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism (2019, background): “The inability of computer vision systems to respond directly to the totality of registered signal has been acknowledged by some as a failure [66], and others as a fundamental limitation [20].”
- Modeling object appearance using Context-Conditioned Component Analysis (2015, background): “The product of two Gaussians is also Gaussian and the optimal parameter value will be at the mean of this new distribution and is given by More details can be found in section 7.6.2 of [22].”
- A Review of Human Activity Recognition Methods (2015, methodology): “A probabilistic Markov random field (MRF) (Prince, 2012) framework was used to classify and localize the activities in a scene.”
- Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review (2018, unknown): “It associates scale and orientation information for each point resulting from the detection process (Prince, 2012).”