A graph-spectral method for surface height recovery
نویسندگان
چکیده
منابع مشابه
A Graph-Spectral Method for Surface Height Recovery from Needle-maps
This paper describes a graph-spectral method for 3D surface integration. The algorithm takes as its input a 2D field of surface normal estimates, delivered, for instance, by a shape-from-shading or shape-from-texture procedure. We commence by using the surface normals to obtain an affinity weight matrix whose elements are related to the surface curvature. The weight matrix is used to compute a ...
متن کاملMirex2007 - Graph Spectral Method
We present a graph spectral approach in which melodies are represented as graphs, based on the intervals between the notes they are composed of. These graphs are then indexed into a database using their laplacian spectra as a feature vector. This laplacian spectrum is a good representative of the original melody. Consequently, range searching around the query spectrum returns similar melodies.
متن کاملA Graph-Spectral Approach to Surface Segmentation
In this paper we describe a graph-spectral method for 3D surface segmentation from 2D imagery. The method locates patches by finding groups of pixels that can be connected using a curvature minimising path. The path is the steady state Markov chain on transition probability matrix. We provide two methods for computing this matrix. The first uses information provided by the field of surface norm...
متن کاملNomonotone Spectral Gradient Method for Sparse Recovery
In the paper, we present an algorithm framework for the more general problem of minimizing the sum f(x) + ψ(x), where f is smooth and ψ is convex, but possible nonsmooth. At each step, the search direction of the algorithm is obtained by solving an optimization problem involving a quadratic term with diagonal Hessian and Barzilai-Borwein steplength plus ψ(x). The method with the nomonotone line...
متن کاملA MinMaxCut Spectral Method for Data Clustering and Graph Partitioning
The goal of data clustering can be formally stated as a min-max clustering principle: data points are grouped into clusters such that (a) between-cluster similarities are minimized while (b) within-cluster similarities are maximized. Existing methods typically focus on one of the above requirements. Here we propose a new method that emphasizes both requirements simultaneously. The MinMaxCut met...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition
سال: 2005
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2004.12.005