Learn More
In this paper, we show how inexact graph matching (that is, the correspondence between sets of vertices of pairs of graphs) can be solved using the renormalization of projections of the vertices (as defined in this case by their connectivities) into the joint eigenspace of a pair of graphs and a form of relational clustering. An important feature of this(More)
The scale-space S(x, u) of a signal I(x) is defined as the space of the zero-crossings from { v*G(o)* Z(x)}, where G is a Gaussian filter. We present a new method for parsing scale-space, spatial stability analysis, that allows the localization of region boundaries from scale space. Spatial stability analysis is based on the observation that zero-crossings(More)
Probabilistic classification techniques based on Bayesian decision theory are used to analyze human supervised learning and classification. The procedure rests on the assumption that human classification behaviour is based on internal feature states which can be linked to physical feature vectors (corresponding to the system input). In the present approach,(More)
The authors examine the problem of segmenting foreground objects in live video when background scene textures change over time. In particular, we formulate background subtraction as minimizing a penalized instantaneous risk functional-yielding a local online discriminative algorithm that can quickly adapt to temporal changes. We analyze the algorithm's(More)
We present two new algorithms for online learning in reproducing kernel Hilbert spaces. Our first algorithm, ILK (implicit online learning with kernels), employs a new, implicit update technique that can be applied to a wide variety of convex loss functions. We then introduce a bounded memory version, SILK (sparse ILK), that maintains a compact(More)
This paper describes a novel solution to the rigid point pattern matching problem in Euclidean spaces of any dimension. Although we assume rigid motion, jitter is allowed. We present a noniterative, polynomial time algorithm that is guaranteed to find an optimal solution for the noiseless case. First, we model point pattern matching as a weighted graph(More)
adaptive compression methods for color images. One, we use ferential pulse code modulation (DPCM) [25], transform clustering or segmentation procedures to determine self-similar coding [25, 30], and vector quantization [12], developed image regions. Two, for each such region we use a Karhunen– initially for encoding monochrome images. This approach Loeve(More)
In this paper we explore a Bayesian framework for inferring the disparity map from an image pair. Markov Chain Monte Carlo sampling techniques are employed for learning the hyper-parameters which control two robust statistical functions for modelling the specific image pair; and loopy belief propagation is used for approximate inference of the MAP disparity(More)