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Rapid development of artificial intelligence motivates researchers to expand the capabilities of intelligent and autonomous robots. In many robotic applications, robots are required to make planning decisions based on perceptual information to achieve diverse goals in an efficient and effective way. The planning problem has been investigated in active robot vision, in which a robot analyzes its environment and its own state in order to move sensors to obtain more useful information under certain constraints. View planning, which aims to find the best view sequence for a sensor, is one of the most challenging issues in active robot vision. The quality and efficiency of view planning are critical for many robot systems and are influenced by the nature of their tasks, hardware conditions, scanning states, and planning strategies. In this paper, we first summarize some basic concepts of active robot vision, and then review representative work on systems, algorithms and applications from four perspectives: object reconstruction, scene reconstruction, object recognition, and pose estimation. Finally, some potential directions are outlined for future work.

The singly linearly and box constrained least squares regression has diverse applications in various fields. This paper builds upon previous work to develop an efficient and robust semismooth Newton based augmented Lagrangian (Ssnal) algorithm for solving this problem, in which a semismooth Newton (Ssn) algorithm with superlinear or even quadratic convergence is applied to solve the subproblems. Theoretically, the global and asymptotically superlinear local convergence of the Ssnal algorithm hold automatically under standard conditions. Computationally, a generalized Jacobian for the projector onto the feasible set is shown to be either diagonal or diagonal-minus-rank-1, which is a key ingredient for the efficiency of the Ssnal algorithm. Numerical experiments conducted on both synthetic and real data sets demonstrate that the Ssnal algorithm compared to several state-of-the-art first-order algorithms is much more efficient and robust.

Self-reconfigurable modular robots (SRM-robots) can autonomously change their shape according to different tasks and work environments, and have received considerable attention recently. Many reshaping/reconfiguration algorithms have been proposed. In this paper, we present a theoretical analysis of computational complexity on a reshape planning for a kind of lattice-type 3D SRM-robots, whose modules are of cubic shape and can move by rotating on the surfaces of other modules. Different from previous NP-completeness study on general chain-type robots (i.e. the motion of any chains and the location of modules can be arbitrary), we consider more practical constraints on modules’ shape (i.e. cubic shape), position (lying in 2D/3D grids) and motion (using orthogonal rotations) in this paper. We formulate the reshape planning problem of SRM-robots with these practical constraints by a (p, q) optimization problem, where p and q characterize two widely used metrics, i.e. the number of disconnecting/reconnecting operations and the number of reshaping steps. Proofs are presented, showing that this optimization problem is NP-complete. Therefore, instead of finding global optimization results, most likely approximation solution can be obtained for the problem instead of seeking polynomial algorithm. We also present the upper and lower bounds for the 2-tuple (p, q), which is useful for evaluating the approximation algorithms in future research.

This paper focuses on a singly linearly constrained class of convex quadratic programs with box-like constraints. We propose a new fast algorithm based on parametric approach and secant approximation method to solve this class of quadratic problems. We design efficient implementations for our proposed algorithm and compare its performance with two state-of-the-art standard solvers called Gurobi and Mosek. Numerical results on a variety of test problems demonstrate that our algorithm is able to efficiently solve the large-scale problems with the dimension up to fifty million and it substantially outperforms Gurobi and Mosek in terms of the running time.

This paper studies some properties associated with a closed convex cone $$\mathcal {K}_{1+}$$ , which is defined as the epigraph of the $$l_1$$ norm function of the metric projection onto the nonnegative orthant. Specifically, this paper presents some properties on variational geometry of $$\mathcal {K}_{1+}$$ such as the dual cone, the tangent cone, the normal cone, the critical cone and its convex hull, and others; as well as the differential properties of the metric projection onto $$\mathcal {K}_{1+}$$ including the directional derivative, the B-subdifferential, and the Clarke’s generalized Jacobian. These results presented in this paper lay a foundation for future work on sensitivity and stability analysis of the optimization problems over $$\mathcal {K}_{1+}$$ .

Line drawings, as a concise form, can be recognized by infants and even chimpanzees. Recently, how the visual system processes line-drawings attracts more and more attention from psychology, cognitive science and computer science. The neuroscientific studies revealed that line drawings generate similar neural actions as color photographs, which give insights on how to efficiently process big media data. In this paper, we present a comprehensive survey on line drawing studies, including cognitive mechanism of visual perception, computational models in computer vision and intelligent process in diverse media applications. Major debates, challenges and solutions that have been addressed over the years are discussed. Finally some of the ensuing challenges in line drawing studies are outlined.

The Mordukhovich coderivative has become an important tool for stability and optimality in variational analysis. In this paper, we focus on variational geometry of the complementarity set for second order cone. Particularly, the tangent cone and the normal cone to the complementarity set for second order cone are explicitly characterized. The result developed in this paper paves the way for characterizing the Mordukhovich coderivative of the normal mapping in the study of the Aubin property and optimality conditions for mathematical programs with second order cone complementarity constraints.

