Test
Computer Vision
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Preguntas del test (9)
1Computer vision is most easily done as an entirely bottom up (starting with raw signal and inferring from there) task.
- AVerdadero
- BFalso
2Bayesian inference provides a means for integrating prior information with empirical information gathered from image data.
- AVerdadero
- BFalso
3Which is a big problem that must be tackled in computer vision?
- ADecision making under uncertainty
- BDecision making under certainty
4How does bayesian inference intepret probability?
- ADegree of belief
- BProbability of occuring
5If H is hypothesis and D is data what form would a human experts knowledge be in?
- AP(H|D)
- BP(D|H)
6In bayesian inference, to update our decision we use;
- ALatest posterior as new prior
- BLatest prior as new posterior
7What is one difficulty with computer vision tasks when using prior probabilities in bayesian inference
- AThere are often no useful/strong priors
- BThere are too many priors
8For reliable recognition we want:
- AWithin-class variability to be larger than between-class variability
- BBetween-class variability to be larger than within-class variability
9The posterior probability (likelihood of being a class given the data) is:
- AP(x|Ck)
- BP(Ck|x)
Información
- Descripción
- Test sobre Computer Vision, creado por hp343 el 26/04/2015.
- Autor
- hp343
- Idioma
- English
- Visitas
- 3
- Valoración
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