Results 1  10
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25
Distinctive Image Features from ScaleInvariant Keypoints
, 2003
"... This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substa ..."
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Cited by 5169 (20 self)
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This paper presents a method for extracting distinctive invariant features from images, which can be used to perform reliable matching between different images of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a a substantial range of affine distortion, addition of noise, change in 3D viewpoint, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearestneighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through leastsquares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near realtime performance.
Object Recognition from Local ScaleInvariant Features
 PROC. OF THE INTERNATIONAL CONFERENCE ON COMPUTER VISION, CORFU
, 1999
"... An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons i ..."
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Cited by 1605 (13 self)
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An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearestneighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a lowresidual leastsquares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partiallyoccluded images with a computation time of under 2 seconds.
Robust and Efficient Detection of Convex Groups
, 1995
"... This paper describes an algorithm that robustly locates convex collections of line segments in an image. The algorithm is guaranteed to find all convex sets of line segments in which the length of the line segments accounts for at least some fixed proportion of the length of their convex hull. This ..."
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Cited by 36 (1 self)
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This paper describes an algorithm that robustly locates convex collections of line segments in an image. The algorithm is guaranteed to find all convex sets of line segments in which the length of the line segments accounts for at least some fixed proportion of the length of their convex hull. This enables the algorithm to find convex groups whose contours are partially occluded or missing due to noise. We perform an expected case analysis of the algorithm's performance that shows that its run time is O(n 2 log(n) + nm), when we wish to find the m most salient groups in an image with n line segments. We support this analysis with experiments on real data. Our analysis not only reveals the circumstances under which our algorithm is efficient, but also tells us when the groups found are unlikely to occur at random, and so are likely to capture the underlying structure of a scene. We also demonstrate the grouping system as a module in an efficient recognition system that combines group...
Computational Theories of Object Recognition
 Trends in Cognitive Science
, 1997
"... This paper examines four current theoretical approaches to the representation and recognition of visual objects: structural descriptions, geometric constraints, multidimensional feature spaces, and shapespace approximation. The strengths and the weaknesses of the theories are considered, with a spe ..."
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Cited by 28 (6 self)
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This paper examines four current theoretical approaches to the representation and recognition of visual objects: structural descriptions, geometric constraints, multidimensional feature spaces, and shapespace approximation. The strengths and the weaknesses of the theories are considered, with a special focus on their approach to categorization  a computationally challenging task which is not widely addressed in computer vision (where the stress is rather on the generalization of recognition across changes of viewpoint).
Unsupervised Category Modeling, Recognition, and Segmentation in Images
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 2008
"... paper is aimed at simultaneously solving the following related problems: 1) unsupervised identification of photometric, geometric, and topological properties of multiscale regions comprising instances of the 2D category, 2) learning a regionbased structural model of the ..."
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Cited by 22 (7 self)
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paper is aimed at simultaneously solving the following related problems: 1) unsupervised identification of photometric, geometric, and topological properties of multiscale regions comprising instances of the 2D category, 2) learning a regionbased structural model of the
Largest Placement of One Convex Polygon inside Another
 Geom
, 1995
"... We show that the largest similar copy of a convex polygon P with m edges inside a convex polygon Q with n edges can be computed in O(mn 2 log n) time. We also show that the combinatorial complexity of the space of all similar copies of P inside Q is O(mn 2 ), and that it can also be computed in ..."
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Cited by 12 (3 self)
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We show that the largest similar copy of a convex polygon P with m edges inside a convex polygon Q with n edges can be computed in O(mn 2 log n) time. We also show that the combinatorial complexity of the space of all similar copies of P inside Q is O(mn 2 ), and that it can also be computed in O(mn 2 log n) time. Let P be a convex polygon with m edges and Q a convex polygon with n edges. Our goal is to find the largest similar copy of P inside Q (allowing translation, rotation, and scaling of P ); see Figure 1. A restricted version of this problem, in which we just determine whether P can be placed inside Q without scaling, was solved by Chazelle [4], in O(mn 2 ) time. See also [1, 6, 12] for other approaches to the more general problem, in which Q is an arbitrary polygonal region. (We remark that the complexity of the algorithms for the general case is considerably higher, about O(m 2 n 2 ) in [1], O(m 3 n 2 ) in [12], and O(m 4 n 2 ) in [6].) Problems concernin...
RegionBased Hierarchical Image Matching
 INT J COMPUT VIS
, 2007
"... This paper presents an approach to regionbased hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, b ..."
