A robust global and local mixture distance based non-rigid point set registration

We present a robust global and local mixture distance (GLMD) based non-rigid point set registration method which consists of an alternating two-step process: correspondence estimation and transformation updating. We first define two distance features for measuring global and local structural differences between two point sets, respectively. The two distances are then combined to form a GLMD based cost matrix which provides a flexible way to estimate correspondences by minimizing global or local structural differences using a linear assignment solution. To improve the correspondence estimation and enhance the interaction between the two steps, an annealing scheme is designed to gradually change the cost minimization from local to global and the thin plate spline transformation from rigid to non-rigid during registration. We test the performance of our method in contour registration, sequence images and real images, and compare with six state-of-the-art methods where our method shows the best alignments in most scenarios.     

A new measure using intuitionistic fuzzy set theory and its application to edge detection

In this paper, a new attempt has been made using Attanassov's intuitionistic fuzzy set theory for image edge detection. Intuitionistic fuzzy set takes into account the uncertainty in assignment of membership degree known as hesitation degree. Also a new distance measure, called intuitionistic fuzzy divergence, has been proposed. With this proposed distance measure, edge detection is carried out, and the results are found better with respect to the previous methods.     

A new vector field distance transform and its application to mesh processing from 3D scanned data

In this paper we define a new 3D vector field distance transform to implicitly represent a mesh surface. We show that this new representation is more accurate than the classic scalar field distance transform by comparing both representations with an error metric evaluation. The widely used marching cube triangulation algorithm is adapted to the new vector field distance transform to correctly reconstruct the resulting explicit surface. In the reconstruction process of 3D scanned data, the useful mesh denoising operation is extended to the new vector field representation, which enables adaptive and selective filtering features. Results show that mesh processing with this new vector field representation is more accurate than with the scalar field distance transform and that it outperforms previous mesh filtering algorithms. Future work is discussed to extend this new vector field representation to other mesh useful operations and applications.     

Grammatical evolution of shape and its application to structural shape optimisation

We propose an automated shape generative framework, which provides an alternative way of exploring the design space in a structural mechanics context. The framework presented uses “blind” evolutionary intelligence to synthesise shape grammar sentences i.e. Grammatical Evolution (GE), where rules are selected by a Genetic Algorithm (GA). This is a novel approach to automate the Shape Grammar (SG) formalism. We then present an application of a grammar based shape generative framework to solve a 2D design optimisation problem. This involves synthesis of parametric 2D curves where the shape grammar primitives are introduced as arcs represented by rotation and a radius. The efficacy of the proposed shape generative framework is then compared with that of Non-Uniform Rational B-Splines (NURBS) parametrisation for structural optimisation.     

The angular difference function and its application to image registration

The estimation of large motions without prior knowledge is an important problem in image registration. In this paper, we present the angular difference function (ADF) and demonstrate its applicability to rotation estimation. The ADF of two functions is defined as the integral of their spectral difference along the radial direction. It is efficiently computed using the pseudopolar Fourier transform, which computes the discrete Fourier transform of an image on a near spherical grid. Unlike other Fourier-based registration schemes, the suggested approach does not require any interpolation. Thus, it is more accurate and significantly faster.     

A formal library of set relations and its application to synchronous languages

Set relations are particularly suitable for specifying the small-step operational semantics of synchronous languages. In this paper, a formal library of set relations for the definition, verification of properties, and execution of binary set relations is presented. The formal library consists of a set of theories written in the Prototype Verification System (PVS) that contains definitions and proofs of properties, such as determinism and compositionality, for synchronous relations. The paper also proposes a serialization procedure that enables the simulation of synchronous set relations via set rewriting systems. The library and the serialization procedure are illustrated with the rewriting logic semantics of the Plan Execution Interchange Language (PLEXIL), a rich synchronous plan execution language developed by NASA to support autonomous spacecraft operations.     

