Key Points-based Indexing for Pre-attentive Similarities: The KIWI System

In this paper, we introduce the general architecture of an image-search engine based on pre-attentive similarities. Local features are computed in key points to represent local properties of the images. The location of key points, where local features are computed, is discussed. We present two new key point detectors designed for image retrieval, both based on multi-resolution: the contrast-based point detector, and the wavelet-based point detector. Four different local features are used in our system: differential invariants, texture, shape and colour. The local information computed in each key point is stored in 2D histograms to allow fast querying. We study the choice of the key points detector depending on the feature used, for different test sets. The Harris corner detector is used for benchmarking. Uniformly distributed points are also used, and we conclude for which applications they are effective. Finally, we show that point detector and feature efficiency depend upon the test set studied.     

A wavelet-based framework for acquired radiometric quantity representation and accurate physical rendering

In this paper, we present a framework based on a generic representation, which is able to handle most of the radiometric quantities required by global illumination software. A sparse representation in the wavelet space is built using the separation between the directional and the wavelength dependencies of such radiometric quantities. Particularly, we show how to use this representation for spectral power distribution, spectral reflectance and phase function measurements modeling. Then, we explain how the representation is useful for performing spectral rendering. On the one hand, it speeds up spectral path tracing by importance sampling to generate reflected directions and by avoiding expensive computations usually done on-the-fly. On the other hand, it allows efficient spectral photon mapping, both in terms of memory and speed. We also show how complex light emission from real luminaires can be efficiently sampled to emit photons with our numerical model.     

Wavelet analysis to characterize cluster dynamics in a circulating fluidized bed

A common hydrodynamic feature in heavily loaded circulating fluidized beds is the presence of clusters. The continuous formation and destruction of clusters strongly influences particle hold-up, pressure drop, heat transfer at the wall, and mixing. In this paper fiber optic data is analyzed using discrete wavelet analysis to characterize the dynamic behavior of clusters. Five radial positions at three different axial locations under five different operating conditions spanning three different flow regimes were analyzed using discrete wavelets. Results are summarized with respect to cluster size and frequency.     

Discrimination of locally stationary time series using wavelets

Time series are sometimes generated by processes that change suddenly from one stationary regime to another, with no intervening periods of transition of any significant duration. A good example of this is provided by seismic data, namely, waveforms of earthquakes and explosions. In order to classify an unknown event as either an earthquake or an explosion, statistical analysts might be helped by having at their disposal an automatic means of identifying, at any time, which pattern prevails. Several authors have proposed methods to tackle this problem by combining the techniques of spectral analysis with those of discriminant analysis. The goal is to develop a discriminant scheme for locally stationary time series such as earthquake and explosion waveforms, by combining the techniques of wavelet analysis with those of discriminant analysis.     

Sparse BEM for potential theory and Stokes flow using variable order wavelets

Wavelets for the discretization of boundary integral operators usually have fixed order and are constructed in some parameter space of the surface. Here a new approach is presented, where the order is flexible and no parameterizations are needed. The wavelets are restrictions of piecewise polynomial functions in three variables on the boundary manifold. This construction is especially suited for surfaces with complicated geometries. If the polynomial order is suitably adjusted to the level of the wavelet, then the non-standard form of a large class of boundary integral operators can be truncated to contain only O(N) non-vanishing entries while retaining the asymptotic convergence of the full Galerkin scheme. An algorithm which sets up the basis and the non-standard form in O(N) operations will be discussed. The method is applied to problems from potential theory and Stokes flow and compared with the Fast Multipole Method.     

Eigen-image based compression for the image-based relighting with cascade recursive least squared networks

This paper presents a principal component analysis (PCA) based data compression method for the image-base relighting (IBL) technology, which needs tremendous reference images to produce high quality rendering. The method contains two main steps, eigen-image based representation and eigen-image compression. We extract eigen-images by the cascade recursive least squared (CRLS) networks based PCA due to the large data dimension. By keeping only a few important eigen-images, which are enough to describe the IBL data set, the data size can be drastically reduced. To further reduce the data size, we use the embedded zero wavelet (EZW) approach to compress those retained eigen-images, and use uniform quantization plus arithmetic coding to compress the representing coefficients. Simulation results demonstrate that our approach is superior to that of compressing reference images separately with JPEG or EZW.     

Fast Wavelet Radiosity Method

Wavelet analysis has been found [1] to be very useful for functional representation and accurate global solution of radiosity. In radiosity we deal with functions in 2D and 4D spaces. Under such conditions, the biggest bottleneck in applying this wavelet analysis seems to be the large number of multidimensional inner products. In this paper, we propose (i) the use of interpolating wavelets for fast inner product computation and consequently for faster wavelet radiosity solution (ii) the use of hierarchical decomposition technique for determining the smoothness of the radiosity function for optimal adaptive subdivision.     

Multi-resolution analysis with frontal decomposition

Use of the compactlyB-spline wavelet of Chui and Wang (1991); Chui (1992) is hindered by loss of accuracy on decomposition, through truncation of weight sequences which are countably infinite. Adaptations to finite intervals often encounter problems at boundaries. For multiresolution analysis on a finite interval employing the linearB-wavelet the present research provides a frontal approach to decomposition which avoids truncation of weight sequences, experiences no problems at boundaries, and which is exhibits a factor of three increase in computational efficiency. The boundary wavelets which complete the linearB-wavelet basis on a finite interval are constructed.