Shape retrieval using multi-level included angle functions-based Fourier descriptor

An effective shape signature namely multi-level included angle functions MIAFs is proposed to describe the hierarchy information ranging from global information to local variations of shape.Invariance to rotation translation and scaling are the intrinsic properties of the MIAFs.For each contour point the multi-level included angles are obtained based on the paired line segments derived from unequal-arc-length partitions of contour.And a Fourier descriptor derived from multi-level included angle functions MIAFD is presented for efficient shape retrieval.The proposed descriptor is evaluated with the standard performance evaluation method on three shape image databases the MPEG-7 database the Kimia-99 database and the Swedish leaf database. The experimental results of shape retrieval indicate that the MIAFD outperforms the existing Fourier descriptors and has low computational complexity.And the comparison of the MIAFD with other shape description methods also shows that the proposed descriptor has the highest precision at the same recall value which verifies its effectiveness.

Modeling Real Objects for Kansei-based Shape Retrieval

A large number of 3D models are created on computers and available for networks. Some content-based retrieval technologies are indispensable to find out particular data from such anonymous datasets. Though several shape retrieval technologies have been developed, little attention has been given to the points on human＇s sense and impression （as known as Kansei） in the conventional techniques, In this paper, the authors propose a novel method of shape retrieval based on shape impression of human＇s Kansei. The key to the method is the Gaussian curvature distribution from 3D models as features for shape retrieval. Then it classifies the 3D models by extracted feature and measures similarity among models in storage.

Fourier Descriptors with Different Shape Signatures:a Comparative Study for Shape Based Retrieval of Kinematic Constraints

Fourier Descriptors（FD） has been widely used in image analysis and computer vision for shape recognition as they can be made independent of translation,rotation,as well as scaling.They have also been used for developing methods for the analysis and synthesis of four-bar linkages for path generation.This paper focuses on a comparative study of Fourier descriptors derived from various shape signatures of planar closed curves.This includes representations based on Cartesian coordinates,centroid distance,cumulative angle,and curvature.The comparison is conducted not only using commonly used criteria for shape representation and identification but also in the context of shape based retrieval of kinematic constraints for task centered mechanism design.Examples are provided to seek to extract geometric constraints such as circle,circular arc,ellipse and line-segment from a given motion.

3D Engineering Model Retrieval Based on Enhanced Shape Distributions

To reuse and share the valuable knowledge embedded in repositories of engineering models for accelerating the design process, improving product quality, and reducing costs, it is crucial to devise search engines capable of matching 3D models efficiently and effectively. In this paper, an enhanced shape distributions-based technique of using geometrical and topological information to search 3D engineering models represented by polygonal meshes was presented. A simplification method of polygonal meshes was used to simplify engineering model as the pretreatment for generation of sample points. The method of sampling points was improved and a pair of functions that was more sensitive to shape was employed to construct a 2D shape distribution. Experiments were conducted to evaluate the proposed algorithm utilizing the Engineering Shape Benchmark (ESB) database. The experiential results suggest that the search effectiveness is significantly improved by enforcing the simplification and enhanced shape distributions to engineering model retrieval.

A METHOD OF SHAPE ENCODING AND RETRIEVAL

A method of shape encoding and retrieval is proposed in this letter, which uses centripetal code to encode shape and extracts shape's convex for retrieval. For the rotation invariance and translation invariance of the centripetal code and the normalization of convex,the proposed retrieval method is similarity transform resistant, Experimental results confirm this capability.

Data driven composite shape descriptor design for shape retrieval with a VoR-Tree

We develop a data driven method(probability model) to construct a composite shape descriptor by combining a pair of scale-based shape descriptors. The selection of a pair of scale-based shape descriptors is modeled as the computation of the union of two events, i.e.,retrieving similar shapes by using a single scale-based shape descriptor. The pair of scale-based shape descriptors with the highest probability forms the composite shape descriptor. Given a shape database, the composite shape descriptors for the shapes constitute a planar point set.A VoR-Tree of the planar point set is then used as an indexing structure for efficient query operation. Experiments and comparisons show the effectiveness and efficiency of the proposed composite shape descriptor.

Shape Retrieval Using Fourier Descriptors Applied to Industrial Process

Nowadays, manufacturing processes are carried out at speeds that they themselves demand and subject to rigorous standards to maintain the quality of materials. An important step to define the quality of products in metalworking is the casting process, which principal focus is seeking control and monitoring of properties of materials. Nevertheless, it is not easy due to the high temperatures and gas produced in the vessel. Although some researchers have been attempting to solve these problems, it is difficult to carry out due to hard conditions. This article proposes the analysis of the surface of the liquid metal, that is, the slag on the surface, which is considered as connected spaces characterized by the topology of their discrete surface. These spaces are described through Fast Fourier Transform, associating changes of intensities to the frequency domain and obtaining main features of these frequencies, these features are used to define an enveloping shape that represents the liquid metal. Finally, the results obtained are presented, which, according to them shows that it is possible to characterize the slag, and by which it is possible to spatially locate the molten metal liquid in the refractory. Therefore, this research serves as the basis for the development of new algorithms for level detection and measurement, preventing overflows and damage to refractories.

