We propose a new framework for image-based three-dimensional (3D) model retrieval. We first model the query image as a Euclidean point. Then we model all projected views of a 3D model as a symmetric positive definite (SPD) matrix, which is a point on a Riemannian manifold. Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric learning. To solve this heterogeneous matching problem, we map the Euclidean space and SPD Riemannian manifold to the same high-dimensional Hilbert space, thus shrinking the great gap between them. Finally, we design an optimization algorithm to learn a metric in this Hilbert space using a kernel trick. Any new image descriptors, such as the features from deep learning, can be easily embedded in our framework. Experimental results show the advantages of our approach over the state-of-the-art methods for image-based 3D model retrieval.

Ship License Numbers (SLNs) localization is an important part of waterway intelligent transportation systems. Unfortunately, this issue has been neglected for a long time. In this paper, we present an effective approach for localizing multi-style SLNs in nature scenes. The problem of locating SLNs is posed as the detection of character sequences which possess SLNs prior features. First, faced with the difficulty of no training data, a transfer learning-based deep convolutional neural network is designed to detect character sequences in SLNs. In the second step, to accurately locate SLNs from the detected character sequences, the prior features of SLNs are considered. Three SLNs prior features are summarized. An SLNs region generating algorithm and a low-level similarity-based fake SLNs filtering algorithm are presented, respectively. The accurate positions of the SLNs in the input image are obtained in this stage. The proposed approach is finally tested on ZJUSHIPS950 dataset. The approach achieves a FPPI of 0.42 and a F-measure of 0.614 on 1374 labeled SLNs, surpassing several related methods by a large margin. Controlled experiment results also prove the impressive performances of the proposed SLNs region generating and fake SLNs filtering algorithms.

While recent techniques for discriminative dictionary learning have attained promising results on the classification tasks, their performance is highly dependent on the number of labeled samples available for training. However, labeling samples is expensive and time consuming due to the significant human effort involved. In this paper, we present a novel semi- supervised dictionary learning method which utilizes the structural sparse relationships between the labeled and unlabeled samples. Specifically, by connecting the sparse reconstruction coefficients on both the original samples and dictionary, the unlabeled samples can be automatically grouped to the different labeled samples, and the grouped samples share a small number of atoms in the dictionary via mixed l2p- norm regularization. This makes the learned dictionary more representative and discriminative since the shared atoms are learned by using the labeled and unlabeled samples potentially from the same class. Minimizing the derived objective function is a challenging task because it is non-convex and highly non-smooth. We propose an efficient optimization algorithm to solve the problem based on the block coordinate descent method. Moreover, we have a rigorous proof of the convergence of the algorithm. Extensive experiments are presented to show the superior performance of our method in classification applications.

This paper presents a novel formulation for detecting objects with articulated rigid bodies from high-resolution monitoring images, particularly engineering vehicles. There are many pixels in high-resolution monitoring images, and most of them represent the background. Our method first detects object patches from monitoring images using a coarse detection process. In this phase, we build a descriptor based on histograms of oriented gradient, which contain color frequency information. Then we use a linear support vector machine to rapidly detect many image patches that may contain object parts, with a low false negative rate and a high false positive rate. In the second phase, we apply a refinement classification to determine the patches that actually contain objects. In this stage, we increase the size of the image patches so that they include the complete object using models of the object parts. Then an accelerated and improved salient mask is used to improve the performance of the dense scale-invariant feature transform descriptor. The detection process returns the absolute position of positive objects in the original images. We have applied our methods to three datasets to demonstrate their effectiveness.

In this paper, we propose a block-polynomial mapping for image feature learning, which can be efficiently represented by the matrix Khatri-Rao product. The block-polynomial mapping not only captures the local discriminative information within the image structure, but is also much more efficient than the traditional kernel mapping. Moreover, we embed the proposed mapping into the manifold regularization framework for semi-supervised image classification. Experimental results demonstrate that, while maintaining a comparable classification accuracy, the proposed algorithm performs much more efficient than the state-of-the-art methods.

BACKGROUND: Understanding the constituent domains of oncogenes, their origins and their fusions may shed new light about the initiation and the development of cancers. RESULTS: We have developed a computational pipeline for identification of functional domains of human genes, prediction of the origins of these domains and their major fusion events during evolution through integration of existing and new tools of our own. An application of the pipeline to 124 well-characterized human oncogenes has led to the identification of a collection of domains and domain pairs that occur substantially more frequently in oncogenes than in human genes on average. Most of these enriched domains and domain pairs are related to tyrosine kinase activities. In addition, our analyses indicate that a substantial portion of the domain-fusion events of oncogenes took place in metazoans during evolution. CONCLUSION: We expect that the computational pipeline for domain identification, domain origin and domain fusion prediction will prove to be useful for studying other groups of genes.

SUMMARY: QTLNetwork is a software package for mapping and visualizing the genetic architecture underlying complex traits for experimental populations derived from a cross between two inbred lines. It can simultaneously map quantitative trait loci (QTL) with individual effects, epistasis and QTL-environment interaction. Currently, it is able to handle data from F(2), backcross, recombinant inbred lines and double-haploid populations, as well as populations from specific mating designs (immortalized F(2) and BC(n)F(n) populations). The Windows version of QTLNetwork was developed with a graphical user interface. Alternatively, the command-line versions have the facility to be run in other prevalent operating systems, such as Linux, Unix and MacOS. AVAILABILITY: http://ibi.zju.edu.cn/software/qtlnetwork.

