9354577
Identifying Faces in a 2D Line Drawing Representing a Manifold Object
A straightforward way to illustrate a 3D model is to use a line drawing. Faces in a 2D line drawing provide important information for reconstructing its 3D geometry. Manifold objects belong to a class of common solids and most solid systems are based on manifold geometry. In this paper, a new method is proposed for finding faces from single 2D line drawings representing manifolds. The face identification is formulated based on a property of manifolds: each edge of a manifold is shared exactly by two faces. The two main steps in our method are (1) searching for cycles from a line drawing and (2) searching for faces from the cycles. In order to speed up the face identification procedure, a number of properties, most of which relate to planar manifold geometry in line drawings, are presented to identify most of the cycles that are or are not real faces in a drawing, thus reducing the number of unknown cycles in the second searching. Schemes to deal with manifolds with curved faces and manifolds each represented by two or more disjoint graphs are also proposed. The experimental results show that our method can handle manifolds previous methods can handle, as well as those they cannot.
2027823
Evolutionary Search for Faces from Line Drawings
Single 2D line drawing is a straightforward method to illustrate 3D objects. The faces of an object depicted by a line drawing give very useful information for the reconstruction of its 3D geometry. Two recently proposed methods for face identification from line drawings are based on two steps: finding a set of circuits that may be faces and searching for real faces from the set according to some criteria. The two steps, however, involve two combinatorial problems. The number of the circuits generated in the first step grows exponentially with the number of edges of a line drawing. These circuits are then used as the input to the second combinatorial search step. When dealing with objects having more faces, the combinatorial explosion prevents these methods from finding solutions within feasible time. This paper proposes a new method to tackle the face identification problem by a variable-length genetic algorithm with a novel heuristic and geometric constraints incorporated for local search. The hybrid GA solves the two combinatorial problems simultaneously. Experimental results show that our algorithm can find the faces of a line drawing having more than 30 faces much more efficiently. In addition, simulated annealing for solving the face identification problem is also implemented for comparison.
18277516
Mesh Optimisation Using Edge Information in Feature-Based Surface Reconstruction
One of the most challenging and fundamental problems in computer vision is to reconstruct a surface model given a set of uncalibrated 2D images. Well-established Structure from Motion (SfM) algorithms often result in a sparse set of 3D surface points, but surface modelling based on sparse 3D points is not easy. In this paper, we present a new method to refine and optimise surface meshes using edge information in the 2D images. We design a meshing – edge point detection – re-meshing scheme that can gradually refine the surface mesh until it best fits the true physical surface of the object being modelled. Our method is tested on real images and satisfactory results are obtained.
13217169
New Method for Constructing Isospectral Graphs
There exist several methods available to construct isospectral molecular graphs.l-1° Recently, M. RandiC and his co-workers proposed the ring enlargement process (vide infra) for constructing subspectral graphs.” This method can be also used to construct isospectral graphs; however, it may fail sometimes. For example, the isospectral pair 1, 2 can be enlarged to the isospectral graphs 3,4 shown in Figure 1, where the symbol ‘‘z” signifies an isospectral pair and “-” denotes the ring enlargement procedure. Eigenvalues of 3, 4 are divided into two parts, those in coincidence with that of 1 , 2 are listed on the left, and the remaining ones are put on the right. On the other hand, graph pairs 7, 8 and 9, 10 in Figure 2, yielded from isospectral pair 5, 6, are not isospectral. Noticing the difference of the spectrum of graphs XX, XX’ (by “XX, XX”’ we mean that they are generated from subgraph X in double size by the enlargement procedure, such as 7, 8 from 5; and 9, 10 from a), RandiC suggested that if XX, XX’ are bipartite, they should be isospectral, because all eigenvalues now occur in pairs and the difference between XX and XX’ disappears. Hence, he proposed a conjecture below. CONJECTURE: If graph X yields nonisomorphic bipartite enlargements XX, XX’, etc.; the derived graphs are not only superspectral to the parent graph X but are also mutually cospectral.
28349358
Data Parallel Program Design
We have shown how graphical languages such as CODE/ROPE and PPSE can be used to design SIMD or data parallel programs. The advantages of this approach are machine independence, design clarity, automated program analysis, and accelerated software development. The disadvantages are that many problems remain in this approach such as how to reduce design clutter, how to automate optimal processor mapping, and how to perform messagepassing optimization. More work is needed before this approach can be used in the design of large-scale applications, but we believe the approach is promising. The main contribution is to show how the simple ideas of stencils, stream generators, and replicators can be used effectively to extend the classical dataflow design paradigm into the data parallel design paradigm. While more work is needed, these three ideas lead to greater expressiveness of design. The PPSE toolset currently does much of the mapping, scheduling, and automatic code generation described in this paper. However, PPSE does not currently handle data parallel programming. Work is progressing toward a full implementation of these ideas. Even so, PPSE has been invaluable for performing a variety of what-if analyses on parallel programs. Insights have been gained with this approach that would not be possible with a purely textual representation of the parallel program.
9690577
Automatic Camera Calibration and Scene Reconstruction with Scale-Invariant Features
The goal of our research is to robustly reconstruct general 3D scenes from 2D images, with application to automatic model generation in computer graphics and virtual reality. In this paper we aim at producing relatively dense and well-distributed 3D points which can subsequently be used to reconstruct the scene structure. We present novel camera calibration and scene reconstruction using scale-invariant feature points. A generic high-dimensional vector matching scheme is proposed to enhance the efficiency and reduce the computational cost while finding feature correspondences. A framework for structure and motion is also presented that better exploits the advantages of scale-invariant features. In this approach we solve the “phantom points” problem and this greatly reduces the possibility of error propagation. The whole process requires no information other than the input images. The results illustrate that our system is capable of producing accurate scene structure and realistic 3D models within a few minutes.
6396851
Multiagent diffusion and distributed optimization
Distributed problem solving by a multiagent system represents a promising approach to solving complex computational problems. However, many multiagent systems require certain degree of planning, coordination and negotiation to achieve the given goal. This paper presents a multiagent framework for tackling global optimization tasks inspired by diffusion in nature. The framework is designed for situations where agent communication must be kept to a minimal. Hence, complicated coordination and negotiation is not possible. Distributed agents in this framework share the common goal of finding the global optimal solution. They cooperate to achieve this common goal by sharing and updating a common belief that captures their estimation of the whereabouts of the optimal solution. To facilitate this, agents are naturally organized in families with a parent and its offsprings as members. This paper also presents an algorithm called Evolutionary Diffusion Optimization, which is implemented base on the proposed agent framework. Experimental results on some benchmark problems are presented together with performance comparison with a simulated annealing algorithm.