Fuzzy reasoning based on a new fuzzy rough set and its application to scheduling problems

In this paper, we study the fuzzy reasoning based on a new fuzzy rough set. First, we define a broad family of new lower and upper approximation operators of fuzzy sets between different universes using a set of axioms. Then, based on the approximation operators above, we propose the fuzzy reasoning based on the new fuzzy rough set. By means of the above fuzzy reasoning based on the new fuzzy rough set, for a given premise, we can obtain the fuzzy reasoning consequence expressed by the fuzzy interval constructed by the above two approximations of fuzzy sets. Furthermore, through the defuzzification of the lower and upper approximations, we can get the corresponding two values constructing the interval used as the fuzzy reasoning consequence after defuzzification. Then, from the above interval, a suitable value can be selected as the final reasoning consequence so that some special constraints are satisfied as possibly. At last, we apply the fuzzy reasoning based on the new fuzzy rough set to the scheduling problems, and numerical computational results show that the fuzzy reasoning based on the new fuzzy rough set is more suitable for the scheduling problems compared with the fuzzy reasoning based on the CRI method and the III method.     

The asymmetry of image registration and its application to face tracking

Most image registration problems are formulated in an asymmetric fashion. Given a pair of images, one is implicitly or explicitly regarded as a template and warped onto the other to match as well as possible. In this paper, we focus on this seemingly arbitrary choice of the roles and reveal how it may lead to biased warp estimates in the presence of relative scaling. We present a principled way of selecting the template and explain why only the correct asymmetric form, with the potential inclusion of a blurring step, can yield an unbiased estimator. We validate our analysis in the domain of model-based face tracking. We show how the usual active appearance model (AAM) formulation overlooks the asymmetry issue, causing the fitting accuracy to degrade quickly when the observed objects are smaller than their model. We formulate a novel, "resolution-aware fitting" (RAF) algorithm that respects the asymmetry and incorporates an explicit model of the blur caused by the camera's sensing elements into the fitting formulation. We compare the RAF algorithm against a state-of-the-art tracker across a variety of resolutions and AAM complexity levels. Experimental results show that RAF significantly improves the estimation accuracy of both shape and appearance parameters when fitting to low-resolution data. Recognizing and accounting for the asymmetry of image registration leads to tangible accuracy improvements in analyzing low-resolution imagery     

A type-2 linguistic set theory and its application to multi-criteria decision making

Fuzzy set theory (FST), since its introduction in the 1960s, has been continuously developed. Theory development for FST is highly challenging. For instance, various researchers have devoted substantive efforts in developing methodologies for many fundamental tasks, such as ranking type-1 fuzzy numbers, defining arithmetic and logical operations on type-1 and type-2 fuzzy numbers, and defining correlation measure on type-1 and type-2 numbers, resulting in a multitude of approaches, many of which based on differing postulates and assumptions. On the other hand, by interpreting the membership function of a linguistic concept based on a probabilistic framework, and by abandoning Zadeh’s extension principle in favor of relying on probabilistic arguments, many of the technical difficulties in developing theory involving “type-1” like linguistic concepts and variables can be bypassed, with the resulting probabilistic linguistic framework enabling a uniform approach for theory development for a wide range of elementary operations and measures. In this article, the probabilistic linguistic framework is extended to type-2 linguistic sets that allows, with as few postulates as possible, uniform approach for developing methodologies for fundamental tasks such as taking the union and intersection of and performing arithmetic operations on type-2 linguistic numbers. Furthermore, we demonstrate the resulting methodology by applying it to an industrial data set concerning multi-criteria decision making.     

S-粗Vague集合及应用

在原始Vague集基础上提出动态的Vague集,即Ｓ-Vague集,由此提出了Ｓ-粗Vague集概念。接着给出了Ｓ-粗Vague集结构与性质,分析了Ｓ-粗Vague集与Z.Pawlak粗集、Dubois粗糙模糊集以及S-粗集之间的关系,给出了Ｓ-粗Vague集的应用。     

A new approach to code motion and its application to hoisting

An approach to code motion and hoisting, a program optimization technique, is discussed. The safety and profitability of optimization in general and hoisting in particular are analyzed. By restricting the analysis to a spanning tree imposed on the program graph, a linear algorithm is developed that provides sufficient but not necessary conditions for hoisting.     