BACKGROUND: With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression. RESULTS: RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective. CONCLUSION: A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules.

An algorithm to approximate circle/circular arc with C2 cubic B-spline curve is presented. Given a prescribed tolerance e, the algorithm can find a C2 cubic B-spline curve with minimal number of segments that approximate the given circle/circular arc within e. The method developed can be used for various applications in CAD/CAM, biomechanical engineering and other fieIds.

Swept volume has been applied to many applications areas such as NC machining simulation and verification, robot workspace analysis, collision detection, and CAD. The numerical computation of swept volume remains to be a very challenging problem in terms of simplicity, efficiency and accuracy. The paper presents a novel swept volume approximation method for revolution generator solids sweeping along curved trajectories. This algorithms consists of the following main steps: (1) Discretization of the trajectory curve and establishments of reference frames; (2) Extraction and approximation of envelop profile at each discretized position based on the velocity vector calculated; and (3) Generation of envelop surfaces from the extracted envelop profile curves, and attachment of the ingress and egress surfaces to the envelop surface to form the SV Solid. Examples show that our algorithm is simple, efficient and accurate.

Abstract In this paper, we propose an example‐driven synthesis approach to create 3D animation of a target character. Our approach consists of the following steps: (a) a sketch‐based mapping between parts of the sources and corresponding portions of the target; (b) calculating the affine matrix of the source mesh for each vertex; (c) deforming the target based on the deformations of the corresponding parts of the sources by means of mean value coordinates. We provide new research contributions on all these topics and integrate them into our newly developed prototype animating system. Our approach is general and does not require the sources and target to share the same number of vertices or triangles, or to have matching connectivity. In our system, source animations can be any 3D animations and the target can even be unstructured or point cloud. Our approach is intuitive and is able to produce highly authentic 3D animations. We demonstrate our approach by constructing a full angel animation video from parts of kid and dolphin animations. Other animating examples produced from our prototype system are also given in the paper to illustrate our approach. Copyright © 2007 John Wiley & Sons, Ltd.

Product data exchange (DE) is a key technology to implement collaborative design of heterogeneous CAD. Based on analyzing layered expression of product CAD model and current methods of DE, heterogeneous CAD hybrid data exchange (HDE) based on feature semantics and geometry representation is presented. The method integrates advantages of feature semantics exchange (FSE) and geometric data exchange (GDE). It features high flexibility and reliability. A case illustrates the validity of the method

Access control is one of the key steps to ensuring the availability, confidentiality and integrity of system resources. While it has been fully applied in various network systems, it's still relatively new for collaborative CAD. This paper provides a new framework of multi-level access control for collaborative CAD based on hierarchical product modeling. A layered privilege model (LPM) has been developed to provide multi-granularity access permissions in the part level and feature level. An extended hierarchy role-based access control (EHRBAC) is implemented, integrated with LPM and common Hierarchy RBAC, to provide hierarchical access control of collaborative feature modeling in the component/part level and design feature level. Mesh simplification techniques are used to create variable level-of-detail for individual features.

We present in this paper a general algorithm called uniform color transfer for transferring dominant colors in one image to another. Our algorithm frees users from interactions required for colorizing grayscale image or transferring color between images. Different from existing algorithms, we cluster the source color image into regions using the GMM-EM method, and the target image using the K-means algorithm. We then impose chromatic mean values on corresponding source image regions in the target image. Experiments showed that our algorithm can give better results.

Recent development in computer hardware and modeling technologies has led to a fast increasing of 3D models. To help user find expected models accurately and efficiently, technologies on content-based 3D model retrievals have become one of the most challenging research topic recently. In this paper, radius-normal histogram(RNH) is proposed to describe shape contents and used for shape retrievals. The RNH shape descriptor first uses a series of concentric spheres to capture the point distribution information of the given model. Then for points in each concentric sphere, a radian normal angle is computed to extract the local geometry features. Finally, the radius-normal histogram is constructed by using the extracted shape signatures. The proposed shape representation remains invariant under rotations. It can be generated from the given 3D model efficiently and easily as well. Performance comparisons for the shape benchmark database have proven that the proposed algorithm can achieve better retrieving performance than other similar histogram-based shape representations. To further improve the retrieving performance, different shape features are combined based on dynamic weight selections and a hierarchical architecture is used to speed up the matching process.

We present an efficient and reliable method for computing the unit-in-the-last-place (ulp) of a double-precision floating-point number, taking advantage of the standard binary representation for floatingpoint numbers defined by IEEE Std 754-1985. The ulp is necessary to perform software rounding for robust rounded-interval arithmetic (RIA) operations. Hardware rounding, using two of the standard rounding modes defined by IEEE-754, may be more efficient. RIA has been used to produce robust software systems for the solution of systems of nonlinear equations, interrogation of geometric and differential properties of curves and surfaces, curve and surface intersections, and solid modeling.

A general and unified method is presented for generating a wide range of 3D objects by smoothing the vertices and edges of a given polyhedron with arbitrary topology using bicubic Bezier patches. The common solution to the compatibility equations of geometric continuity between two Bezier patches is obtained and employed as the foundation of this new method such that this new solid and surface model is reliable and compatible with the solid modeling and surface modeling system in the most common use. The new method has been embeded in an algorithm supported by our newly developed solid modeling system MESSAGE. The performance and implementation of this new algorithm show that it is efficient, flexible and easy to manipulate.