This paper presents a new fully automatic method for segmenting upright people in the images. Is is based on the efficient graph cut segmentation. Since colour and texture prevent from discriminating this particular class, silhouette shape is used instead. The graph cut is guided by a non-binary template of silhouette that represents the probability of each pixel to be a part of the person to segment. Subsequently, part-based template is used to better take into account the different postures of a person. Our method is close to real time and is tested on a large person dataset.

In this paper, we propose a generic framework for 3D surface remeshing. Based on a metric-driven Discrete Voronoi Diagram construction, our output is an optimized 3D triangular mesh with a user defined vertex budget. Our approach can deal with a wide range of applications, from high quality mesh generation to shape approximation. By using appropriate metric constraints the method generates isotropic or anisotropic elements. Based on point-sampling, our algorithm combines the robustness and theoretical strength of Delaunay criteria with the efficiency of entirely discrete geometry processing . Besides the general described framework, we show experimental results using isotropic, quadric-enhanced isotropic and anisotropic metrics which prove the efficiency of our method on large meshes, for a low computational cost.

Reversible contrast mapping (RCM) is a simple integer transform that applies to pairs of pixels. For some pairs of pixels, RCM is invertible, even if the least significant bits (LSBs) of the transformed pixels are lost. The data space occupied by the LSBs is suitable for data hiding. The embedded information bit-rates of the proposed spatial domain reversible watermarking scheme are close to the highest bit-rates reported so far. The scheme does not need additional data compression, and, in terms of mathematical complexity, it appears to be the lowest complexity one proposed up to now. A very fast lookup table implementation is proposed. Robustness against cropping can be ensured as well

This paper proposes a high capacity low cost reversible watermarking scheme. Image is partitioned in pairs of pixels. In order to satisfy a congruence equation, the pairs obeying some simple constraints are transformed. By adding some correction data, the not transformed pixel pairs are enforced to obey the same equation, too. The payload and the correction data are embedded into the transformed pixels by simple additions. After watermark insertion, only the not transformed pixel pairs satisfy the congruence equation. This allows transformed pairs localization, payload and correction data extraction and, finally, exact recovery of the original image. The proposed scheme can provide, in a single pass, bit-rates greater than 1 bit per pixel without any additional data compression.

While in the continuous case, statistical models of histogram equalization/specification would yield exact results, their discrete counterparts fail. This is due to the fact that the cumulative distribution functions one deals with are not exactly invertible. Otherwise stated, exact histogram specification for discrete images is an ill-posed problem. Invertible cumulative distribution functions are obtained by translating the problem in a K-dimensional space and further inducing a strict ordering among image pixels. The proposed ordering refines the natural one. Experimental results and statistical models of the induced ordering are presented and several applications are discussed: image enhancement, normalization, watermarking, etc.

Abstract We present a novel clustering algorithm for polygonal meshes which approximates a Centroidal Voronoi Diagram construction. The clustering provides an efficient way to construct uniform tessellations, and therefore leads to uniform coarsening of polygonal meshes, when the output triangulation has many fewer elements than the input mesh. The mesh topology is also simplified by the clustering algorithm. Based on a mathematical framework, our algorithm is easy to implement, and has low memory requirements. We demonstrate the efficiency of the proposed scheme by processing several reference meshes having up to 1 million triangles and very high genus within a few minutes on a low‐ end computer.

The paper presents a digital color image watermarking scheme using a hypercomplex numbers representation and the quaternion Fourier transform (QFT). Previous color image watermarking methods are first presented and the quaternion representation is then described. In this framework, RGB pixel values are associated with a unique quaternion number having three imaginary parts. The QFT is presented; this transform depends on an arbitrary unit pure quaternion, /spl mu/. The value of /spl mu/ is selected to provide embedding spaces having robustness and/or perceptual properties. In our approach, /spl mu/ is a function of the mean color value of a block and a perceptual component. A watermarking scheme based on the QFT and the quantization index modulation scheme is then presented. This scheme is evaluated for different color image filtering processes (JPEG, blur). The fact that perceptive QFT embedding can offer robustness to luminance filtering techniques is outlined.

