In this work, we propose two novel quantum walk kernels, namely the Hierarchical Aligned Quantum Jensen-Shannon Kernels (HAQJSK), between un-attributed graph structures. Different from most classical proposed HAQJSK kernels can incorporate hierarchical aligned structure information graphs and transform of random sizes into fixed-size structures, i.e., Transitive Adjacency Matrix vertices Density Continuous-Time Walks (CTQW). With pairwise to hand, resulting are defined by computing...

This paper describes an efficient algorithm for inexact graph matching. The method is purely structural, that is, it uses only the edge or connectivity structure of and does not draw on node attributes. We make two contributions: 1) commencing from a probability distribution matching errors, we show how problem can be posed as maximum-likelihood estimation using apparatus EM algorithm; 2) cast recovery correspondence matches between nodes in matrix framework. allows one to efficiently...

This paper describes a Bayesian framework for performing relational graph matching by discrete relaxation. Our basic aim is to draw on this provide comparative evaluation of number contrasting approaches matching. Broadly speaking there are two main aspects study. Firstly we focus the issue how inexactness may be quantified. We illustrate that several popular distance measures can recovered as specific limiting cases consistency measure. The second aspect our comparison concerns way in which...

When unpolarized light is reflected from a smooth dielectric surface, it becomes partially polarized. This due to the orientation of dipoles induced in reflecting medium and applies both specular diffuse reflection. paper concerned with exploiting polarization by surface reflection, using images objects, recover normals and, hence, height. presents underlying physics starting Fresnel equations. These equations are used interpret taken linear polarizer digital camera, revealing shape objects....

This paper exploits the properties of commute time between nodes a graph for purposes clustering and embedding, explores its applications to image segmentation multi-body motion tracking. Our starting point is lazy random walk on graph, which determined by heatkernel can be computed from spectrum Laplacian. We characterize using (i.e. expected taken travel two return) show how this quantity may Laplacian discrete Green’s function. motivation that anticipated more robust measure proximity...

Graph structures have proven computationally cumbersome for pattern analysis. The reason this is that, before graphs can be converted to vectors, correspondences must established between the nodes of which are potentially different size. To overcome problem, in paper, we turn spectral decomposition Laplacian matrix. We show how elements matrix used construct symmetric polynomials that permutation invariants. coefficients these as graph features encoded a vectorial manner. extend...

This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification realized by constructing mixture model over bipartite graph representing correspondence match affecting optimization using EM algorithm. According our framework, probabilities structural gate contributions expected likelihood function used estimate maximum parameters. These...

This paper makes two contributions to the problem of needle-map recovery using shape-from-shading. First, we provide a geometric update procedure which allows image irradiance equation be satisfied as hard constraint. not only improves data closeness recovered needle-map, but also removes necessity for extensive parameter tuning. Second, exploit improved ease control new shape-from-shading process investigate various types consistency The first set constraints are based on smoothness. second...

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for distance they lack some formality and rigor computation string Hence, our aim to convert graphs sequences so matching techniques used. To do this, we use a spectral seriation method adjacency matrix into or sequence order. We show how serial ordering established using leading eigenvector matrix. pose problem graph-matching as maximum posteriori probability (MAP)...

Many computer vision and pattern recognition problems may be posed as the analysis of a set dissimilarities between objects. For many types data, these are not euclidean (i.e., they do represent distances points in space), therefore cannot isometrically embedded space. Examples include shape-dissimilarities, graph mesh geodesic distances. In this paper, we provide means embedding such non-euclidean data onto surfaces constant curvature. We aim to embed on space whose radius curvature is...

In this paper, we develop a novel backtrackless aligned-spatial graph convolutional network (BASGCN) model to learn effective features for classification. Our idea is transform arbitrary-sized graphs into fixed-sized aligned grid structures and define new spatial convolution operation associated with the structures. We show that proposed BASGCN not only reduces problems of information loss imprecise representation arising in existing spatially-based (GCN) models, but also bridges theoretical...

Despite recent stereo matching networks achieving impressive performance given sufficient training data, they suffer from domain shifts and generalize poorly to unseen domains. We argue that maintaining feature consistency between pixels is a vital factor for promoting the generalization capability of networks, which has not been adequately considered. Here we address this issue by proposing simple pixel-wise contrastive learning across viewpoints. The loss function explicitly constrains...

This paper describes a novel framework for comparing and matching corrupted relational graphs. The develops the idea of edit-distance originally introduced graph-matching by Sanfeliu Fu (1983). We show how Levenshtein distance (1966) can be used to model probability distribution structural errors in problem. is locate matches using MAP label updates. compare resulting algorithm with that recently reported Wilson Hancock. use offers an elegant alternative exhaustive compilation dictionaries....

An improved application of probabilistic relaxation to edge labeling is presented. The improvement derives from the use a representation process that internally consistent and which utilizes more complex description structure. uses dictionary represent permitted labelings entire context-conveying neighborhood each pixel. Details are given approach related process. A comparison with other edge-postprocessing strategies provided.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML"...

We present a specification of the problem combining evidence that arises in approach to consistent object-labelling known as probabilistic relaxation. This differs from others several important respects. Firstly, we ensure internal consistency by distinguishing between directly and indirectly interacting objects. Secondly, avoid certain problems interpretation meaning regarding iterative updating probabilities filtering process on measurements for Finally, overcome exponential complexity...