Chung-Nan Lee

Pose estimation from corresponding point data

Solutions for four different pose estimation problems are presented. Closed-form least-squares solutions are given to the overconstrained 2D-2D and 3D-3D pose estimation problems. A globally convergent iterative technique is given for the 2D-perspective-projection-3D pose estimation problem. A simplified linear solution and a robust solution to the 2D-perspective-projection-2D-perspective-projection pose-estimation problem are also given. Simulation experiments consisting of millions of trials with varying numbers of pairs of corresponding points and varying signal-to-noise ratios (SNRs) with either Gaussian or uniform noise provide data suggesting that accurate inference of rotation and translation with noisy data may require corresponding point data sets with hundreds of corresponding point pairs when the SNR is less than 40 dB. The experimental results also show that the robust technique can suppress the blunder data which come from outliers or mismatched points. >

Review and analysis of solutions of the three point perspective pose estimation problem

In this paper, the major direct solutions to the three point perspective pose estimation problems are reviewed from a unified perspective beginning with the first solution which was published in 1841 by a German mathematician, continuing through the solutions published in the German and then American photogrammetry literature, and most recently in the current computer vision literature. The numerical stability of these three point perspective solutions are also discussed. We show that even in case where the solution is not near the geometric unstable region, considerable care must be exercised in the calculation. Depending on the order of the substitutions utilized, the relative error can change over a thousand to one. This difference is due entirely to the way the calculations are performed and not due to any geometric structural instability of any problem instance. We present an analysis method which produces a numerically stable calculation.

Wireless Heterogeneous Transmitter Placement Using Multiobjective Variable-Length Genetic Algorithm

The problem of placing wireless transmitters to meet particular objectives, such as coverage and cost, has proven to be NP-hard. Furthermore, the heterogeneity of wireless networks makes the problem more intractable to deal with. This paper presents a novel multiobjective variable-length genetic algorithm to solve this problem. One does not need to determine the number of transmitters beforehand; the proposed algorithm simultaneously searches for the optimal number, types, and positions of heterogeneous transmitters by considering coverage, cost, capacity, and overlap. The proposed algorithm can achieve the optimal number of transmitters with coverage exceeding 98% on average for six benchmarks. These preferable experimental results demonstrate the high capability of the proposed algorithm for the wireless heterogeneous transmitter placement problem.

A chinese-character-stroke-extraction algorithm based on contour information

Abstract In this paper, we present a new stroke-extraction algorithm that integrates all levels of contour information including boundary points, dominant points, corner points, segments, cross-section-sequence graph and character structure to extract strokes of Chinese characters. In the algorithm, first, the boundary points are extracted, then the dominant and corner points are detected. Third, the character structure including singular and regular regions are extracted by the contour information and a modified cross-section-sequence graph (CSSG). Finally, a Bezier curve taking dominant points and corner points as inputs is used to check the continuity of strokes. Experimental results show that the proposed algorithm can correctly extract the strokes up to 95% from printed and handwritten test samples based on the human perception. Compared with a typical thinning approach, the proposed algorithm gives better results in terms of both stroke smoothness and the precise number of stroke extractions.

Primer design using genetic algorithm

MOTIVATION Before performing a polymerase chain reaction experiment, a pair of primers to clip the target DNA subsequence is required. However, this is a tedious task as too many constraints need to be satisfied. Various kinds of approaches for designing a primer have been proposed in the last few decades, but most of them do not have restriction sites on the designed primers and do not satisfy the specificity constraint. RESULTS The proposed algorithm imitates natures process of evolution and genetic operations on chromosomes in order to achieve optimal solutions, and is a best fit for DNA behavior. Experimental results indicate that the proposed algorithm can find a pair of primers that not only obeys the design properties but also has a specific restriction site and specificity. Gel electrophoresis verifies that the proposed method really can clip out the target sequence. AVAILABILITY A public version of the software is available on request from the authors.

On the harmonious mating strategy through tabu search

Genetic algorithms (GAs) are well-known heuristic algorithms and have been applied to solve a variety of complicated problems. When adopting GA approaches, two important issues--selection pressure and population diversity--must be considered. This work presents a novel mating strategy, called tabu genetic algorithm (TGA), which harmonizes these two issues by integrating tabu search (TS) into GAs selection. TGA incorporates the tabu list to prevent inbreeding so that population diversity can be maintained, and further utilizes the aspiration criterion to supply moderate selection pressure. An accompanied self-adaptive mutation method is also proposed to overcome the difficulty of determining mutation rate, which is sensitive to computing performance. The classic traveling salesman problem is used as a benchmark to validate the effectiveness of the proposed algorithm. Experimental results indicate that TGA can achieve harmony between population diversity and selection pressure. Comparisons with GA, TS, and hybrids of GA and TS further confirm the superiority of TGA in terms of both solution quality and convergence speed.

