Maximum Mean Discrepancy (MMD) is a distance on the space of probability measures which has found numerous applications in machine learning and nonparametric testing. This distance is based on the notion of embedding probabilities in a reproducing kernel Hilbert space. In this paper, we present the first known lower bounds for the estimation of MMD based on finite samples. Our lower bounds hold...

In this work I evaluate the performance of image classification method based on spatial pyramid matching for sparse codes, using the JAFFE database of facial expressions. I show that the method is comparable to other methods, typically used for such datasets, and I also introduce some attempts that I made towards the improvement of the algorithm.

Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse...

This contribution deals with the Heeger-Bergen pyramid-based texture analysis/synthesis algorithm. It brings a detailed explanation of the original algorithm tested on many characteristic examples. Our analysis reproduces the original results, but also brings a minor improvement concerning non-periodic textures. Inspired by visual perception theories, Heeger and Bergen proposed to characterize ...

A motion estimation algorithm using wavelet approximation as an optical ow model has been developed to estimate accurate dense optical ow from an image sequence. This wavelet motion model is particularly useful in estimating optical ows with large displacement. Traditional pyramid methods which use the coarse-tone image pyramid by image burring in estimating optical ow often produce incorrect r...

The recent work on perception and measurement of visual motion has consistently advocated the use of a hierarchical representation and analysis. In most of the practical applications of motion perception it is absolutely necessary to be able to construct and process these hierarchical image representations in real-time. First, we discuss a simple scheme for coarse motion detection that highligh...

The algorithm developed uses an octree pyramid in which noise is reduced at the expense of the spatial resolution. At a certain level an unsupervised clustering without spatial connectivity constraints is applied. After the classification, isolated voxels and insignificant regions are removed by assigning them to their neighbours. The spatial resolution is then increased by the downprojection o...

The ignorance on spatial information and semantics of visual words becomes main obstacles in the bag-of-visual-words (BoW) method for image classification. To address the obstacles, we present an improved BoW representation using spatial pyramid coding (SPC) and visual word reweighting. In SPC procedure, we adopt the sparse coding technique to encode visual features with the spatial constraint....

We introduce a fast and efficient convolutional neural network, ESPNet, for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a new convolutional module, efficient spatial pyramid (ESP), which is efficient in terms of computation, memory, and power. ESPNet is 22 times faster (on a standard GPU) and 180 times smaller than the stateof-the-art semantic ...