To address the 3D point matching problem where the pose difference between two point sets is unknown, the authors propose a path following (PF)–based algorithm. This method works by reducing the objective function of robust point matching (RPM) algorithm to a function of point correspondence variable and then using PF for optimisation. By using the 3D similarity transformation which has few parameters, authors’ method needs no regularisation on transformation and, therefore, can handle the case when the pose difference between two point sets is unknown. The authors also propose a novel convex term for use in the PF algorithm which is based on the low‐rank nature of authors’ objective function and leads to a PF algorithm which converges quickly. Experimental results demonstrated better robustness of the proposed method over state‐of‐the‐art methods and authors’ method is also efficient.

Psychological and cognitive findings indicate that human visual perception is attentive and selective, which may process spatial and appearance selective attentions in parallel. By reflecting some aspects of these attentions, this paper presents a novel correlation filter (CF) based tracking approach, corresponding to processing a local and a semi-local background domains, respectively. In the local domain, inspired by the Gestalt principle of figure-ground segregation, we leverage an efficient Boolean map representation, which characterizes an image by a set of Boolean maps via randomly thresholding its color channels, yielding a location response map as a weighted sum of all Boolean maps. The Boolean maps capture the topological structures of target and its scene with different granularities, thereby enabling to effectively improve tracking of non-rectangular objects. Alternatively, in the semi-local domains, we introduce a novel distractor-resilient metric regularization into CF, which acts as a force to push distractors into negative space. Consequently, the unwanted boundary effects of CF can be effectively alleviated. Finally, both models associated with the local and the semi-local domains are seamlessly integrated into a Bayesian framework, and the tracked location is determined by maximizing its likelihood function. Extensive evaluations on the OTB50, OTB100, VOT2016 and VOT2017 tracking benchmarks demonstrate that the proposed method achieves favorable performance against a variety of state-of-the-art trackers with a speed of 45 fps on a single CPU.

This study is concerned with the state smoothing problem for a class of non‐linear discrete‐time stochastic systems with one‐step random measurement delay (ORMD). The main contribution is that the forward–backward particle smoothing scheme is successfully extended to the systems with ORMD. First, the particle filter, specially designed to deal with ORMD, is implemented to obtain the filtering distribution and the joint distribution of state history. Then, by marginalising the joint distribution, the one‐step fixed‐lag smoothing distribution can be obtained. Finally, based on the forward–backward smoothing scheme, the particle approximation of the fixed‐interval smoothing distribution can be obtained by re‐weighting the particles which have been used in the foregoing one‐step fixed‐lag smoothing distribution. Simulation results demonstrate the effectiveness of the proposed smoother.

Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these issues, we propose a globally optimal algorithm for the robust point matching problem in the case that each model point has a counterpart in scene set. By eliminating the transformation variables, we show that the original matching problem is reduced to a concave quadratic assignment problem where the objective function has a low rank Hessian matrix. This facilitates the use of large scale global optimization techniques. We propose a modified normal rectangular branch-and-bound algorithm to solve the resulting problem where multiple rectangles are simultaneously subdivided to increase the chance of shrinking the rectangle containing the global optimal solution. In addition, we present an efficient lower bounding scheme which has a linear assignment formulation and can be efficiently solved. Extensive experiments on synthetic and real datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of robustness to outliers, matching accuracy, and run-time.

We propose a new point matching algorithm in this letter by minimizing a concave geometric matching cost function coming from the objective function of the robust point matching algorithm. Due to concavity of this function, naive optimization strategies such as gradient descent will fail. To address this problem, we use a path following strategy for optimization which works by adding a convex quadratic term to the objective function and then gradually transitioning from the state that there is only weight of the convex term to the state that there is only weight of the concave geometric matching cost term. Extensive experimental results demonstrate strong robustness of the method over several state-of-the-art methods and it also has good computational efficiency.

Recently, a concave optimization approach has been proposed to solve the robust point matching (RPM) problem. This method is globally optimal, but it requires that each model point has a counterpart in the data point set. Unfortunately, such a requirement may not be satisfied in certain applications when there are outliers in both point sets. To address this problem, we relax this condition and reduce the objective function of RPM to a function with few nonlinear terms by eliminating the transformation variables. The resulting function, however, is no longer quadratic. We prove that it is still concave over the feasible region of point correspondence. The branch-and-bound (BnB) algorithm can then be used for optimization. To further improve the efficiency of the BnB algorithm whose bottleneck lies in the costly computation of the lower bound, we propose a new lower bounding scheme which has a k-cardinality linear assignment formulation and can be efficiently solved. Experimental results show that the proposed algorithm outperforms state-of-the-arts in its robustness to disturbances and point matching accuracy.

Business-to-Consumer (B2C) e-commerce is popular because of its convenience, speed and price. Although there has been intense debate about quality dimensions of e-commerce websites, more research is needed to find a well-established measurement. This empirical study identifies a set of measurements with 10 factors and their corresponding dimensions, including software development attributes based on the literature and the qualitative and quantitative data gathered from four different stakeholders. The survey results suggest that security, smooth transaction processes and smooth shopping processes are the most important concerns for online shoppers. The IS success model checking suggests that the proposed measurements are comprehensive. This work is compared with the customized ISO 9126 quality model.

This paper addresses the problem of rotation-invariant nonrigid point set matching. The shape context (SC) feature descriptor is used because of its strong discriminative nature, whereas edges in the graphs constructed by point sets are used to determine the orientations of SCs. Similar to lengths or directions, oriented SCs constructed this way can be regarded as attributes of edges. By matching edges between two point sets, rotation invariance is achieved. Two novel ways of constructing graphs on a model point set are proposed, aiming at making the orientations of SCs as robust to disturbances as possible. The structures of these graphs facilitate the use of dynamic programming (DP) for optimization. The strong discriminative nature of SC, the special structure of the model graphs, and the global optimality of DP make our methods robust to various types of disturbances, particularly clutters. The extensive experiments on both synthetic and real data validated the robustness of the proposed methods to various types of disturbances. They can robustly detect the desired shapes in complex and highly cluttered scenes.