Showing papers by "Peter Meer published in 2010"

Robust and fast collaborative tracking with two stage sparse optimization

Abstract: The sparse representation has been widely used in many areas and utilized for visual tracking. Tracking with sparse representation is formulated as searching for samples with minimal reconstruction errors from learned template subspace. However, the computational cost makes it unsuitable to utilize high dimensional advanced features which are often important for robust tracking under dynamic environment. Based on the observations that a target can be reconstructed from several templates, and only some of the features with discriminative power are significant to separate the target from the background, we propose a novel online tracking algorithm with two stage sparse optimization to jointly minimize the target reconstruction error and maximize the discriminative power. As the target template and discriminative features usually have temporal and spatial relationship, dynamic group sparsity (DGS) is utilized in our algorithm. The proposed method is compared with three state-of-art trackers using five public challenging sequences, which exhibit appearance changes, heavy occlusions, and pose variations. Our algorithm is shown to outperform these methods.

Shape in Picture: Mathematical Description Of Shape In Grey-Level Images

Abstract: The Khalimsky Line as a Foundation for Digital Topology.- Topological Foundations of Shape Analysis.- A New Concept for Digital Geometry.- Theoretical Approaches to N-Dimensional Digital Objects.- On Boundaries and Boundary Crack-Codes of Multidimensional Digital Images.- Studying Shape Through Size Functions.- to Categorical Shape Theory, with Applications in Mathematical Morphology.- Shape Theory: an ANR-Sequence Approach.- Can Categorical Shape Theory Handle Grey-level Images?.- Mathematical Morphology as a Tool for Shape Description.- On Information Contained in the Erosion Curve.- Morphological Area Openings and Closings for Grey-scale Images.- Manifold Shape: from Differential Geometry to Mathematical Morphology.- On Negative Shape.- An Overview of the Theory and Applications of Wavelets.- Fractal Surfaces, Multiresolution Analyses, and Wavelet Transforms.- Interpolation in Multiscale Representations.- Discrete Stochastic Growth Models for Two-Dimensional Shapes.- Classical and Fuzzy Differential Methods in Shape Analysis.- Elements of a Fuzzy Geometry for Visual Space.- On the Relationship Between Surface Covariance and Differential Geometry.- Image Representation Using Affine Covariant Coordinates.- Equivariant Dynamical Systems: a Formal Model for the Generation of Arbitrary Shapes.- Neural Processing of Overlapping Shapes.- Contour Texture and Frame Curves for the Recognition of Non-Rigid Objects.- Conic Primitives for Projectively Invariant Representation of Planar Curves.- Blind Approximation of Planar Convex Shapes.- Recognition of Affine Planar Curves Using Geometric Properties.- Recognizing 3-D Curves from a Stereo Pair of Images: a Semi-differential Approach.- Statistical Shape Methodology in Image Analysis.- Recognition of Shapes from a Finite Series of Plane Figures.- Polygonal Harmonic Shape Characterization.- Shape Description and Classification Using the Interrelationship of Structures at Multiple Scales.- Learning Shape Classes.- Inference of Stochastic Graph Models for 2-D and 3-D Shapes.- Hierarchical Shape Analysis in Grey-level Images.- Irregular Curve Pyramids.- Multiresolution Shape Description by Corners.- Model-based Bottom-Up Grouping of Geometric Image Primitives.- Hierarchical Shape Representation for Image Analysis.- Scale-Space for N-dimensional Discrete Signals.- Scale-Space Behaviour and Invariance Properties of Differential Singularities.- Exploring the Shape Manifold: the Role of Conservation Laws.- Performance in Noise of a Diffusion-based Shape Descriptor.- Towards a Morphological Scale-Space Theory.- Geometry-based Image Segmentation Using Anisotropic Diffusion.- Images: Regular Tempered Distributions.- Local and Multilocal Scale-Space Description.- List of Authors.

Fast automatic detection of calcified coronary lesions in 3d cardiac CT images

Abstract: Even with the recent advances in multidetector computed tomography (MDCT) imaging techniques, detection of calcified coronary lesions remains a highly tedious task. Noise, blooming and motion artifacts etc. add to its complication. We propose a novel learning-based, fully automatic algorithm for detection of calcified lesions in contrast-enhanced CT data. We compare and evaluate the performance of two supervised learning methods. Both these methods use rotation invariant features that are extracted along the centerline of the coronary. Our approach is quite robust to the estimates of the centerline and works well in practice. We are able to achieve average detection times of 0.67 and 0.82 seconds per volume using the two methods.