Bayesian object identification

Abstract

This paper addresses the task of locating and identifying an unknown number of objects of different types in an image. Baddeley & Van Lieshout (1993) advocate marked point processes as object priors, whereas Grenander & Miller (1994) use deformable template models. In this paper elements of both approaches are combined to handle scenes containing variable numbers of objects of different types, using reversible jump Markov chain Monte Carlo methods for inference (Green, 1995). The naive application of these methods here leads to slow mixing and we adapt the model and algorithm in tandem in proposing three strategies to deal with this. The first two expand the model space by introducing an additional 'unknown' object type and the idea of a variable resolution template. The third strategy, utilising the first two, augments the algorithm with classes of updates which provide intuitive transitions between realisations containing different numbers of cells by splitting or merging nearby objects.

Crowd Detection with a Multiview Sampler

“…Among monocular approaches for pedestrian detection [4][5][6][7][8][9], classifier-based methods are very popular [7][8][9] and sampling-based methods have also been shown effective for crowd detection [2,3,10] as well as generic object detection [11,12]. Within the sampling framework, various efficient, data-driven sampling strategies have been proposed.…”

Improving the acceptance rate of reversible jump MCMC proposals

“…Following the marked pointed process approach of Baddeley and van Lieshout (1993), we take the prior model to be a hard core interaction point process which prevents cells overlapping. The data Y are assumed to be Gaussian with different means and variances assigned to observations from different cells and from the background region; for more details see, for example, Rue and Hurn (1999) or Al-Awadhi (2001).…”

“…Incorporating a fit to the data into the proposal itself is hard because of the computational cost of evaluating the posterior over a range of values for the proposal and of evaluating the reverse step to compute the acceptance probability. Rue and Hurn (1999) demonstrate one approach for such "data-matching" suitable for work with deformable polygonal templates, but the restriction to ellipses prevents us from using their approach here.…”

Bayesian multiscale analysis of images modeled as Gaussian Markov random fields

“…The use of Markov Random Fields in imaging has a long tradition [2], and has been used in a variety of applications, including segmentation [3], low level vision [4], object identification [5], as well as pattern recognition in dermatoscopic images [6]. The underlying graph is a regular lattice corresponding to the pixels of the image, which has a Markov property, i.e., the value of any pixel is a random variable conditionally independent of all except the neighboring pixels defined in a neighborhood structure.…”