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We overview techniques for optimal geometric estimation from noisy observations for computer vision applications. We first describe techniques based on minimization of a given cost function: least squares (LS), maximum likelihood (ML), and Sampson error ...

Technology drives advances in science. Giving scientists access to more powerful tools for collecting and understanding data enables them to both ask and answer new kinds questions that were previously beyond their reach. Of these new tools at their ...

Computational vision and biomedical image have made tremendous progress of the past decade. This is mostly due the development of efficient learning and inference algorithms which allow better, faster and richer modeling of visual perception tasks. ...

This paper addresses the problem of fine-grained recognition: recognizing subordinate categories such as bird species, car models, or dog breeds. We focus on two major challenges: learning expressive appearance descriptors and localizing discriminative ...

Various hand-crafted features and metric learning methods prevail in the field of person re-identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a "...

In this paper we describe the first version of our system for estimating 3D shape sequences from images of the frontal face. This approach is developed with 3D Visual Speech Animation (VSA) as the target application. In particular, the focus is on the ...

The past twenty years has seen the explosion of the "shape zoo": myriad shape representations, each with pros and cons. Of the varied denizens, distance transforms and density function shape representations have proven to be the most utile. Distance ...

Driven by desirable attributes such as topological characterization and invariance to isometric transformations, the use of the Laplace-Beltrami operator (LBO) and its associated spectrum have been widely adopted among the shape analysis community. Here ...

In this paper, we formulate a probabilistic point set matching problem under variational Bayesian framework and propose an iterative algorithm in which the posteriors of parameters are updated in sequence until a local optimum is reached. This ...

This paper proposes a method to align a set of images captured from multiple view points. Traditional methods using image warps parameterized by global transformations suffer from the problem of misalignment due to parallax effects induced by camera ...

Tristimulus display of the abundant information contained in a hyper spectral image is a challenging task. Previous visualization approaches focused on preserving as much information as possible in the reduced spectral space, but ended up with ...

We address the problem of encoding a graph of order n into a graph of order k ‹ n in a way to minimize reconstruction error. We characterize this encoding in terms of a particular factorization of the adjacency matrix of the original graph. The ...

Huge volume of graph data are becoming available. This scenario demands the development of effective and efficient methods to perform graph matching. In this paper, we propose to adapt the Bag-of-Words model into the context of graphs. Using a ...

Bai and Hancock recently proposed a novel information theoretic kernel for graphs, namely the Jensen-Shannon graph kernel. One drawback of their approach is that it cannot be applied to either attributed or labeled graphs. In this paper, we aim to ...

The estimation of mutual information between graphs has been an elusive problem until the formulation of graph matching in terms of manifold alignment. Then, graphs are mapped to multi-dimensional sets of points through structural preserving embeddings. ...

Complex networks are finding increasing use in many scientific fields as a data representation. They are used to describe social networks, power grids, transportation networks, food webs and protein interactions in organisms, for example. A number of ...

We present a novel framework, based on Germander's pattern theoretic concepts, for high-level interpretation of video activities. This framework allows us to elegantly integrate ontological constraints and machine learning classifiers in one formalism ...

In this study, the link between quantum mechanics and graph-cuts is exploited and a novel saliency map generation and salient object segmentation method is proposed based on the ground state solution of a modified Hamiltonian. First, the graph ...

As one of the most important image segmentation models, the Mumford-Shah functional was developed to pursue a piecewise smooth approximation of a given image based on the regularization on the total length of curves. In this paper, we modify the Mumford-...

The use of Millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. In this paper, several body shape-based techniques were applied to model the ...

Image segmentation is a challenging task that has several applications in domains like medical imaging and surveillance. Among the various approaches proposed for this task, unsupervised methods have the advantage of being able to segment images without ...

We describe a novel framework for segmenting a time- and view-coherent foreground matte sequence from synchronised multiple view video. We construct a Markov Random Field (MRF) comprising links between super pixels corresponded across views, and links ...