8845811
Contextual Inference in Contour-Based Stereo Correspondence
Standard approaches to stereo correspondence have difficulty when scene structure does not lie in or near the frontal parallel plane, in part because an orientation disparity as well as a positional disparity is introduced. We propose a correspondence algorithm based on differential geometry, that takes explicit advantage of both disparities. The algorithm relates the 2D differential structure (position, tangent, and curvature) of curves in the left and right images to the Frenet approximation of the (3D) space curve. A compatibility function is defined via transport of the Frenet frames, and they are matched by relaxing this compatibility function on overlapping neighborhoods along the curve. The remaining false matches are concurrently eliminated by a model of “near” and “far” neurons derived from neurobiology. Examples on scenes with complex 3D structures are provided.
1481458
Geometry of contour-based correspondence for stereo
We consider the problem of computing stereo correspondence for scenes dense in physical structures, such as a bouquet of flowers. Boundaries are the space curves, formally governed by the projective geometry induced by the stereo camera configuration and by the differential structure of curves in the scene. We propose that the correspondence problem can be solved by relating the differential structure in the left and right image planes to the geometry of curves in space. Based on the Frenet equations, we show that the compatibility between pairs of corresponding points, and tangents and curvatures at those points, is related to the local approximation of a space curve using an osculating helix. A relaxation labeling network demonstrates that the compatibilities can be used to infer appropriate correspondences in a scene. Examples are shown.
2186483
Improvements for the Symbolic Verification of Timed Automata
Based on the equivalence relation for location based reachability between continuous and integer semantics of closed timed automata, Beyer et al. have implemented the verifier Rabbit, with the uniform representation of reachable configurations. However, the growth of maximal constant of clock variables will decline the performance of Rabbit. The paper proposes an improved symbolic method, using binary decision diagrams (BDDs) to store the symbolic representation of discretized states, for the verification of timed systems. Compared with Rabbit, experiments demonstrate that besides the memory reduction, our implementation is also less sensitive to the size of clock domain.
109807
One table stores all: Enabling painless free-and-easy data publishing and sharing
In this paper, we present a free-and-easy data publishing and sharing system based on folksonomy. The system accepts data objects described with user-created metadata, called data units. The system supports flexible structure on the data units, and places no restrictions on the vocabulary used. We devise a generic table model for storing and representing the data units of various structures. We propose a framework for managing the data units and providing browsing, searching and querying services over them. We present our current approaches and discuss relevant research issues.
26295637
A Linear-Time Algorithm for Computing Translocation Distance between Signed Genomes
The study of evolution based on rearrangements leads to a rearrangement distance problem, i.e., computing the minimum number of rearrangement events required to transform one geonome to another. In this paper we study the translocation distance problem, modeling the evolution of genomes by translocations. We present a linear-time algorithm for computing the translocation distance between signed genomes in this paper, improving a previous O(n 3) bound by Hannenhalli in 1996.
9386148
Orthogonal factorizations of graphs
Let G be a graph with vertex set V(G) and edge set E(G), and let g and f be two nonnegative integer-valued functions defined on V(G) such that g(x) ≤ f(x) for every vertex x of V(G). We use dG(x) to denote the degree of a vertex x of G. A graph G is called a (g,f)-graph if g(x) ≤ dG(x) ≤ f(x) for each x ∈ V(G). Then a spanning subgraph F of G is said to be a (g,f)-factor of G if F itself is a (g,f)-graph. A (g,f)-factorization of G is a partition of E(G) into edge disjoint (g,f)-factors. Let F = {F1,F2,... ,Fm} be a factorization of G and H be a subgraph of G with m edges. If Fi, 1 ≤ i ≤ m, has exactly one edge in common with H, we say that F is orthogonal to H. In this paper it is proved that every (mg + k,mf - k)-graph contains a subgraph R such that R has a (g,f)-factorization orthogonal to a given subgraph with k edges, where m and k are positive integers with 1 ≤ k > m and g(x) ≥ 0. This result has been conjectured by Yan in Yan (Sci. China Ser. A 41(1) (1998) 48).
664043
Evolutionary structure learning algorithm for Bayesian network and Penalized Mutual Information metric
The paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and an evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.