Olivier Laligant

A Nonlinear Derivative Scheme Applied to Edge Detection

This paper presents a nonlinear derivative approach to addressing the problem of discrete edge detection. This edge detection scheme is based on the nonlinear combination of two polarized derivatives. Its main property is a favorable signal-to-noise ratio (SNR) at a very low computation cost and without any regularization. A 2D extension of the method is presented and the benefits of the 2D localization are discussed. The performance of the localization and SNR are compared to that obtained using classical edge detection schemes. Tests of the regularized versions and a theoretical estimation of the SNR improvement complete this work.

Review of industrial applications of wavelet and multiresolution-based signal and image processing

Twenty five years after the seminal work of Jean Morlet, the wavelet transform, multiresolution analysis, and other space-frequency or space-scale approaches are considered standard tools by researchers in image processing. Many applications that point out the interest of these techniques have been proposed. We review the recent published work dealing with industrial applications of the wavelet and, more generally speaking, multiresolution analysis. We present more than 190 recent papers.

Wavelets in industrial applications: a review

This paper aims at reviewing the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. After a quick recall in a simple overview of the basics of wavelet transform and of its main variations, some of its applications are reviewed domain by domain, beginning with signal processing, continuous and discrete wavelet transform proceeding with image processing and applications. More than 150 recent papers are presented in these two sections.

Texture Discrimination Using HierarchicalComplex Networks

Texture analysis represents one of the main areas in image processing and computer vision. The current article describes how complex networks have been used in order to represent and characterized textures. More speci?cally, networks are derived from the texture images by expressing pixels as network nodes and similarities between pixels as network edges. Then, measurements such as the node degree, strengths and clustering coe?cient are used in order to quantify properties of the connectivity and topology of the analyzed networks. Because such properties are directly related to the structure of the respective texture images, they can be used as features for characterizing and classifying textures. The latter possibility is illustrated with respect to images of textures, DNA chaos game, and faces. The possibility of using the network representations as a subsidy for DNA characterization is also discussed in this work.

Regularization Preserving Localization of Close Edges

In this letter, we address the problem of the influence of neighbor edges and their effect on the edge delocalization while extracting a neighbor contour by a derivative approach. The properties to be fulfilled by the regularization operators to minimize or suppress this side effect are deduced, and the best detectors are pointed out. The study is carried out in 1-D for discrete signal. We show that among the derivative filters, one of them can correctly detect our model edges without being influenced by a neighboring transition, whatever their separation distance is and their respective amplitude is. A model of contour and close transitions is presented and used throughout this letter. The noise effect on the edge delocalization is recalled through one of the Canny criteria. Different derivative filters are applied onto synthetic images, and their performances are compared

Discrete wavelet transform implementation in Fourier domain for multidimensional signal

Wavelet transforms are often calculated by using the Mallat algorithm. In this algorithm, a signal is decomposed by a cascade of filtering and downsampling operations. Computing time can be important but the filtering operations can be speeded up by using fast Fourier transform (FFT)-based convolutions. Since it is necessary to work in the Fourier domain when large filters are used, we present some results of Fourier-based optimization of the sampling operations. Acceleration can be obtained by expressing the samplings in the Fourier domain. The general equations of the down- and upsampling of digital multidimensional signals are given. It is shown that for special cases such as the separable scheme and Feauveau’s quincunx scheme, the samplings can be implemented in the Fourier domain. The performance of the implementations is determined by the number of multiplications involved in both FFT-convolution-based and Fourier-based algorithms. This comparison shows that the computational costs are reduced when the proposed implementation is used. The complexity of the algorithm is O(N log N). By using this Fourier-based method, the use of large filters or infinite impulse response filters in multiresolution analysis becomes manageable in terms of computation costs. Mesh simplification based on multiresolution “detail relevance” images illustrates an application of the implemenentation.

Wavelets transform in artificial vision inspection of threading

We present an application of multiresolution analysis with orthonormal wavelets of 1D signal to quality control by artificial vision. The purpose of the control is to check the thread of a polyethylene bottle stopper. Using a section picture of the stopper provided by a linear CCD camera, we calculate the wavelet coefficients of the first three levels of resolution. The energy densities of these coefficients calculated on a given area, provide three discriminant parameters which permit to distinguish correctly between the two classes (defectless, defective) according to a classifying method which can run without supervision after a period of training.<<ETX>>

Frame of wavelets for edge detection

We present in the following work, a multiscale edge detection algorithm whose aim is to detect edges of any slope. Our work is based on a generalization of the Canny-Deriche filter, modelized by a more realistic edge than the traditional step shape edge. The filter impulse response is used to generate a frame of wavelets. For the merging of the wavelet coefficients, we use a geometrical classifier developed in our laboratory. The segmentation system thus set up and after the training phase does not require any adjustment nor parameter. The main original property of this algorithm is that it leads to a binary edge image without any threshold setting.

Merging system for multiscale edge detection

We present a multiscale edge detection algorithm whose aim is to detect edges whatever their slope. Our work is based on a gener- alization of the Canny-Deriche filter, characterized by a more realistic edge than the traditional step shape edge. The filter impulse response is used to generate a multiscale edge detection scheme. For the merging of the edge information, we use a geometrical classifier developed in our laboratory. The segmentation system thus set up, after the training phase, does not require any adjustment or depend on any parameter. The main original property of this algorithm is that it leads to a binary edge image without any threshold setting. The quality of the results is inferior to that for classical multiscale merging approaches; nevertheless, this system, studied for real-time functioning, presents satisfactory per- formance for well-contrasted images and excellent performance for noisy

Multi-scale analysis of shell growth increments using wavelet transform

Abstract Shell increments contain information related to the evolution of the environment in which the organism grew during its biomineralization. To extract the information from variations in shell topography, a new and promising technique is presented, involving multi-scale analysis of the shell topography using a B-spline wavelet transform. An accurate non-contact optical system, based on laser triangulation, is used to map the shell surface. The resulting range image is treated as a grey-level image by using a multi-resolution approach based on the generalization of the cascade algorithm. This method allows reconstruction of non-subsampled images that correspond to the projection onto the space of the chosen scale of detail. This new approach provides an efficient tool for analyzing multi-scale information contained in growth increment rings and/or within quasi-periodic features. In conclusion, this approach can be applied to any 3D object, in order to extract features such as rhythmic information, color variations or object envelope.