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Feature correspondence lays the foundation for many computer vision and image processing tasks, which can be well formulated and solved by graph matching. Because of the high complexity, approximate methods are necessary for graph matching, and the continuous relaxation provides an efficient approximate scheme. But there are still many problems to be settled, such as the highly nonconvex objective function, the ignorance of the combinatorial nature of graph matching in the optimization process, and few attention to the outlier problem. Focusing on these problems, this paper introduces a continuation method directly targeting at the combinatorial optimization problem associated with graph matching. Specifically, first a regularization function incorporating the original objective function and the discrete constraints is proposed. Then a continuation method based on Gaussian smoothing is applied to it, in which the closed forms of relevant functions with respect to the outlier distribution are deduced. Experiments on both synthetic data and real world images validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Graph matching is a fundamental problem in theoretical computer science and artificial intelligence, and lays the foundation for many computer vision and machine learning tasks. Approximate algorithms are necessary for graph matching due to its NP-complete nature. Inspired by the usage in network-related tasks, random walk is generalized to graph matching as a type of approximate algorithm. However, it may be inappropriate for the previous random walk-based graph matching algorithms to utilize continuous techniques without considering the discrete property. In this paper, we propose a novel random walk-based graph matching algorithm by incorporating both continuous and discrete constraints in the optimization process. Specifically, after interpreting graph matching by random walk, the continuous constraints are directly embedded in the random walk constraint in each iteration. Further, both the assignment matrix (vector) and the pairwise similarity measure between graphs are iteratively updated according the discrete constraints, which automatically leads the continuous solution to the discrete domain. Comparisons on both synthetic and real-world data demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Summary: Image stitching is important for the perception and manipulation of undersea robots. In spite of a well-developed technique, it is still challenging for undersea images because of their inevitable appearance ambiguity caused by the limited light in the undersea environment, and local disturbance caused by moving objects, ocean current, etc. To get a clean and stable background panorama in the undersea environment, this paper proposes an undersea image-stitching method by introducing graph-based registration and blending procedures. Specifically, in the registration procedure, matching the features in each undersea image pair is formulated and solved by graph matching, to incorporate the structural information between features. In the blending procedure, an energy function on the indirect graph Markov random field is proposed, which takes both image consistency and neighboring consistency into consideration. Coincidentally, both graph matching and energy minimization can be mathematically formulated by integer quadratic programming problems with different constraints; the recently proposed graduated nonconvexity and concavity procedure is used to optimize both problems. Experiments on both synthetic images and real-world undersea images witness the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Underwater robot plays an important role in underwater perception and manipulation tasks. Vision information processing is essential for the intelligent perception of an underwater robot, in which image matching is a fundamental topic. Feature-based image matching is suitable for the underwater environment. However, current underwater image matching usually directly applies those methods with a general purpose or designed for images obtained from the land to underwater images. The problem is that the blurring appearance caused feature descriptor ambiguity, which may greatly deteriorate the performance of these methods on underwater images. Aiming at problem, this article provides an underwater image matching framework by incorporating structural constraints. By integrating the appearance descriptor and structural information by a graph model, the feature correspondence-based image matching is formulated and solved by a graph matching method. Particularly, to solve the outlier feature problem, the graph matching method is applicable to the case where outlier features exist in both underwater images. Experiments on both synthetic points and real-world underwater images validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Graph matching is a fundamental problem in pattern recognition and computer vision. In this paper we introduce a novel graph matching algorithm to find the specified number of best vertex assignments between two labeled weighted graphs. The problem is first explicitly formulated as the minimization of a quadratic objective function and then solved by an optimization algorithm based on the recently proposed graduated nonconvexity and concavity procedure (GNCCP). Simulations on both synthetic data and real world images witness the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Feature correspondence lays the foundation for many tasks in computer vision and pattern recognition. In this paper the directed structural model is utilized to represent the feature set, and the correspondence problem is then formulated as the structural model matching. Compared with the undirected structural model, the proposed directed model provides more discriminating ability and invariance against rotation and scale transformations. Finally, the recently proposed convex–concave relaxation procedure (CCRP) is generalized to approximately solve the problem. Extensive experiments on synthetic and real data witness the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: Exploiting both appearance similarity and geometric consistency is popular in addressing the feature correspondence problem. However, when there exist outliers the performance generally deteriorates greatly. In this paper, we propose a novel partial correspondence method to tackle the problem with outliers. Specifically, a novel pairwise term together with a neighborhood system is proposed, which, together with the other two pairwise terms and a unary term, formulates the correspondence to be solved as a subgraph matching problem. The problem is then approximated by the recently proposed Graduated Non-Convexity and Graduated Concavity Procedure (GNCGCP). The proposed algorithm obtains a state-of-the-art accuracy in the existence of outliers while keeping computational complexity and storage complexity. Simulations on both the synthetic and real-world images witness the effectiveness of the proposed method. [Copyright &y& Elsevier]