The now well-known SRTM (Shuttle Radar Topography Mission) mission, launched in February, 2000 for 11 days, used C-band and X-band interferometric SAR to acquire data all over the Earth surface. These data allowed to generate the world's largest DEM. The aim of our study is to assess the quality of the SRTM DEM derived from the X-SAR system, compared with the quality of a reference DEM and of DEM from other sources. An important aspect of the study is to be able to conclude on the real relevancy of the SRTM product compared to the already existing products, for instance is it to be considered as a new global topographic reference or should it be only limited to some applications. The chosen test site is the Grand Morin, watershed of the Seine river nearby Paris, France, that exhibits a wide thematic diversity. Beyond the qualification of SRTM products, we tried to develop a systematic framework for the analysis of quality, which constitutes a challenging issue nowadays.

This paper presents a new approach for watermarking of digital images providing robustness to geometrical distortions. The weaknesses of classical watermarking methods to geometrical distortions are outlined first. Geometrical distortions can be decomposed into two classes: global transformations such as rotations and translations and local transformations such as the StirMark attack. An overview of existing self-synchronizing schemes is then presented. Theses schemes can use periodical properties of the mark, invariant properties of transforms, template insertion, or information provided by the original image to counter geometrical distortions. Thereafter, a new class of watermarking schemes using the image content is presented. We propose an embedding and detection scheme where the mark is bound with a content descriptor defined by salient points. Three different types of feature points are studied and their robustness to geometrical transformations is evaluated to develop an enhanced detector. The embedding of the signature is done by extracting feature points of the image and performing a Delaunay tessellation on the set of points. The mark is embedded using a classical additive scheme inside each triangle of the tessellation. The detection is done using correlation properties on the different triangles. The performance of the presented scheme is evaluated after JPEG compression, geometrical attack and transformations. Results show that the fact that the scheme is robust to these different manipulations. Finally, in our concluding remarks, we analyze the different perspectives of such content-based watermarking scheme.

This paper introduces a video object segmentation algorithm developed in the context of the European project Art.live where constraints on the quality of segmentation and the processing rate (at least 10 images/second) are required. In order to obtain a fine segmentation (no blocking effect, boundaries precision, temporal stability without flickering), the segmentation process is based on Markov random field (MRF) modelling which involves consecutive frame difference and a reference image in a unified way. Temporal changes of the luminance are predominant when the reference image is not yet available whereas the reference image prevails for low textured moving objects or for objects which stop moving for a while. The increased processing rate comes from the substitution of some Markovian iterations with morphological operations without loss of quality. Simulation results show the efficiency of the proposed method in term of accuracy and complexity (/spl sime/6 images/second for 352/spl times/288 pixels YUV images on a low-end processor).

Image watermarking has today a growing success in the community of image processing. Many methods were already proposed making it possible to obtain increasingly more powerful algorithms. The multi-layer method that we propose aims at inserting a non binary message in an image. It is issued from techniques tested in communication, namely spread spectrum techniques and more specifically the CDMA (code division multiaccess). Applied to image watermarking, it makes it possible to insert a more significant payload in an image without degrading it. In addition this method is robust with all the typical image processing attacks : JPEG compression with a quality factor of 50%, collusion, histogram manipulations, average filters.

The paper proposes a spatial domain watermarking scheme for image authentication and, in case image content has been changed, for localization of the altered area. A pseudo-random set of pixels is selected by using a secret key and is marked with a visually meaningful watermark by exact histogram specification. The exact recovery of the watermark authenticates the image. Specially designed watermarks are used to locate the altered area. Image visual aspect as well as image histogram are preserved. The approach addresses gray level and color images as well.