AN EFFICIENT SHAPE RETRIEVAL WAY

By using the generalized Gaussian density model, this letter puts forward a new shape retrieval way based on the wavelet coefficients. Experimental results show that the proposed shape feature description is superior to the traditional invariant moment algorithm. Moreover, this algorithm provides the important invariant trait to image's size and rotation, which can retrieve images based on shape with more similar results comparing with invariant moment method.

Shape Retrieval Using ECPDH with Dynamic Programming

The matching and retrieval of the 2D shapes are challenging issues in object recognition and computer vision. In this paper, we propose a new object contour descriptor termed ECPDH (Elliptic Contour Points Distribution Histogram), which is based on the distribution of the points on an object contour under the polar coordinates. ECPDH has the essential merits of invariance to scale and translation. Dynamic Programming (DP) algorithm is used to measure the distance between the ECPDHs. The effectiveness of the proposed method is demonstrated using some standard tests on MPEG-7 shape database. The results show the precision and recall of our method over other recent methods in the literature.

Probabilistic 2D Shape Retrieval and Applications via the Method of Hurwitz-Radon Matrices

Artificial intelligence and computer vision need methods for 2D （two-dimensional） shape retrieval having discrete set of boundary points. A novel method of MHR （Hurwitz-Radon Matrices） is used in shape modeling. Proposed method is based on the family of MHR which possess columns composed of orthogonal vectors. 2D curve is retrieved via different functions as probability distribution functions： sine, cosine, tangent, logarithm, exponent, arcsin, arccos, arctan and power function. Created from the family of N-1 MHR and completed with the identical matrix, system of matrices is orthogonal only for dimensions N = 2, 4 or 8. Orthogonality of columns and rows is very significant for stability and high precision of calculations. MHR method is interpolating the function point by point without using any formula of function. Main features of MHR method are： accuracy of curve reconstruction depending on number of nodes and method of choosing nodes, interpolation of L points of the curve is connected with the computational cost of rank O（L）, MHR interpolation is not a linear interpolation.

Shape embedding and retrieval in multi-flow deformation

We propose a unified 3D flow frameworkfor joint learning of shape embedding and deformationfor different categories. Our goal is to recovershapes from imperfect point clouds by fitting thebest shape template in a shape repository afterdeformation. Accordingly, we learn a shape embeddingfor template retrieval and a flow-based network forrobust deformation. We note that the deformationflow can be quite different for different shapecategories. Therefore, we introduce a novel multi-hubmodule to learn multiple modes of deformation toincorporate such variation, providing a network whichcan handle a wide range of objects from differentcategories. The shape embedding is designed to retrievethe best-fit template as the nearest neighbor in a latentspace. We replace the standard fully connected layerwith a tiny structure in the embedding that significantlyreduces network complexity and further improvesdeformation quality. Experiments show the superiorityof our method to existing state-of-the-art methods viaqualitative and quantitative comparisons. Finally, ourmethod provides efficient and flexible deformation thatcan further be used for novel shape design.

High Efficient Methods of Content-based 3D Model Retrieval

Content-based 3D model retrieval is of great help to facilitate the reuse of existing designs and to inspire designers during conceptual design. However, there is still a gap to apply it in industry due to the low time efficiency. This paper presents two new methods with high efficiency to build a Content-based 3D model retrieval system. First, an improvement is made on the ＂Shape Distribution （D2）＂ algorithm, and a new algorithm named ＂Quick D2＂ is proposed. Four sample 3D mechanical models are used in an experiment to compare the time cost of the two algorithms. The result indicates that the time cost of Quick D2 is much lower than that of D2, while the descriptors extracted by the two algorithms are almost the same. Second, an expandable 3D model repository index method with high performance, namely, RBK index, is presented. On the basis of RBK index, the search space is pruned effectively during the search process, leading to a speed up of the whole system. The factors that influence the values of the key parameters of RBK index are discussed and an experimental method to find the optimal values of the key parameters is given. Finally, ＂3D Searcher＂, a content-based 3D model retrieval system is developed. By using the methods proposed, the time cost for the system to respond one query online is reduced by 75% on average. The system has been implemented in a manufacturing enterprise, and practical query examples during a case of the automobile rear axle design are also shown. The research method presented shows a new research perspective and can effectively improve the content-based 3D model retrieval efficiency.

Support Vector Machine active learning for 3D model retrieval

In this paper, we present a novel Support Vector Machine active learning algorithm for effective 3D model retrieval using the concept of relevance feedback. The proposed method learns from the most informative objects which are marked by the user, and then creates a boundary separating the relevant models from irrelevant ones. What it needs is only a small number of 3D models labelled by the user. It can grasp the user＇s semantic knowledge rapidly and accurately. Experimental results showed that the proposed algorithm significantly improves the retrieval effectiveness. Compared with four state-of-the-art query refinement schemes for 3D model retrieval, it provides superior retrieval performance after no more than two rounds of relevance feedback.

