A complete and practical range image sensor development is presented in this paper: from the mathematical modeling to the shape reconstruction. This scanner aims to be integrated in a larger collaborative project. The nal goal is to provide a framework to allow easy comparisons of ancient wooden items by historians. Motivations and expected results are clearly stated in accordance to nancial and easy-to-use constraints. In order to alleviate the calibration process a new calibrating pattern is proposed. The pattern allow both calibration of camera and projector. The method is validated with experimental results. Experimental results are given for the calibration process and the range image acquisition. These results have been performed on both real and synthetic data, which allows us to comment quantitative performances as well as qualitative ones. They are quite encouraging and satisfactory.
In this paper, an objective criterion on wavelet filters in proposed. Wavelet transforms are used in number of important signal and image industrial processing tasks including image coding and denoising. The choice of the wavelet filter bank is is very important and is directly linked to the efficiency of the application.
Some criteria have been proposed such as regularity, size of the support of the wavelet and number of vanishing moments. The size of the wavelet support increases with the number of vanishing moments. The wavelet regularity is important to reduce the artifacts. The choice of an optimal wavelet is thus the result of a trade-off between the number of vanishing moments and artifacts. But there is only a partial correlation between filter regularity and reconstructed image quality.
The proposed criterion is composed of two indexes. The first one is a frequency index computed from the aliasing of the filters. The second is a spatial index computed from the spread of the coefficients in spatial domain. From these indexes a filter set can be represented by a point in a criteria-plan. The abscissa is given by the frequency index and the ordinate by the spatial index. The quality of a wavelet filter bank is a trade-off between frequency and spatial quality. So the quality of a wavelet filter bank can be assessed from the position of the corresponding point in the criteria-plan.
The coding and denoising performances are estimated for various filters (including orthogonal splines and Daubechies). These performances are connected to the indexes of each filter bank. The results show that the two proposed indexes allow :
1/ a good estimation of the coding and denoising performances of the
wavelet filters, and 2/ an objective comparison of the filters.
Some clues on the connection between our indexes and the kernel size in the Heisenberg-Gabor formula are also given.
KEYWORDS: Wavelets, Denoising, Wavelet transforms, Signal processing, Signal to noise ratio, Filtering (signal processing), Algorithms, Signal analyzers, Electronic filtering, Projection systems
This paper deals with the rational wavelet transform apply to a wavelet shrinkage problem. The rational multiresolution analysis (MRA) allows a better adaptation of the scale factor to the signal components than the dyadic one. The theory of the rational MRA is reviewed and a pyramidal algorithm for the computation of the fast orthogonal wavelet transform is proposed. Both, the analysis and the synthesis parts of the process are detailed. Moreover, using filters defined in Fourier domain, the implementation of the proposed algorithm is extended to this space. To illustrate the potential of rational analysis for signal processing, a wavelet shrinkage
application is presented.
This paper deals with the analysis of ancient wooden stamps. The aim is to extract a binary image from the stamp. This image must be the closer to the image produced by inking and using a printing press with the stamps. A range image based method is proposed to extract a stamped image from the stamps. The range image acquisition from a 3D laser scanner is presented. Pre-filtering for range image enhancement is detailed. The range image binarization method is based on an adaptive thresholding. Few simple processes applied on the range image enable a final binarized image computing. The proposed method provides here a very efficient way to perform "virtual" stampings with ancient wooden stamps.
Traditionally, medical geneticists have employed visual inspection (anthroposcopy) to clinically evaluate dysmorphology. In the last 20 years, there has been an increasing trend towards quantitative assessment to render diagnosis of anomalies more objective and reliable. These methods have focused on direct anthropometry, using a combination of classical physical anthropology tools and new instruments tailor-made to describe craniofacial morphometry. These methods are painstaking and require that the patient remain still for extended periods of time. Most recently, semiautomated techniques (e.g., structured light scanning) have been developed to capture the geometry of the face in a matter of seconds. In this paper, we establish that direct anthropometry and structured light scanning yield reliable measurements, with remarkably high levels of inter-rater and intra-rater reliability, as well as validity (contrasting the two methods).
