We first briefly review the role of lattice paths in the derivation of fermionic expressions for the M(p, p′) minimal model characters of the Virasoro Lie algebra. We then focus on the recently introduced half-lattice paths for the M(p, 2p ± 1) chara

In atmospheric sensing, one application that has demonstrated several impressive successes over the last two decades is the light detection and ranging (LIDAR). Elastic LIDAR has shown an important capability in providing aerosol density and spatial distribution from a standoff position. However, it provides limited information on the material composition of the aerosol component. On the other hand, inelastic LIDARs (including laser-induced fluorescence and Raman LIDARs) measure the spectrally distributed returned signal that may contain important clues about the nature of the scatterers. In order to investigate the capability of these LIDARs in characterizing bioaerosols from a standoff position, Defence Research & Development Canada initiated a three-year program in spring 1999, named SINBAHD (Standoff Integrated Bioaerosol Active Hyperspectral Detection). The aim of the program was to investigate the sensitivity and discrimination capabilities of an inelastic LIDAR based on the intensified range-gated spectral detection of laser-induced fluorescence. An exploratory prototype based on this technique has shown sensitivity of a few living bioaerosol particles per liter of air for a range of 1.4 km at night. Furthermore, based on spectral signatures measured during open-air releases, good discrimination capabilities were obtained between Bacillius subtilis var globiggi (BG) and Erwinia herbicola (EH). These results agree well with a performance model using Raman returns from atmospheric nitrogen as a calibration tool.

Multimedia data accessibility depends on a precise indexing, involving a computational cost. This paper proposes a new fast method of segmentation and indexing in order to fill out in an automatic way several MPEG7 [ISO/IEC, 1999] fields (e.g. camera and objects movement). In order to accelerate segmentation process, we exploit most of the information contained in MPEG1-2 flow [ISO/IEC, 1995], the decompression is restricted to entropic decoding and inverse quantization, the estimation of the camera movement is obtained from MPEG1-2 movement prediction. Segmentation in homogeneous color zones is obtained by a "split and merge" algorithm improved by a B-splines active contour segmentation regularization [F. Precioso, M. Barlaud, 2002].

This paper deals with the supervised classification of synthetic aperture radar (SAR) images. Our approach is based on two criteria, which explicitly take into account the intensity of the SAR image and the neighborhood classes, similarly to the Pots model, but weighted by a discontinuity map. The high level of noise involves numerous classification errors. We classify a restored image filtered with a well-adapted algorithm to clustering. Moreover, we isolate the texture of SAR images in order to help the classification. Finally, we present results on real SAR images.

Coherent imagery emerges as one of the major domains in image processing and includes topics as diversified as radar, medical and surface analysis. Whatever the application, the resulting image is noisy corrupted. In coherent imaging, images suffer from speckle noise, whose main characteristic is to be multiplicative. The proposed method takes explicitly into account the multiplicative property of the noise while preserving discontinuities in the restored image. Moreover, in a second step our algorithm estimates the noise, thus the information contained in the speckle still remains usable.

This paper proposes an adaptive vector quantization scheme designed for spaceborne raw SAR data compression. This approach is based on the fact that spaceborne raw data are Gaussian distributed, independent, and quite stationary over an interval (in both azimuth and range) which depends on SAR system parameters. The block gain adaptive vector quantization (BGAVQ) is a generalization of the block adaptive quantization (BAQ) algorithm to vectors. It operates as a set of optimum vector quantizers (designed by the LBG algorithm) with different gain settings. The adaptation is particularly efficient since, for a fixed compression ratio, the same codebook is used for any spaceborne SAR data. Results on simulated and real images, for data rate of 1.5 to 2 bit/sample, have confirmed the expected performance of the BGAVQ algorithm.

This paper deals with a new method for estimation of motion field in image sequence coding domain. The proposed method is based on a pel-recursive technique and characterised by two fundamental points: the use of a convex function for the regularization and a deterministic relaxation algorithm for edge preserving regularization. These two points are completely new in motion estimation techniques.>

Introduces a new image coding scheme using lattice vector quantization. The proposed method involves two steps: biorthogonal wavelet transform of the image, and lattice vector quantization of wavelet coefficients. In order to obtain a compromise between minimum distortion and bit rate, we must truncate and scale the lattice suitably. To meet this goal, we need to know how many lattice points lie within the truncated area. We investigate the case of Laplacian sources where surfaces of equal probability are spheres for the L(1) metric (pyramids) for arbitrary lattices. We give explicit generating functions for the codebook sizes for the most useful lattices like Z(n), D(n), E(s), wedge(16).