A flower image retrieval method based on ROI feature

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

Image Retrieval Based on Objects’ Spatial Orientation Relationships

A new concept of characteristic scanning radial (CSR) is proposed for thesegmented image on the basis of two shape-specific points of its shape-objects. Subsequently, twocharacteristic attribute sequences (CAS) of relative distance and relative direction are derived torepresent the spatial orientation relationships among objects of the image. A novel image retrievalalgorithm is presented using these two CASs. The proposed retrieval approach not only satisfies thetransformational invariance, butalso attains the quantitative comparison of matching. Experimentsidentify the effectiveness and efficiency of the algorithm adequately.

On-Demand Photon Storage and Retrieval with a Solid-State Photon Molecule at Room Temperature

Deterministically achieving on-chip photon storage and retrieval is a fundamental challenge for integrated photonics.Moreover,this requirement is increasingly urgent as photon storage and retrieval is crucial to realize truly scalable room-temperature quantum computing.However,most of existing quantum memories integrated on chips must either work at cryogenic temperature or else are strongly coupled with the environment,which hugely reduces the efficiency.Here,we propose an on-chip room-temperature quantum memory comprising three coupled microcavities,which presents an ideal dark state decoupled by a waveguide,thereby allowing on-demand photon storage and retrieval with high efficiency and high fidelity simultaneously.Furthermore,we demonstrate that the single-photon temporal duration can be increased or decreased by a factor of 10^(3),thereby enabling many crucial quantum applications.Our error-robust approach highlights the potential for a solid-state photonic molecule for use as on-chip quantum memory and for optical quantum computing.

Local features and manifold ranking coupled method for sketch-based 3D model retrieval

3D model retrieval virtual reality applications. In can benefit many downstream this paper, we propose a new sketch-based 3D model retrieval framework by coupling local features and manifold ranking. At technical fronts, we exploit spatial pyramids based local structures to facilitate the efficient construction of feature descriptors. Meanwhile, we propose an improved manifold ranking method, wherein all the categories between arbitrary model pairs will be taken into account. Since the smooth and detail-preserving line drawings of 3D model are important for sketch-based 3D model retrieval, the Difference of Gaussians （DOG） method is employed to extract the line drawings over the projected depth images of 3D model, and Bezier Curve is then adopted to further optimize the extracted line drawing. On that basis, we develop a 3D model retrieval engine to verify our method. We have conducted extensive experiments over various public benchmarks, and have made comprehensive comparisons with some state-of-the-art 3D retrieval methods. All the evaluation results based on the widely-used indicators prove the superiority of our method in accuracy, reliability, robustness, and versatility.

A Survey on Partial Retrieval of 3D Shapes

Content-based shape retrieval techniques can facilitate 3D model resource reuse, 3D model modeling, object recognition, and 3D content classification. Recently more and more researchers have attempted to solve the problems of partial retrieval in the domain of computer graphics, vision, CAD, and multimedia. Unfortunately, in the literature, there is little comprehensive discussion on the state-of-the-art methods of partial shape retrieval. In this article we focus on reviewing the partial shape retrieval methods over the last decade, and help novices to grasp latest developments in this field. We first give the definition of partial retrieval and discuss its desirable capabilities. Secondly, we classify the existing methods on partial shape retrieval into three classes by several criteria, describe the main ideas and techniques for each class, and detailedly compare their advantages and limits. We also present several relevant 3D datasets and corresponding evaluation metrics, which are necessary for evaluating partial retrieval performance. Finally, we discuss possible research directions to address partial shape retrieval.

双多普勒雷达观测在一次雹云结构中的应用

利用常州站和泰州站双多普勒天气雷达探测数据,结合常规观测资料和ERA5再分析资料,综合分析了2019年7月6日一次强对流天气过程的天气形势、雷达回波强度演变特征和双多普勒雷达反演的三维流场信息。重点利用双多普勒雷达径向速度资料反演出格点流场,并结合回波特征分析雹云的结构。结果表明:强对流天气过程中,江苏受高空冷涡影响,在高空冷槽、高空急流、低层切变线和地面辐合线的配置条件下,形成了上干冷、下暖湿的层结不稳定结构。本次过程中,雹云不仅具有典型的强对流单体雷达回波特征,在中层强回波中心处,还有明显的“S”型水平流场结构。雹云的低层,是明显的辐合与旋转配置的水平流场。深厚的零线结构是成雹的典型特征。

面向光束整形的自由曲面衍射光学设计方法(特邀)

光束整形技术能够实现激光光束能量分布的良好控制,在激光加工、医疗、激光核聚变等方面具有广泛的应用前景。光束整形方法主要分为几何光学方法和物理光学方法两大类,其中几何光学方法由于忽略了衍射效应,导致一些情形下的光束整形效果不够精细,但却能为物理光学方法提供良好的优化起点。结合几何光学方法和物理光学方法的复合设计方法能够设计出易于加工的自由曲面衍射光学元件,同时能抑制杂散光和散斑噪声。首先回顾了物理光学方法,然后综述了几何光学-物理光学复合设计方法的研究进展,最后对衍射光学元件设计方法的未来发展方向进行了一定的展望。