We consider a class of matrix spectral norm approximation problems for finding an affine combination of given matrices having the minimal spectral norm subject to some prescribed linear equality and inequality constraints. These problems arise often in numerical algebra, engineering and other areas, such as finding Chebyshev polynomials of matrices and fastest mixing Markov chain models. Based on classical analysis of proximal point algorithms (PPAs) and recent developments on semismooth analysis of nonseparable spectral operators, we propose a semismooth Newton-CG based dual PPA for solving the matrix norm approximation problems. Furthermore, when the primal constraint nondegeneracy condition holds for the subproblems, our semismooth Newton-CG method is proven to have at least a superlinear convergence rate. We also design efficient implementations for our proposed algorithm to solve a variety of instances and compare its performance with the nowadays popular first order alternating direction method of multipliers (ADMM). The results show that our algorithm, which is warm-started with an initial point obtained from the ADMM, substantially outperforms the pure ADMM, especially for the constrained cases and it is able to solve the problems robustly and efficiently to a relatively high accuracy.

The mechanism of human cognition and its computability provide an important theoretical foundation to intelligent computation of visual media. This paper focuses on the intelligent processing of massive data of visual media and its corresponding processes of perception, memory, and judgment in cognition. In particular, both the human cognitive mechanism and cognitive computability of visual media are investigated in this paper at the following three levels: neurophysiology, cognitive psychology, and computational modeling. A computational cognition model of Perception, Memory, and Judgment (PMJ model for short) is proposed, which consists of three stages and three pathways by integrating the cognitive mechanism and computability aspects in a unified framework. Finally, this paper illustrates the applications of the proposed PMJ model in five visual media research areas. As demonstrated by these applications, the PMJ model sheds some light on the intelligent processing of visual media, and it would be innovative for researchers to apply human cognitive mechanism to computer science.

With the rapid progress of the network and mobile techniques, mobile devices such as mobile phones and portable devices, have become one of the most important parts in common life. Efficient techniques for watching, navigating and sharing videos on mobile devices collaboratively are appealing in mobile environment. In this paper, we propose a novel approach supporting efficiently collaborative operations on videos with sketch gestures. Furthermore, effective collaborative annotation and navigation operations are given to interact with videos on mobile devices for facilitating users’ communication based on mobile devices’ characteristics. Gesture operation and collaborative interaction architecture are given and improved during the interactive process. Finally, a user study is conducted showing that the effectivity and collaborative accessibility of video exploration is improved.

Based on cognitive functionalities in human vision processing, we propose a computational cognitive model for object recognition with detailed algorithmic descriptions. The contribution of this paper is of two folds. Firstly, we present a systematic review on psychological and neurophysiological studies, which provide collective evidence for a distributed representation of 3D objects in the human brain. Secondly, we present a computational model which simulates the distributed mechanism of object vision pathway. Experimental results show that the presented computational cognitive model outperforms five representative 3D object recognition algorithms in computer science research.

The nuclear norm minimization problem is to find a matrix with the minimum nuclear norm subject to linear and second order cone constraints. Such a problem often arises from the convex relaxation of a rank minimization problem with noisy data, and arises in many fields of engineering and science. In this paper, we study inexact proximal point algorithms in the primal, dual and primal-dual forms for solving the nuclear norm minimization with linear equality and second order cone constraints. We design efficient implementations of these algorithms and present comprehensive convergence results. In particular, we investigate the performance of our proposed algorithms in which the inner sub-problems are approximately solved by the gradient projection method or the accelerated proximal gradient method. Our numerical results for solving randomly generated matrix completion problems and real matrix completion problems show that our algorithms perform favorably in comparison to several recently proposed state-of-the-art algorithms. Interestingly, our proposed algorithms are connected with other algorithms that have been studied in the literature.

Finding a sequence of workpiece orientations such that the number of setups is minimized is an important optimization problem in manufacturing industry. In this paper we present some interesting notes on this optimal workpiece setup problem. These notes show that (1) The greedy algorithm proposed in Comput. Aided Des. 35 (2003), pp. 1269–1285 for the optimal workpiece setup problem has the performance ratio bounded by O(ln n−ln ln n+0.78), where n is the number of spherical polygons in the ground set; (2) In addition to greedy heuristic, linear programming can also be used as a near-optimal approximation solution; (3) The performance ratio by linear programming is shown to be tighter than that of greedy heuristic in some cases.

