Metrics, Metrics, Metrics: Negative Hedonicity

Intelligent technologies such as performance support systems and decision aids represent a key aspect of modern sociotechnical systems. When new tools are introduced into the workplace, they represent hypotheses about how cognitive work is expected to change. The tacit hypothesis is that any such change will be for the better, performance will be more efficient, and decisions will be improved - that is, they'll be made faster and on the basis of greater evidence. Experience suggests that technological interventions sometimes have the intended positive effect. However, they often result in negative effects, including unintended cascading failures and worker frustration due to "user-hostile" aspects of interfaces.     

Geodesic Active Contours

A novel scheme for the detection of object boundaries is presented. The technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours naturally split and merge, allowing the simultaneous detection of several objects and both interior and exterior boundaries. The proposed approach is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve lays in a Riemannian space whose metric is defined by the image content. This geodesic approach for object segmentation allows to connect classical snakes based on energy minimization and geometric active contours based on the theory of curve evolution. Previous models of geometric active contours are improved, allowing stable boundary detection when their gradients suffer from large variations, including gaps. Formal results concerning existence, uniqueness, stability, and correctness of the evolution are presented as well. The scheme was implemented using an efficient algorithm for curve evolution. Experimental results of applying the scheme to real images including objects with holes and medical data imagery demonstrate its power. The results may be extended to 3D object segmentation as well.     

Robust Geodesic Regression

International Journal of Computer Vision - This paper studies robust regression for data on Riemannian manifolds. Geodesic regression is the generalization of linear regression to a setting with a...     

Geodesic Order Types

The geodesic between two points a and b in the interior of a simple polygon P is the shortest polygonal path inside P that connects a to b. It is thus the natural generalization of straight line segments on unconstrained point sets to polygonal environments. In this paper we use this extension to generalize the concept of the order type of a set of points in the Euclidean plane to geodesic order types. In particular, we show that, for any set S of points and an ordered subset \(\mathcal {B} \subseteq S\) of at least four points, one can always construct a polygon P such that the points of \(\mathcal {B} \) define the geodesic hull of S w.r.t. P, in the specified order. Moreover, we show that an abstract order type derived from the dual of the Pappus arrangement can be realized as a geodesic order type.     

Geodesic pancyclicity of twisted cubes

The hypercube is one of the most popular interconnection networks since it has simple structure and is easy to implement. An n-dimensional twisted cube, TQ_n, is an important variation of the hypercube Q_n and preserves many of its desirable properties. The problem of embedding linear arrays and cycles into a host graph has attracted substantial attention in recent years. The geodesic cycle embedding problem is for any two distinct vertices, to find all the possible lengths of cycles that include a shortest path joining them. In this paper, we prove that TQ_n is geodesic 2-pancyclic for each odd integer n ? 3. This result implies that TQ_n is edge-pancyclic for each odd integer n ? 3. Moreover, TQ_n × K₂ is also demonstrated to be geodesic 4-pancyclic.     