佳点集遗传算法及其在PID控制中的应用*

传统遗传算法具有收敛速度慢、局部寻优能力较差且容易出现“早熟”等现象,运用数论中的佳点集理论与方法对其进行改进。改进的算法通过生成一种具有良好多样性的均匀初始种群,构造出新型的佳点交叉算子和变异算子,使得在进化过程中子代可以更好地继承父代的优良特性,有效地改善了传统算法的全局收敛速度,避免了“早熟”现象的发生。将此改进算法应用于工业PID控制器的参数寻优,仿真结果显示出佳点集遗传算法明显提高了搜索速度,系统的动态性能和稳定性也有明显改善。     

A new probabilistic relaxation scheme and its application to edgedetection

This paper presents a new scheme for probabilistic relaxation labeling that consists of an update function and a dictionary construction method. The nonlinear update function is derived from Markov random field theory and Bayes' formula. The method combines evidence from neighboring label assignments and eliminates label ambiguity efficiently. This result is important for a variety of image processing tasks, such as image restoration, edge enhancement, edge detection, pixel classification, and image segmentation. The authors successfully applied this method to edge detection. The relaxation step of the proposed edge-detection algorithm greatly reduces noise effects, gets better edge localization such as line ends and corners, and plays a crucial role in refining edge outputs. The experiments show that our algorithm converges quickly and is robust in noisy environments     

Generalized symmetry and its application to 3D shape generation

A new method for easily and rapidly generating three-dimensional shapes from two-dimensional line-drawings is presented. This method is based on the generalized symmetry constraint. Generalized symmetry is an extended concept of threedimensional symmetry and its axis is a 3D smooth curve. This paper first develops the definition and constraint of generalized symmetry, and then describes an algorithm which generates the three-dimensional shape of an object from its linedrawing. The generation algorithm is extended to generate generalized cylindrical objects from line-drawings. Several experiments by computer simulation verify that the algorithm can generate three-dimensional shapes from line-drawings.     

A new distance measure based on the weighted induced method and its application to Pythagorean fuzzy multiple attribute group decision making

This paper investigates a novel induced ordered weighted averaging (IOWA) distance operator and its application in Pythagorean fuzzy (PF) multiattribute group decision making (MAGDM). First, a new induced aggregated distance operator named the weighted IOWA distance (WIOWAD) operator is developed, which differs from the existing methods in that it considers the dual roles of the order-inducing variables at the same time. In other words, in addition to inducing the order of the arguments, the order-inducing variables of the WIOWAD operator also plays an important role in moderating the associated weight vector. Some useful properties and different families of the WIOWAD are also discussed. Then, an extension of the WIOWAD within the PF situation is presented, thus obtaining the PFWIOWAD operator. Furthermore, a MAGDM method based on the PFWIOWAD is introduced. Finally, the practicality and effectiveness of proposed approach are illustrated in a research and development project selection problem.     

一种基于形状描述的模型配准方法

针对模型配准中需要预配准操作的问题, 提出了一种基于几何形状描述的模型配准方法。首先利用一种基于距离场的几何形状描述方法从预配准模型中获取特征点并建立对应关系, 即构造预配准模型间的特征点对; 之后, 以特征点对为基础计算模型配准所需的转换矩阵以实现对模型的一步精确配准。实例表明该方法在不需要对模型进行预配准操作的情况下可实现模型的精确配准。     

真度方程组及其应用

基于计量逻辑学和真度方程的思想,提出了真度方程组的概念。给出了真度方程组的同型解,探讨了真度方程组解集合的相容性。将这一理论成功地运用于多重广义MP问题的研究,求出了多重广义MP问题的三I真度解与α-三I真度解,为进一步探讨模糊推理的逻辑基础提供了一个可行的途径。     

双向S-模糊粗糙集及其应用

以Z.Pawlak粗集理论为基础,将动态模糊近似概念引入Dubois模糊粗糙集中。提出了双向S-模糊粗糙集概念,给出了双向S-模糊粗糙集的结构与性质。分析了双向S-模糊粗糙集与Z.Pawlak粗集、Dubois模糊粗集、S-粗集、S-粗糙模糊集及单向S-模糊粗糙集之间的关系。给出了双向S-模糊粗糙集的应用及存在价值。