In this paper, we present an evolution of IFS-based image compression schemes replacing usual affine mass map by cosine mass map, well adapted to high frequency contents and poor domain pools. This change implies the creation of a new IFS determination algorithm for solving the inverse problem, and a new way to quantize and store involved coefficients. We then present how VOP facilities can be performed using this new IFS family.

Delaunay triangulation is well known for its use in geometric design. A derived version of this structure, the Delaunay constrained triangulation, takes into account the triangular mesh problem in presence of rectilinear constraints. The Delaunay constrained triangulation is very useful for CAD, topography and mapping and in finite element analysis. This technique is still developing. We present a taxonomy of this geometric structure. First we describe the different tools used to introduce the problem. Then we introduce the different approaches highlighting various points of view of the problem. We focus on the Delaunay stable methods. A Delaunay stable method preserves the Delaunay nature of the constrained triangulation. Each method is detailed by its algorithms, performances, and properties. For instance we show how these methods approximate the generalised Voronoi diagram of the configuration. The Delaunay stable algorithms are used for 2.5D DEM design. The aim of this work is to demonstrate that the use of topographic constraints in a regular DEM without adding new points preserves the terrain shape. So the resulting DEM can be more easily interpreted because its realism is preserved and the mesh still owns all the Delaunay triangulation properties.

Our paper presents a watermarking scheme based on an insertion of similarities. In the first part different watermarking techniques are presented and classed. In the second part our scheme is described in its spatial and frequential implantations. Finally the different results and perspectives of the work are outlined.

In this work, we show that digital image processing methods can be applied to enhance the quality of X-ray microscopic images. One application of X-ray microscopy is imaging of biological specimens in their natural aqueous environment. Since X radiation can introduce structural changes in these objects when observing them at room temperature, it is sometimes necessary to take images with short exposure time. The image quality can thus be reduced due to low signal-to-noise ratio (SNR). Digital image processing methods can be applied to reduce image degradation caused by noise. Another example of a digital image processing method we applied to X-ray microscopy images is contrast enhancement of structures near the resolution limit of the microscope. Structures of 20 nm in size, which show weak contrast in the original image, become more clearly visible after restoration. Since image restoration methods are based on the knowledge of the optical transfer function (OTF) or the point spread function (PSF), a handy method for quick PSF determination is presented. An iterative image-restoration method was applied to pictures obtained with the Gottingen transmission X-ray microscope at BESSY. Results of image quality enhancement by this method are shown for images of polytene chromosomes of Chironomus Thummi larvae, test-structures and in situ hybridization on Xist RNA using biotinilated DNA probe.

A set of monocular images of a curved painting is taken from different viewpoints around its curved surface. After deriving the surface localization in the camera coordinate system we backproject the image on the curved surface and we flatten it. We analyze the perspective distortions of the scene in the case when it is mapped on a cylindrical surface. Based on the result of this analysis we derive the necessary number of views in order to represent the entire scene depicted on a cylindrical surface. We employ a matching-based mosaicing method for reconstructing the scene from the curved surface. The proposed method is appropriate to be used for painting reconstruction.

The purpose of the paper is to describe a multi-agent approach to edge detection as a distributed optimization problem. In this framework, edge detection is seen as a goal to be reached, expressed in terms of the minimization of an estimated edge detection error with respect to an ideal reference which is not explicitly known. Furthermore, an original method for distributed optimization is illustrated based on an initial partitioning of the image into zones corresponding to different characteristics. The initial partitioning can be further refined, based on the evaluation of the result obtained so far. Consistency between adjacent zones in the image is also taken into account. The implementation of this method as a multi-agent system is presented demonstrating the interest in using such systems for solving distributed optimization problems.

The paper presents an approach for reconstructing images painted on curved surfaces. A set of monocular images is taken from different viewpoints in order to mosaic and represent the entire scene. By using a priori knowledge about the support surface of the picture, we derive the surface localization in the camera coordinate system. An automatic mosaicing method is applied on the patterned images in order to obtain the complete scene. The mosaiced scene is visualized on a new synthetic surface by a mapping procedure.

