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Recognizing 3D objects from heavily cluttered and occluded 3D scenes is a very challenging task. This paper addresses this problem by developing a 3D object recognition method based on local and global geometric consistency constraints. Assuming an ...

A major challenge in real-world feature matching problems is to tolerate the numerous outliers arising in typical visual tasks. Variations in object appearance, shape, and structure within the same object class make it harder to distinguish inliers from ...

We consider the problem of finding a geometrically consistent set of point matches between two images. We assume that local descriptors have provided a set of candidate matches, which may include many outliers. We then seek the largest subset of these ...

Many tasks in computer vision are formulated as graph matching problems. Despite the NP-hard nature of the problem, fast and accurate approximations have led to significant progress in a wide range of applications. Learning graph models from observed ...

In this paper, we present a new approach for establishing correspondences between sparse image features related by an unknown nonrigid mapping and corrupted by clutter and occlusion, such as points extracted from images of different instances of the ...

We investigate the problem of finding the correspondence from multiple images, which is a challenging combinatorial problem. In this work, we propose a robust solution by exploiting the priors that the rank of the ordered patterns from a set of linearly ...

Graph matching is a powerful tool for computer vision and machine learning. In this paper, a novel approach to graph matching is developed based on the sequential Monte Carlo framework. By constructing a sequence of intermediate target distributions, ...

This paper presents a novel binary descriptor named as B-SIFT(Binarized Scale Invariant Feature Transform) for efficient invariant feature correspondence.Through analyzing the local distinctive gradient pattern, we convert the standard SIFT descriptor ...

We here propose a simple but highly potential algorithm to detect a model object's position on an input image by determining the initially unknown transformational states of the model object, in particular, size and 2D-rotation. In this algorithm, a ...

The significant advances in developing high-speed shape acquisition devices make it possible to capture the moving and deforming objects at video speeds. However, due to its complicated nature, it is technically challenging to effectively model and ...

Graph matching is an essential problem in computer vision and machine learning. In this paper, we introduce a random walk view on the problem and propose a robust graph matching algorithm against outliers and deformation. Matching between two graphs is ...

This paper deals with feature matching and segmentation of common objects in a pair of images, simultaneously. For the feature matching problem, the matching likelihoods of all feature correspondences are obtained by combining their discriminative power ...

Given a set of unordered images taken in a wide area, an effective solution is proposed for establishing robust feature correspondences among them. Two major improvements are made in our work as follows: firstly, a robust technique is proposed for the ...

In this paper, we consider the feature correspondence task as a graph matching problem. Our approach tends to maximize a similarity objective function, which consists of not only the feature vectors but also their corresponding constrained global ...

In this paper, we propose an efficient method for finding consistent correspondences between two sets of features Our matching algorithm augments the discriminative power of each correspondence with the spatial consistency directly estimated from a ...

Feature correspondence is an important step in image registration. Chamfer matching is a process of establishing the feature correspondence of an object (subimage) in an image where both the subimage and the image are binary. Chamfer matching ...

In this paper, we present a novel approach to the problem of curve alignment, which measures and matches the similarity of two curves. Our method extracts curve features with high priorities given to salient and general features, and therefore leads to ...

Establishing correspondence between features in two images of the same scene taken from different viewing angles is a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide ...

This paper presents a novel solution to the reconstruction of 3D geometry models from partial, segmented (2.5D or 3D) range views. First, the geometric fusion works entirely on sparse symbolic information, i.e. attributed surface graphs, rather than ...

Invariant representations are frequently used in computer vision algorithms to eliminate the effect of an unknown transformation of the data. These representations, however, depend on the order in which the features are considered in the computations. ...