Supporting large-scale distributed simulation using HLA

This article describes the design of a Web-based environment to support large-scale distributed simulation using Java and IEEE standard P1516 high level architecture (HLA) framework and rules. Based on the run-time infrastructure (RTI) services within the HLA and Java application programmers interfaces (APIs) of the RTI, the proposed HLA-based environment provides an architectural foundation to enhance interactivity, portability, and interoperability for Web-based simulations. In addition, the proposed architectural design not only provides a client/server mechanism for simulation on the Web, but also supports a distributed federation execution over the network. A 3-level control mechanism (3LCM) was implemented to HLA-based middleware federateServer, in order to adaptively maintain information consistency and minimize message traffic for distributing information among client hosts and the federateServers. A dynamic filtering strategy (DFS), associated with the data distribution management (DDM) in the HLA RTI, is proposed to minimize false positive updates and enhance the filtering efficiency of subscription regions within an HLA federation. To verify the feasibility of this prototype, a distributed discrete event simulation application in Java was developed and performance of the proposed modeling design and Java RMIs distributed object model presented. From the experimental results, we show that the proposed environment based on HLA using 3LCM and DFS is workable and practical for supporting a large-scale distributed simulation.

A Near Optimal QoE-Driven Power Allocation Scheme for Scalable Video Transmissions Over MIMO Systems

The rapid increasing demands of wireless multimedia applications have boosted the developments of video delivery technologies with cross-layer designs, driven by optimizing quality of experiences (QoEs) of end users. In this paper, a near optimal power allocation scheme, targeting at maximizing QoE, is proposed for transmitting scalable video coding (SVC) based videos over multi-input multi-output (MIMO) systems. Both transmission errors in the physical (PHY) layer and video source coding characteristics in the application (APP) layer are jointly considered in the proposed scheme. A near optimal solution is achieved by decomposing the original optimization problem into several convex optimization sub-problems. Detailed algorithms with corresponding theoretical reasoning are provided. Since forward error corrections (FEC) techniques are widely implemented in modern wireless communication systems, the proposed scheme is further extended to the systems with Reed-Solomon (RS) code and a more practical approach with different modulation and coding schemes (MCSs). The near optimality of our proposed scheme, in terms of measured utilities, is shown by comparing with the exhaustive searched optimal solutions. Simulations with real H.264 SVC video traces demonstrate the effectiveness of our proposed scheme by comparing with other existing schemes in terms of well-accepted video quality assessment methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index.

Multi-parent extension of partially mapped crossover for combinatorial optimization problems

This paper proposes the multi-parent partially mapped crossover (MPPMX), which generalizes the partially mapped crossover (PMX) to a multi-parent crossover. The mapping list and legalization of PMX are modified to deal with the issues that arise from the increase of parents in PMX. Experimental results on five traveling salesman problems show that MPPMX significantly improves PMX by up to 13.95% in mean tour length. These preferable results not only demonstrate the advantage of the proposed MPPMX over PMX, but also confirm the merit of using more than two parents in crossover.

Cost Optimization of Elasticity Cloud Resource Subscription Policy

In cloud computing, resource subscription is an important procedure which enables customers to elastically subscribe to IT resources based on their service requirements. Resource subscription can be divided into two categories, namely long-term reservation and on-demand subscription. Although customers need to pay the upfront fee for a long-term reservation contract, the usage charge of reserved resources is generally much cheaper than that of the on-demand subscription. To provide a better Internet service by using cloud resource, service operators will expect to make a trade-off between the amount of long-term reserved resources and that of on-demand subscribed resources. Therefore, how to properly make resource provision plans is a challenging issue. In this paper, we present a two-phase algorithm for service operators to minimize their service provision cost. In the first phase, we propose a mathematical formulae to compute the optimal amount of long-term reserved resources. In the second phase, we use the Kalman filter to predict resource demand and adaptively change the subscribed on-demand resources such that provision cost could be minimized. We evaluated our solution by using real-world data. Our numerical results indicated that the proposed mechanisms are able to significantly reduce the provision cost.