KEYWORDS: Edge detection, Signal to noise ratio, Detection and tracking algorithms, Signal processing, Machine vision, Pixel resolution, Image processing, Digital filtering, Sensors, Linear filtering
Blurred images are produced by interpolation process. A wavelet-based magnification method is proposed that both increases the resolution of an image and adds local high- frequency information, in order to provide digitally zoomed images with sharp edges. Wavelet transforms computed by the decimated Mallat's algorithm present pyramidal aspect. This pyramidal analysis combined with a prediction of high- frequency coefficients is used to produce a magnified image. The prediction is based on a zero-crossings representation and on the construction of a multiscale edge-signature database. Performances are evaluated for synthetic and noisy images. A significant improvement regarding some classical methods is observed.
Using the wavelet transform (WT), a given signal is decomposed into a succession of embedded approximations and detail coefficients. The observation of the details shows that similarities can be noticed across scales, in particular for the transitions (edges in an image). A wavelet-based magnification that both increases the resolution of an image and adds high-frequency information is proposed in this paper. From a non-subsampled WT, the zero-crossings of the details coefficients provide a consistent representation. From these coefficients, a prediction of high-frequency coefficients is possible via the computation of local Liptschitz exponents but needs an interpolation due to the constancy of the number of details coefficients. The proposed magnification is based on the decimated Mallat's algorithm. As this transformation is not shift-invariant, the local laws cannot be computed. The prediction is realized via the learning of representative edge signatures. A multiscale database is therefore constructed from the edge's zero-crossings. The magnification quality is evaluated by application on synthetic and noisy images.
Wavelet transforms are efficient tools for texture analysis and classification. Separable techniques are classically used but present several drawbacks. First, diagonal coefficients contain poor information. Second, the other coefficients contain useful information only if the texture is oriented in the vertical and horizontal directions. So an approach of texture analysis by non-separable transform is proposed. An improved interscale resolution is allowed by the quincunx scheme and this analysis leads to only one detail image where no particular orientation is favored. New orthogonal isotropic filters for the decomposition are constructed by applying McClellan transform on one dimension B-spline filters. The obtained wavelet function have better isotropic and frequency properties than those previously proposed by Feauveau. Since IIR filters are obtained, an integration in Fourier domain of the whole operations of the transform is proposed. A texture analysis is performed on wavelet details coefficients. Simple parameters are calculated from each scale. Finally, the evolution over scales of the parameters is obtained and this multiscale parameter is used to characterize the different textures. An application of this method is posed with the analysis of human cells. The aim is to distinguish states of evolution. As no information is provided by monoscale classical methods on these images, the proposed process allows to identify several states. In this process a reference curve is constructed for each states calculated from the multiscale variance of known images. When a new image is analyzed, a new evolution curve is calculated and a measure of the distance with the references is done. This technique is more efficient than classical ones as multiscale information is used.
In this paper an efficient method is presented to cope with the need of phase linear filters in orthonormal wavelet transform for image processing. Phase linear filtering can be obtained in two dimensions by using Cohen/Daubechies biorthogonal wavelets. But as orthogonal analysis is preferable, a new method to construct orthonormal bidimensional wavelet base in the quincunx scheme is proposed. These filters are designed by applying the McClellan Transform on 1D B-spline filters in order to get 2D orthonormal quincunx non-separable ones. This method takes advantage of the orthogonality of the analysis and of the quincunx scheme, indeed these filters lead to only one approximation image and only one detail image. The interscale resolution given by this analysis is twice more accurate than in the case of a separable analysis and the wavelet functions have better isotropic and frequency properties than those previously proposed by Feauveau.
KEYWORDS: Edge detection, Deconvolution, Image processing, Wavelets, Detection and tracking algorithms, Image processing algorithms and systems, Human vision and color perception, Zoom lenses, Image segmentation, Binary data
We present a restoration algorithm using a stable deconvolution for the real images with which we are confronted in our laboratory. In this images, the edge smoothness due to the lack of focusing is not homogeneous. The parameters of the algorithm are determined by the merging of multiscale edge detection. The edge detection is based on a generalization of the Canny-Deriche filter. This filter is used to generate a tight frame of wavelets. We propose a new criterium, the separating power, which allows to choose in a deterministic way the intrinsic parameters of the filters. This criterium introduces the concept of detection resolution which is a new concept in edge detection. Also, we evaluate the maximal delocalization obtained at the limit of the separating power. The merging algorithm is based on human vision which zooms back and forth on the image in order to identify global structures or details in the image. The merging method can be followed by a segmentation method selected from existing ones thus enabling a great adaptability of the complete system in the edge sense. The binary edge representation obtained selects the zones of restoration. Examples of result are presented.
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