16633922
Detecting irregularity in videos using kernel estimation and KD trees
Automatic event understanding is the ultimate goal for many visual surveillance systems. In this paper, we propose a novel approach for on-line detecting unusual human activities in videos without the need to explicitly define all valid configurations. Within the framework of Bayesian inference, the detection process is formulated as an MAP estimation where we attempt to find whether activities in new video segments have similar activities in a video database. Our approach has three contributions: firstly, we build the statistical representation of normal behaviors in the database using nonparametric kernel density estimation; secondly, local feature descriptors are highly compressed using PCA and stored in a K-D tree structure, making the search for behavior-based similarity fast and effective; thirdly, the K-D trees are used to generate multiple hypotheses which compete for the optimal classification. The approach requires no tracking, no explicit motion estimation, and no predefined class-based templates. Experimental results have validated our approach in many real-world video sequences.
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.
16004119
A Graph-Based Method for Face Identification from a Single 2D Line Drawing
The faces in a 2D fine drawing of an object provide important information for the reconstruction of its 3D geometry. In this paper, a graph-based optimization method is proposed for identifying the faces is a line drawing. The face identification is formulated as a maximum weight clique problem. This formulation is proven to be equivalent to the formulation proposed by Shpitalni and Upson (1996). The advantage of our formulation is that it enables one to develop a much faster algorithm to find the faces in a drawing. The significant improvement in speed is derived from two algorithms provided: the depth-first graph search for quickly generating possible faces from a drawing; and the maximum weight clique finding for obtaining the optimal face configurations of the drawing. The experimental results shown that our algorithm generates the same results of face identification as Shpitalni and Lipson's method, but is much faster when dealing with objects of more than 20 faces.
2194421
Degen Generalized Cylinders and Their Properties
Generalized cylinder (GC) has played an important role in computer vision since it was introduced in the 1970s. While studying GC models in human visual perception of shapes from contours, Marr assumed that GC's limbs are planar curves. Later, Koenderink and Ponce pointed out that this assumption does not hold in general by giving some examples. In this paper, we show that straight homogeneous generalized cylinders (SHGCs) and tori (a kind of curved GCs) have planar limbs when viewed from points on specific straight lines. This property leads us to the definition and investigation of a new class of GCs, with the help of the surface model proposed by Degen for geometric modeling. We call them Degen generalized cylinders (DGCs), which include SHGCs, tori, quadrics, cyclides, and more other GCs into one model. Our rigorous discussion is based on projective geometry and homogeneous coordinates. We present some invariant properties of DGCs that reveal the relations among the planar limbs, axes, and contours of DGCs. These properties are useful for recovering DGC descriptions from image contours as well as for some other tasks in computer vision.
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.
6371621
3D object reconstruction from a single 2D line drawing without hidden lines
The human vision system can interpret a single 2D line drawing as a 3D object without much difficulty even if the hidden lines of the object are invisible. Several reconstruction approaches have tried to emulate this ability, but they cannot recover the complete object if the hidden lines of the object are not shown. This paper proposes a novel approach for reconstructing complete 3D objects from line drawings without hidden lines. First, we develop some constraints and properties for the inference of the topology of the invisible edges and vertices of an object. Then we present a reconstruction method based on perceptual symmetry and planarity of the object. We give a number of examples to demonstrate the ability of our approach.
17801993
Efficiency of emergent constraint satisfaction in small-world and random agent networks
In this paper, we study the efficiency of emergent constraint satisfaction in small-world and random agent networks. We find that emergent constraint satisfaction in a small-world network is less efficient than in some other networks (e.g., regular networks). Further, we find that this finding holds in almost all random networks. Based on these observations, we study the relationship between the efficiency of emergent constraint satisfaction and the randomness of the corresponding network from which the constraint satisfaction problem is generated.
14178256
Distributed Problem Solving Without Communication - An Examination of Computationally Hard Satisfiability Problems
In this paper, we extend and modify the ERA approach proposed in Ref. 13 to solve Propositional Satisfiability Problems (SATs). The new ERA approach involves a multiagent system where each agent only senses its local environment and applies some self-organizing rules for governing its movements. The environment, which is a two-dimensional cellular environment, records and updates the local values that are computed and affected according to the movements of individual agents. In solving a SAT with the ERA approach, we first divide variables into several groups, and represent each variable group with an agent whose possible positions correspond to the elements in a Cartesian product of variable domains, and then randomly place each agent onto one of its possible positions. Thereafter, the ERA system will keep on dispatching agents to choose their movements until an exact or approximate solution emerges. The experimental results on some benchmark SAT test-sets have shown that the ERA approach can obtain comparable results as well as stable performances for SAT problems. In particular, it can find approximate solutions for SAT problems in only a few steps. The real value of this approach is that it is a distributed asynchronous approach without any centralized control or evaluation, where the agents can cooperate to solve problems without explicit communication.
36178651
The vertex linear arboricity of distance graphs
A distance graph is a graph G(R,D) with the set of all points of the real line as vertex set and two vertices u,[email protected]?R are adjacent if and only if |u-v|@?D where the distance set D is a subset of the positive real numbers. Here, the vertex linear arboricity of G(R,D) is determined when D is an interval between 1 and @d. In particular, the vertex linear arboricity of integer distance graphs G(D) is discussed, too.
29539267
Energy Optimization of Distributed Embedded Processors by Combined Data Compression and Functional Partitioning
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.