Globally Optimal Geodesic Active Contours

An approach to optimal object segmentation in the geodesic active contour framework is presented with application to automated image segmentation. The new segmentation scheme seeks the geodesic active contour of globally minimal energy under the sole restriction that it contains a specified internal point p_int. This internal point selects the object of interest and may be used as the only input parameter to yield a highly automated segmentation scheme. The image to be segmented is represented as a Riemannian space S with an associated metric induced by the image. The metric is an isotropic and decreasing function of the local image gradient at each point in the image, encoding the local homogeneity of image features. Optimal segmentations are then the closed geodesics which partition the object from the background with minimal similarity across the partitioning. An efficient algorithm is presented for the computation of globally optimal segmentations and applied to cell microscopy, x-ray, magnetic resonance and cDNA microarray images.Ben Appleton received degrees in engineering and in science from the University of Queensland in 2001 and was awarded a university medal. In 2002 he began a Ph.D at the University of Queensland in the field of image analysis. He is supported by an Australian Postgraduate Award and the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Mathematical and Information Sciences. He has been a teaching assistant in image analysis at the University of Queensland since 2001. He has also contributed 10 research papers to international journals and conferences and was recently awarded the prize for Best Student Paper at Digital Image Computing: Techniques and Applications. His research interests include image segmentation, stereo vision and algorithms.Hugues Talbot received the engineering degree from École Centrale de Paris in 1989, the D.E.A. (Masters) from University Paris VI in 1990 and the Ph.D from École des Mines de Paris in 1993, under the guidance of Dominique Jeulin and Jean Serra. He has been affiliated with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Mathematical and Information Sciences since 1994. He has worked on numerous applied projects in relation with industry, he has contributed more than 30 research papers in international journals and conferences and he has co-edited two sets of international conference proceedings on image analysis. He now also teaches image processing at the University of Sydney, and his research interest include image segmentation, linear structure analysis, texture analysis and algorithms.     

Geodesic Matching with Free Extremities

In this paper, we describe how to use geodesic energies defined on various sets of objects to solve several distance related problems. We first present the theory of metamorphoses and the geodesic distances it induces on a Riemannian manifold, followed by classical applications in landmark and image matching. We then explain how to use the geodesic distance for new issues, which can be embedded in a general framework of matching with free extremities. This is illustrated by results on image and shape averaging and unlabeled landmark matching. Laurent Garcin is a former student of the Ecole Polytechnique. He obtained his Ph.D. in 2004 at the Ecole Normale de Cachan, working on matching methods for landmarks and images. He is an engineer at the French National Geographic Institute. Laurent Younes is a former student of the Ecole Normale Superieure in Paris. He was awarded the Ph.D. from the University Paris Sud in 1989, and the thesis advisor certification from the same university in 1995. He works on the statistical analysis of images and shapes, and on modeling shape deformations and shape spaces. Laurent Younes entered CNRS, the French National Research Center in October 1991, in which he has been a “Directeur de Recherche" until 2003. He is now a professor at the Department of Applied Mathematics and Statistics Department and the Center for Imaging Science at Johns Hopkins University in July 2003.     

Structure-Sensitive Superpixels via Geodesic Distance

Segmenting images into superpixels as supporting regions for feature vectors and primitives to reduce computational complexity has been commonly used as a fundamental step in various image analysis and computer vision tasks. In this paper, we describe the structure-sensitive superpixel technique by exploiting Lloyd’s algorithm with the geodesic distance. Our method generates smaller superpixels to achieve relatively low under-segmentation in structure-dense regions with high intensity or color variation, and produces larger segments to increase computational efficiency in structure-sparse regions with homogeneous appearance. We adopt geometric flows to compute geodesic distances amongst pixels. In the segmentation procedure, the density of over-segments is automatically adjusted through iteratively optimizing an energy functional that embeds color homogeneity, structure density. Comparative experiments with the Berkeley database show that the proposed algorithm outperforms the prior arts while offering a comparable computational efficiency as TurboPixels. Further applications in image compression, object closure extraction and video segmentation demonstrate the effective extensions of our approach.     

Geodesic Warps by Conformal Mappings

In recent years there has been considerable interest in methods for diffeomorphic warping of images, with applications in e.g. medical imaging and evolutionary biology. The original work generally cited is that of the evolutionary biologist D’Arcy Wentworth Thompson, who demonstrated warps to deform images of one species into another. However, unlike the deformations in modern methods, which are drawn from the full set of diffeomorphisms, he deliberately chose lower-dimensional sets of transformations, such as planar conformal mappings. In this paper we study warps composed of such conformal mappings. The approach is to equip the infinite dimensional manifold of conformal embeddings with a Riemannian metric, and then use the corresponding geodesic equation in order to obtain diffeomorphic warps. After deriving the geodesic equation, a numerical discretisation method is developed. Several examples of geodesic warps are then given. We also show that the equation admits totally geodesic solutions corresponding to scaling and translation, but not to affine transformations.     