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Cited by 11 (6 self)
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This paper presents an approach to regionbased hierarchical image matching, where, given two images, the goal is to identify the largest part in image 1 and its match in image 2 having the maximum similarity measure defined in terms of geometric and photometric properties of regions (e.g., area, boundary shape, and color), as well as region topology (e.g., recursive embedding of regions). To this end, each image is represented by a tree of recursively embedded regions, obtained by a multiscale segmentation algorithm. This allows us to pose image matching as the tree matching problem. To overcome imaging noise, onetoone, manytoone, and manytomany node correspondences are allowed. The trees are first augmented with new nodes generated by merging adjacent sibling nodes, which produces directed acyclic graphs (DAGs). Then, transitive closures of the DAGs are constructed, and the tree matching problem reformulated as finding a bijection between the two transitive closures on DAGs, while preserving the connectivity and ancestordescendant relationships of the original trees. The proposed approach is validated on real images showing similar objects, captured under different types of noise, including differences in lighting conditions, scales, or viewpoints, amidst limited occlusion and clutter.
Geodesic distanceweighted shape vector image diffusion
 IEEE Trans. Vis. Comput. Graph
"... Abstract—This paper presents a novel and efficient surface matching and visualization framework through the geodesic distanceweighted shape vector image diffusion. Based on conformal geometry, our approach can uniquely map a 3D surface to a canonical rectangular domain and encode the shape character ..."
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Cited by 11 (4 self)
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Abstract—This paper presents a novel and efficient surface matching and visualization framework through the geodesic distanceweighted shape vector image diffusion. Based on conformal geometry, our approach can uniquely map a 3D surface to a canonical rectangular domain and encode the shape characteristics (e.g., mean curvatures and conformal factors) of the surface in the 2D domain to construct a geodesic distanceweighted shape vector image, where the distances between sampling pixels are not uniform but the actual geodesic distances on the manifold. Through the novel geodesic distanceweighted shape vector image diffusion presented in this paper, we can create a multiscale diffusion space, in which the crossscale extrema can be detected as the robust geometric features for the matching and registration of surfaces. Therefore, statistical analysis and visualization of surface properties across subjects become readily available. The experiments on scanned surface models show that our method is very robust for feature extraction and surface matching even under noise and resolution change. We have also applied the framework on the real 3D human neocortical surfaces, and demonstrated the excellent performance of our approach in statistical analysis and integrated visualization of the multimodality volumetric data over the shape vector image. Index Terms—Surface Matching, Shape Vector Image, Multiscale Diffusion, Visualization. 1
Prior Knowledge for Part Correspondence
"... Classical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance still falls far short of that of humans in challenging cases where corresponding shape parts may differ significantly in geom ..."
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Cited by 8 (2 self)
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Classical approaches to shape correspondence base their computation purely on the properties, in particular geometric similarity, of the shapes in question. Their performance still falls far short of that of humans in challenging cases where corresponding shape parts may differ significantly in geometry or even topology. We stipulate that in these cases, shape correspondence by humans involves recognition of the shape parts where prior knowledge on the parts would play a more dominant role than geometric similarity. We introduce an approach to part correspondence which incorporates prior knowledge imparted by a training set of presegmented, labeled models and combines the knowledge with contentdriven analysis based on geometric similarity between the matched shapes. First, the prior knowledge is learned from the training set in the form of perlabel classifiers. Next, given two query shapes to be matched, we apply the classifiers to assign a probabilistic label to each shape face. Finally, by means of a joint labeling scheme, the probabilistic labels are used synergistically with pairwise assignments derived from geometric similarity to provide the resulting part correspondence. We show that the incorporation of knowledge is especially effective in dealing with shapes exhibiting large intraclass variations. We also show that combining knowledge and content analyses outperforms approaches guided by either attribute alone. 1.
3D to 2D Pose Determination with Regions
 International Journal of Computer Vision
, 1999
"... This paper presents a novel approach to partsbased object recognition in the presence of occlusion. We focus on the problem of determining the pose of a 3D object from a single 2D image when convex parts of the object have been matched to corresponding regions in the image. We consider three t ..."
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Cited by 7 (0 self)
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This paper presents a novel approach to partsbased object recognition in the presence of occlusion. We focus on the problem of determining the pose of a 3D object from a single 2D image when convex parts of the object have been matched to corresponding regions in the image. We consider three types of occlusions: selfocclusion, occlusions whose locus is identified in the image, and completely arbitrary occlusions. We show that in the first two cases this is a convex optimization problem, derive efficient algorithms, and characterize their performance. For the last case, we prove that the problem of finding valid poses is computationally hard, but provide an efficient, approximate algorithm. This work generalizes our previous work on regionbased object recognition, which focused on the case of planar models. This research was supported by the Unites StatesIsrael Binational Science Foundation, Grant No. 94100. The vision group at the Weizmann Inst. is supported in part by...