The image coding scheme involves two steps: biorthogonal wavelet transform of the image and pyramidal lattice vector quantization of wavelet coefficients. In order to obtain a compromise between minimum distortion and bit rate, one must truncate and scale the lattice suitably. To meet this goal, one needs to know how many lattice points lie within the truncated area. The case of Laplacian sources where surfaces of equal probability are spheres for the L/sub 1/ metric (pyramids) for arbitrary lattices is investigated. Explicit generating functions for the codebook sizes of the most useful lattices are given.>

A scheme for image compression that takes into account psychovisual features both in the space and frequency domains is proposed. This method involves two steps. First, a wavelet transform used in order to obtain a set of biorthogonal subclasses of images: the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains constant the number of pixels required to describe the image. Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise shaping bit allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. It is shown that the wavelet transform is particularly well adapted to progressive transmission.

An image coding scheme has been introduced by the authors (see IEEE ICASSP, p.2297, 1990). This scheme involves two steps. A biorthogonal wavelet transform is applied to the original image, and wavelet coefficients are then vector quantized using the LBG (Linde, Buzo and Gray, 1980) method. The purpose of this work is to propose a new scheme for vector quantization of wavelet coefficients. The proposed method is based on lattice vector quantization. The application of the D/sub 4/, E/sub 8/ and Barnes-Wall Lambda /sub 16/ lattices is investigated. These lattices are used to encode wavelet coefficients whose PDFs are close to Laplacian. A variable-length coding method is applied and the trade-off between distortion and optimal rate is investigated. Experimental results on the Lena image using the Lambda /sub 16/ lattice leads to a peak signal-to-noise ratio (PSNR) of 31.14 dB at 0.08 bpp. This result outperforms, to the authors knowledge, all other methods. Edges which are most of interest for image analysis are particularly sharp without any smoothing artefacts.>

A new method of multiresolution edge detection is described. This method is based on a recursive biorthogonal wavelet transform which relates the digital filter to a continuous analysis function, the so-called wavelet. In biorthogonal wavelet analysis, there is a high-pass filter corresponding to the wavelet and a low-pass filter corresponding to the scaling function. The new idea is to compute optimal edge detection filters which yield multiresolution analysis (continuous wavelet). The high-pass filter (infinite impulse filter) is chosen in order to have first derivative properties. The family of low-pass filters is deduced from the high-pass filter using wavelet transform theory relations. The detection filters are proposed as a trade-off between detection, localization and regularity criteria.>

A new method is proposed for image coding involving two steps. First, the authors use a dual recursive wavelet transform in order to obtain a set of subclasses of images with better characteristics than the original image (lower entropy, edges discrimination, etc.). Secondly, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized. The purpose of this work is to present and study new recursive filter banks with perfect reconstruction as an alternative to FIR (finite impulse response) filters which are commonly used in wavelet analysis. The authors present two kinds of experimental results: coding of the well known Lena image with only two multiplications per pixel and then with an optimized IIR (infinite impulse response) filter.>

A two-step scheme for image compression that takes into account psychovisual features in space and frequency domains is proposed. A wavelet transform is first used in order to obtain a set of orthonormal subclasses of images; the original image is decomposed at different scales using a pyramidal algorithm architecture. The decomposition is along the vertical and horizontal directions and maintains the number of pixels required to describe the image at a constant. Second, according to Shannon's rate-distortion theory, the wavelet coefficients are vector quantized using a multiresolution codebook. To encode the wavelet coefficients, a noise-shaping bit-allocation procedure which assumes that details at high resolution are less visible to the human eye is proposed. In order to allow the receiver to recognize a picture as quickly as possible at minimum cost, a progressive transmission scheme is presented. The wavelet transform is particularly well adapted to progressive transmission.>

This paper proposes a new method for image coding involving two steps. First, we use a 'Dual Recursive Wavelet' Transform in order to obtain a set of subclasses of images with better characteristics than the original image (lower entropy, edges discrimination, ... ). Second, according to Shannon's rate distortion theory, the wavelet coefficients are vector quantized using the Kohonen Self-Organizing Feature Maps. We compare this training method with the well known LBG algorithm.

A well-known method to solve ill-posed problem in image restoration is to use regularization techniques. The purpose of this paper is to propose a new scheme for digital image restoration based on a regularization method and on the BiOrthogonal Wavelet Transform. We show that, in cases where the blur function can be considered a scale function of a biorthogonal multiresolution analysis, it is possible to obtain an efficient family of regularization operators from the convolution operator alone.

Recursive 2D Kalman filtering has been successfully used in image restoration. In order to reduce computational work and large amounts of storage, a fast algorithm has been introduced by Biemond. However this method is based on the DFT transform and ringing on the restored image results from boundary conditions. Furthermore, the Kalman gain factors may increase due to the zeros of the Fourier transform of the PSF periodicity is realized by means of a new image mirror. No more computational work is required since this new image is symmetric. Then the authors introduce adaptive Kalman filtering in order to avoid divergence of Kalman gain factors and to reduce the remaining ringing.>