This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.

Video matting is the process of pulling a high-quality alpha matte and foreground from a video sequence. Current techniques require either a known background (e.g., a blue screen) or extensive user interaction (e.g., to specify known foreground and background elements) . The matting problem is generally under-constrained, since not enough information has been collected at capture time. We propose a novel, fully autonomous method for pulling a matte using multiple synchronized video streams that share a point of view but differ in their plane of focus. The solution is obtained by directly minimizing the error in filter-based image formation equations, which are over-constrained by our rich data stream. Our system solves the fully dynamic video matting problem without user assistance: both the foreground and background may be high frequency and have dynamic content, the foreground may resemble the background, and the scene is lit by natural (as opposed to polarized or collimated) illumination.

A multiscale morphological dilation-erosion smoothing operation and its associated scalespace expansion for multidimensional signals are proposed. Properties of this smoothing operation are developed and, in particular a scale-space monotonic property for signal extrema is demonstrated. Scale-space fingerprints from this approach have advantages over Gaussian scale-space fingerprints in that they: are defined for negative values of the scale parameter

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for many problems, including studies in temporal change detection of morphology, and 3D visualizations for surgical planning. We present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: Expectation /Maximization segmentation, binary mathematical morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation such that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on

Multiscale image analysis has been used successfully in a number of applications to classify image features according to their relative scales. As a consequence, much has been learned about the scale-space behavior of intensity extrema, edges, intensity ridges, and grey-level blobs. In this paper, we investigate the multiscale behavior of gradient watershed regions. These regions are defined in terms of the gradient properties of the gradient magnitude of the original image. Boundaries of gradient watershed regions correspond to the edges of objects in an image. Multiscale analysis of intensity minima in the gradient magnitude image provides a mechanism for imposing a scale-based hierarchy on the watersheds associated with these minima. This hierarchy can be used to label watershed boundaries according to their scale. This provides valuable insight into the multiscale properties of edges in an image without following these curves through scale-space. In addition, the gradient watershed region hierarchy can be used for automatic or interactive image segmentation. By selecting subtrees of the region hierarchy, visually sensible objects in an image can be easily constructed.

A highly robust channel coding scheme to be seamlessly integrated into our previously developed high performance wavelet-based significance-linked connected component analysis (SLCCA) image coding technique is proposed. The SLCCA source coding algorithm is slightly modified to enable synchronization after each bit-plane. Thus even after uncorrectable errors, the decoding can be continued at the next refinement level. An unequal error protection scheme is also developed by using Reed-Solomon codes. The proposed scheme outperforms Sherwood and Zeger's technique by 0.66 dB on average for the "Lena" image in peak signal-to-noise ratio. Furthermore, the proposed codec has very low computational complexity well applicable for the power constrained mobile scenario.

Triangle meshes are a popular representation of surfaces in computer graphics. Our aim is to detect feature on such surfaces. Feature regions distinguish themselves by high curvature. We are using discrete curvature analysis on triangle meshes to obtain curvature values in every vertex of a mesh. These values are then thresholded resulting in a so called binary feature vector. By adapting morphological operators to triangle meshes, noise and artifacts can be removed from the feature. We introduce an operator that determines the skeleton of the feature region. This skeleton can then be converted into a graph representing the desired feature. Therefore a description of the surface’s geometrical characteristics is constructed.

Van Herk has shown that the erosion/dilation operator with a linear structuring element of an arbitrary length can be implemented in only 3 min/max operations per pixel. In this paper, the algorithm is generalized to erosions and dilations along discrete lines at arbitrary angles. We also address the padding problem; so that the operation can be performed in place without copying the pixels to and from an intermediate buffer. Applications to image filtering and to radial decompositions of discs are presented. Keywords. Mathematical morphology, image filtering, algorithms, recursivity, line and periodic structuring elements, radial decompositions. Name and address of corresponding author: Dr. Pierre Soille LGI2P Ecole des Mines d'Al`es Parc scientifique Georges Besse F--30000 Nimes France Ph.: int+33- 66 38 70 22 Fax: int+33- 66 38 70 74 Email: soille@eerie.fr Pierre Soille is with the Laboratoire de G'enie Informatique et d'Ing'enierie de Production of the Ecole des Mines d'Al`es, Parc...

The main aim of this work is the development of a vision-based road detection system fast enough to cope with the difficult real-time constraints imposed by moving vehicle applications. The hardware platform, a special-purpose massively parallel system, has been chosen to minimize system production and operational costs. This paper presents a novel approach to expectation-driven low-level image segmentation, which can be mapped naturally onto mesh-connected massively parallel Simd architectures capable of handling hierarchical data structures. The input image is assumed to contain a distorted version of a given template; a multiresolution stretching process is used to reshape the original template in accordance with the acquired image content, minimizing a potential function. The distorted template is the process output. 1. Introduction The work discussed in this paper forms part of the Eureka Prometheus activities, aimed at improved road traffic safety. Since the processing of images...

In this paper, we present a method for creating watercolor-like animation, starting from video as input. The method involves two main steps: applying textures that simulate a watercolor appearance; and creating a simplified, abstracted version of the video to which the texturing operations are applied. Both of these steps are subject to highly visible temporal artifacts, so the primary technical contributions of the paper are extensions of previous methods for texturing and abstraction to provide temporal coherence when applied to video sequences. To maintain coherence for textures, we employ texture advection along lines of optical flow. We furthermore extend previous approaches by incorporating advection in both forward and reverse directions through the video, which allows for minimal texture distortion, particularly in areas of disocclusion that are otherwise highly problematic. To maintain coherence for abstraction, we employ mathematical morphology extended to the temporal domain, using filters whose temporal extents are locally controlled by the degree of distortions in the optical flow. Together, these techniques provide the first practical and robust approach for producing watercolor animations from video, which we demonstrate with a number of examples.