The paper analyzes the rate of local convergence of the augmented Lagrangian method for nonlinear second-order cone optimization problems. Under the constraint nondegeneracy condition and the strong second order sufficient condition, we demonstrate that the sequence of iterate points generated by the augmented Lagrangian method locally converges to a local minimizer at a linear rate, whose ratio constant is proportional to 1/τ with penalty parameter τ not less than a threshold \(\hat{\tau}>0\) . Importantly and interestingly enough, the analysis does not require the strict complementarity condition.

The computation of exact geodesics on triangle meshes is a widely used operation in computer-aided design and computer graphics. Practical algorithms for computing such exact geodesics have been recently proposed by Surazhsky et al. [5]. By applying these geometric algorithms to real-world data, degenerate cases frequently appear. In this paper we classify and enumerate all the degenerate cases in a systematic way. Based on the classification, we present solutions to handle all the degenerate cases consistently and correctly. The common users may find the present techniques useful when they implement a robust code of computing exact geodesic paths on meshes.

This paper proposes a smoothing method for symmetric conic linear programming (SCLP). We first characterize the central path conditions for SCLP problems with the help of Chen-Harker-Kanzow-Smale smoothing function. A smoothing-type algorithm is constructed based on this characterization and the global convergence and locally quadratic convergence for the proposed algorithm are demonstrated.

In this paper an efficient framework for the creation of 3D digital content with point sampled geometry is proposed. A new hierarchy of shape representations with three levels is adopted in this framework. Based on this new hierarchical shape representation, the proposed framework offers concise integration of various volumetric- and surface-based modeling techniques, such as Boolean operation, offset, blending, free-form deformation, parameterization and texture mapping, and thus simplifies the complete modeling process. Previously to achieve the same goal, several separated algorithms had to be used independently with inconsistent volumetric and surface representations of the free-form object. Both graphics and industrial applications are presented to demonstrate the effectiveness and efficiency of the proposed framework.

In this paper, a simple and efficient algorithm is proposed for manifold-guaranteed out-of-core simplification of large meshes with controlled topological type. By dual-sampling the input large mesh model, the proposed algorithm utilizes a set of Hermite data (sample points with normals) as an intermediate model representation, which allows the topological structure of the mesh model to be encoded implicitly and thus makes it particularly suitable for out-of-core mesh simplification. Benefiting from the construction of an in-core signed distance field, the proposed algorithm possesses a set of features including manifoldness of the simplified meshes, toleration of nonmanifold mesh data input, topological noise removal, topological type control and, sharp features and boundary preservation. A novel, detailed implementation of the proposed algorithm is presented, and experimental results demonstrate that very large meshes can be simplified quickly on a low-cost off-the-shelf PC with tightly bounded approximation errors and with time and space efficiency.

In this paper, we establish a nonlinear Lagrangian algorithm for nonlinear programming problems with inequality constraints. Under some assumptions, it is proved that the sequence of points, generated by solving an unconstrained programming, convergents locally to a Kuhn-Tucker point of the primal nonlinear programming problem.

A variety of approaches have been proposed for polygon mesh reconstruction from a set of unstructured sample points. Suffering from severe aliases at sharp features and having a large number of unnecessary faces, most resulting meshes need to be optimized using input sample points in a postprocess. In this paper, we propose a fast algorithm to reconstruct high-quality meshes from sample data. The core of our proposed algorithm is a new mesh evaluation criterion which takes full advantage of the relation between the sample points and the reconstructed mesh. Based on our proposed evaluation criterion, we develop necessary operations to efficiently incorporate the functions of data preprocessing, isosurface polygonization, mesh optimization and mesh simplification into one simple algorithm, which can generate high-quality meshes from unstructured point clouds with time and space efficiency.

The human head is a significant part of the human body, with which we can recognize individuals from a vast universe of populations. Since the early 1970s, considerable effort has been devoted to computeraided modeling of human head for applications varied from realistic effect in computer graphics to custom model generation in modern manufacturing industries. However, realistic head modeling is still a challenge and continues to fascinate computer graphics researchers. The greatest difficulties in human head modeling result from the extremely complex geometric form of the human head. To generate realistic individualized models, most proposed head modeling techniques use the common approach of deforming a generic model into an individualized one, based on individual head information. There are various sources to obtain individual information, e.g., anthropometric data, range data and 2D pictures. Given the individual information, the quality of the resulting individualized model depends on the quality of the generic model and the deformation technique used. In our study, we observe that the mathematical form in which the generic model is represented strongly determines the deformation effect and, thus, determines the quality of the resulting individualized models. During the past few years, the multiresolution modeling technique has been demonstrated to be a powerful tool for highly detailed sculptured object modeling. In our work, we adopt the multiresolution technique for generic head modeling and propose a feature-based deformation technique. We show that using our technique we can generate highly realistic individualized head models with speed and efficiency. We also demonstrate that our proposed technique can result in great efficiency for a wide range of downstream applications.