测地B样条曲线的生成

本文首先提出流形网格上的de Boor算法。此算法可用于生成网格模型上的B样条曲线。测地B样条曲线适用于网格模型上自由曲线的设计。其次,证明了测地B样条曲线的一些重要性质,如凸包性,局部调整性和保凸性等性质。最后,在Visual C++6.0语言环境下,利用OpenGL图形库给出网格模型上B样条曲线生成的一些实例。实验结果表明,本文提出的算法正确、稳定、快速且容易实现,具有较好的仿真效果。     

Geodesic Shooting for Computational Anatomy

Studying large deformations with a Riemannian approach has been an efficient point of view to generate metrics between deformable objects, and to provide accurate, non ambiguous and smooth matchings between images. In this paper, we study the geodesics of such large deformation diffeomorphisms, and more precisely, introduce a fundamental property that they satisfy, namely the conservation of momentum. This property allows us to generate and store complex deformations with the help of one initial “momentum” which serves as the initial state of a differential equation in the group of diffeomorphisms. Moreover, it is shown that this momentum can be also used for describing a deformation of given visual structures, like points, contours or images, and that, it has the same dimension as the described object, as a consequence of the normal momentum constraint we introduce.     

Geodesic Remeshing Using Front Propagation

In this paper, we propose a complete framework for 3D geometry modeling and processing that uses only fast geodesic computations. The basic building block for these techniques is a novel greedy algorithm to perform a uniform or adaptive remeshing of a triangulated surface. Our other contributions include a parameterization scheme based on barycentric coordinates, an intrinsic algorithm for computing geodesic centroidal tessellations, and a fast and robust method to flatten a genus-0 surface patch. On large meshes (more than 500,000 vertices), our techniques speed up computation by over one order of magnitude in comparison to classical remeshing and parameterization methods. Our methods are easy to implement and do not need multilevel solvers to handle complex models that may contain poorly shaped triangles.     

程序复杂性度量

本文力图阐明程序复杂性度量的有关定义研究内容、技术和方法，并探讨它用软件产品的质量、交付的时间和费用的关系以及它在软件开发中的作用。     

Useful Cybersecurity Metrics

Measuring cybersecurity is difficult, but other disciplines can offer important lessons and techniques for building a system that can help test hypotheses about system security.     

基于测地线距离的广义高斯型Laplacian 特征映射

传统的Laplacian 特征映射是基于欧氏距离的近邻数据点的保持,近邻的高维数据点映射到内在低维空间后仍为近邻点,高维数据点的近邻选取最终将影响全局低维坐标.将测地线距离和广义高斯函数融合到传统的Laplacian 特征映射算法中,首先提出了一种基于测地线距离的广义高斯型Laplacian 特征映射算法(geodesicdistance-based generalized Gaussian LE,简称GGLE),该算法在用不同的广义高斯函数度量高维数据点间的相似度时,获得的全局低维坐标呈现出不同的聚类特性;然后,利用这种特性进一步提出了它的集成判别算法,该集成判别算法的主要优点是:近邻参数K 固定,邻接图和测地线距离矩阵都只构造一次.在木纹数据集上的识别实验结果表明,这是一种有效的基于流形的集成判别算法.     

基于测地距离的支持向量机分类算法

When dealing with pattern recognition problems one encounters different types of prior knowledge. It is important to incorporate such knowledge into classification method at hand. A very common type of prior knowledge is many data sets are on some kinds of manifolds. Distance based classification methods can make use of this by a modified distance measure called geodesic distance. We introduce a new kind of kernels for support vector machines which incorporate geodesic distance and therefore are applicable in cases such transformation invariance is known. Experiments results show that the performance of our method is comparable to that of other state-of-the-art method.