Improved sampling for gradient-domain metropolis light transport

We present a generalized framework for gradient-domain Metropolis rendering, and introduce three techniques to reduce sampling artifacts and variance. The first one is a heuristic weighting strategy that combines several sampling techniques to avoid outliers. The second one is an improved mapping to generate offset paths required for computing gradients. Here we leverage the properties of manifold walks in path space to cancel out singularities. Finally, the third technique introduces generalized screen space gradient kernels. This approach aligns the gradient kernels with image structures such as texture edges and geometric discontinuities to obtain sparser gradients than with the conventional gradient kernel. We implement our framework on top of an existing Metropolis sampler, and we demonstrate significant improvements in visual and numerical quality of our results compared to previous work.

On-road visual vehicle tracking using Markov chain Monte Carlo with metropolis sampling

In this study, a method for vehicle tracking through video analysis based on Markov chain Monte Carlo (MCMC) particle filtering with metropolis sampling is proposed. The method handles multiple targets with low computational requirements and is, therefore, ideally suited for advanced-driver assistance systems that involve real-time operation. The method exploits the removed perspective domain given by inverse perspective mapping (IPM) to define a fast and efficient likelihood model. Additionally, the method encompasses an interaction model using Markov Random Fields (MRF) that allows treatment of dependencies between the motions of targets. The proposed method is tested in highway sequences and compared to state-of-the-art methods for vehicle tracking, i.e., independent target tracking with Kalman filtering (KF) and joint tracking with particle filtering. The results showed fewer tracking failures using the proposed method.

Fully adaptive gaussian mixture metropolis-hastings algorithm

Markov Chain Monte Carlo methods are widely used in signal processing and communications for statistical inference and stochastic optimization. In this work, we introduce an efficient adaptive Metropolis-Hastings algorithm to draw samples from generic multimodal and multidimensional target distributions. The proposal density is a mixture of Gaussian densities with all parameters (weights, mean vectors and covariance matrices) updated using all the previously generated samples applying simple recursive rules. Numerical results for the one and two-dimensional cases are provided.

Independent doubly Adaptive Rejection Metropolis Sampling

Adaptive Rejection Metropolis Sampling (ARMS) is a wellknown MCMC scheme for generating samples from onedimensional target distributions. ARMS is widely used within Gibbs sampling, where automatic and fast samplers are often needed to draw from univariate full-conditional densities. In this work, we propose an alternative adaptive algorithm (IA2RMS) that overcomes the main drawback of ARMS (an uncomplete adaptation of the proposal in some cases), speeding up the convergence of the chain to the target. Numerical results show that IA2RMS outperforms the standard ARMS, providing a correlation among samples close to zero.

Algercum munita metropolis Regni Algeriani : in littore Africano Barbariæ in monte declivi Amphitheatri instar quadrata forma extructa, cum portu Commodissimo Sedes est Bassæ Turcici et ob piraticam per orbem famosissima =: Algier die Befestigte Haupt-Statt des Königr. Algier, Auf die Africanischen Küsten in der Barbarey auf einem abhängigen Berg wie ein Amphiteatrum in gevierten form gebauet samt dem bequemen hasen. Ift die Residenz des Türkischen Bassa u: wegen ihrer Secrauberey Welt berussen

cura et sumptibus Matth. Seutteri.

The Metropolis Local Management Acts : to which is added an appendix containing other statutes relating to the powers and duties of the Metropolitan Board of Works, Vestries ... with table of cases, notes, and index /

Mode of access: Internet.

Shepp's New York City illustrated : scene and story in the metropolis of the Western world : how two million people live and die, work and play, eat and sleep, govern themselves and break the laws, win fortunes and lose them, and so build and maintain the New York of to-day /cJames W. Shepp and Daniel B. Shepp ...

"Sold by subscription only."

Earth science field trip, guide leaflet : Metropolis area : Massac, Pope and Johnson Counties : Paducah, Brownfield, Vienna and La Center Quadrangles.

"April 11, 1959."

Londiniana; or, Reminiscences of the British metropolis: including characteristic sketches, antiquarian, topographical, descriptive, and literary.

Mode of access: Internet.

Travels in town. By the author of "Random recollections of the lords and commons," "The great metropolis," &c. &c...

Mode of access: Internet.