Presents a new scheme for fractal image compression based on adaptive Delaunay triangulation. Such a partition is computed on an initial set of points obtained with a split and merge algorithm in a grey level dependent way. The triangulation is thus fully flexible and returns a limited number of blocks allowing good compression ratios. Moreover, a second original approach is the integration of a classification step based on a modified version of the Lloyd algorithm (vector quantization) in order to reduce the encoding complexity. The vector quantization algorithm is implemented on pixel histograms directly generated from the triangulation. The aim is to reduce the number of comparisons between the two sets of blocks involved in fractal image compression by keeping only the best representative triangles in the domain blocks set. Quality coding results are achieved at rates between 0.25-0.5 b/pixel depending on the nature of the original image and on the number of triangles retained.

This paper presents a new partitioning scheme for fractal image coding, based on triangles and quadrilaterals. The aim is to have the advantage of the triangles over the square and rectangular blocks, in terms of adaptivity to the image (reduction of the block effect), and to reduce the blocks number by grouping neighboring triangles into quadrilaterals. Quadrilaterals permit a reduction of the number of local contractive affine transformations composing the fractal transform, and thus to increase the compression ratio, while preserving the visual quality of the decoded image.

The principle of attractor image coding presented in this paper is based on the theory of IFS (iterated function systems). The algorithm exploits piece-wise similarities between block of different sizes. To improve the algorithms based on regular and square blocks, we propose an adaptive Delaunay triangulation of the image support. The originality of the method is to map the triangles on specific parts of the image in order to have lots of similarities between them.

Computational geometry provides many solutions to imaging problems, mainly for 3-D image compression, segmentation, and measurement. The authors present a method to partition a volume data with Voronoi polyhedra structured in a graph environment. A dynamic construction of the 3-D Voronoi diagram is defined using an interactive method, guided by the image information. The developed process has been applied to confocal laser scanning microscopy. The authors developed also two other geometrical models: the first one is the octree model and the second one is the Delaunay model, dual of the Voronoi diagram.>

Cooperative methods provide comfortable solutions to segmentation problems. Different works have been engaged in this way in order to combine edge and region based methods. The authors propose a split and merge approach to decompose the image into homogeneous Voronoi polygons. The cooperative aspect comes from the fact that some contours of Voronoi polygons are supported by frontiers issued from an edge detector. From a geometrical point of view, the originality of this work is in the use of punctual Voronoi diagrams as an approximation of generalized Voronoi diagrams based on polygonal seed.>

Deals with the specification of a wavelet for the representation of a discrete contour coded by Freeman chain code. Multiresolution analysis is a functional framework which allows one to build wavelets and to choose which wavelet to use for a given application. The discrete version of data in image processing domain has to be conserved and it needs specific algorithms and tools. The aim is to consider a discrete contour coded by Freeman sequence and to approximate it at several levels of resolution by use of an adapted wavelet. The authors compute the wavelet's coefficients which can be used to reconstruct the original discrete contour. Examples of implementation results with evaluation of entropy and fidelity of reconstruction are presented.>

A method is proposed to detect schistosome eggs in a microscopic environment connected to a computer. The advantage of this method is the use of a simple two pass algorithm. The first pass detects and analyzes the elementary cases of isolated objects. The second pass introduces an enhancement process to improve the classification ratio. Although the noisy environment of images results from the nature of the support, the segmentation process gives satisfactory results using an adaptive statistical thresholding method. The detection is improved by an a priori knowledge of the geometry of the eggs. The method is simple and its portability to a microcomputer environment is envisaged to obtain a transportable machine for mass screening campaigns.>

An iterative segmentation method is presented and illustrated on specific examples. Full control of each iteration step is obtained by combining local and global properties according to a model of the image structure. A consistent convergence criterion is derived from additional image structure properties and a test is proposed to evaluate adequacy of segmentation.