Frederic Nicolier

Binary-image comparison with local-dissimilarity quantification

In this paper, we present a method for binary image comparison. For binary images, intensity information is poor and shape extraction is often difficult. Therefore binary images have to be compared without using feature extraction. Due to the fact that different scene patterns can be present in the images, we propose a modified Hausdorff distance (HD) locally measured in an adaptive way. The resulting set of measures is richer than a single global measure. The local HD measures result in a local-dissimilarity map (LDMap) including the dissimilarity spatial layout. A classification of the images in function of their similarity is carried out on the LDMaps using a support vector machine. The proposed method is tested on a medieval illustration database and compared with other methods to show its efficiency.

Subpixel edge detection for dimensional control by artificial vision

Dimensional control by artificial vision is becoming a standard tool for industrialists interested in such remote and without contact measurement methods. The expected accuracy of those systems is dependent on camera resolution. High precision requires very costly charge coupling device sensors and frame grabbers. The proposed method tends to increase significantly the precision of dimensional measurements without increasing the hardware com- plexity. This algorithm is also quite robust against noisy images as it can be encountered in real world imaging; a precision of 1/16 pixel can easily be obtained with signal to noise ratio52 dB. Our ap- proach aims at improving the edge detection process involved in dimensional control by artificial vision. A lot of edge detection tech- niques with pixel resolution are well known and some of them are designed in order to be robust against image corruption. On the other hand B-spline interpolation methods have been considerably improved and popularized by the signal processing techniques pro- posed by M. Unser et al. An algorithm resulting from the merging of these two ideas is proposed in this paper. In this algorithm, the interpolation is prepared by an optimized filtering and by a detection of local maxima of gradient.

A fast binary-image comparison method with local-dissimilarity quantification

Image similarity measure is widely used in image processing. For binary images that are not composed of a single shape, a local comparison is interesting but the features are usually poor (color) or difficult to extract (texture, forms). We present a new binary image comparison method that uses a windowed Hausdorff distance in a pixel-adaptive way. It enables to quantify the local dissimilarities and to give their spatial distribution which greatly improves the dissimilarity information. Combined with a support vector machine classifier, this method is successfully tested on a medieval-impression database

B-spline quincunx wavelet transform and implementation in Fourier domain

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.

Three-dimensional tools for analysis and conservation of ancient wooden stamps

Abstract This paper deals with the analysis and conservation of ancient wooden stamps from museum or library collections. The aim is to provide historians with tools that ease the process of handling and observation of very fragile and unique objects. By performing a three-dimensional imaging of stamps, data are processed in three different ways: (1) adaptive thresholding on the corresponding range image enables visualization for the first time of an image of the actual print produced by the stamp; (2) interactive enhanced rendering provides a realistic and non-photorealistic interactive visualization; and, finally, (3) rapid prototyping production gives a perfect geometrical facsimile of the stamps, preventing any hazards inherent in the handling of the originals.

Assessment of mesh simplification algorithm quality

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).

A new similarity measure using Hausdorff distance map

Image dissimilarity measure is a hot topic. The measuring process is generally composed of an information mining in each image which results in an image signature and then a signature comparison to make the decision about the image similarity. In the scope of binary images, we propose to replace the information mining by a new straight image comparison which does not require a priori knowledge. The second stage is then replaced by a decision process based on the image comparison. The new comparison process is structured as follows: a morphological multiresolution analysis is applied to the two images. Secondly a distance map is constructed at each scale by the computation of the Hausdorff distance, restricted through a sliding-window. A signature is then extracted from the distance map and is used to make the decision. As an application, the algorithm has been successfully tested on an ancient illustration database.

Hausdorff distance-based multiresolution maps applied to image similarity measure

Abstract Image comparison is widely used in image processing. For binary images that are not composed of a single shape, a local comparison can be interesting because the features are usually poor (colour) or difficult to extract (texture, forms). Thus a new binary image comparison method that uses a windowed Hausdorff distance is presented. It enables local dissimilarities to be quantified in a simple way. The comparison results in a dissimilarity map. These maps are then used to evaluate the image similarity. The evaluation uses a classification step that is based on a comparison of the dissimilarity map histogram with reference histograms. The comparison is carried out at different scales of a multiresolution analysis, allowing the most discriminating scale in a user-defined notion of dissimilarity to be chosen automatically in the learning step. As an application, a database of digitalized ancient illustrations is successfully processed by the new method.

Low-cost system for ancient stamps range image acquisition

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.

Human cell texture analysis with quincunx spline wavelet transform

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.