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120
Evaluation of Interest Point Detectors
, 2000
"... Many different lowlevel feature detectors exist and it is widely agreed that the evaluation of detectors is important. In this paper we introduce two evaluation criteria for interest points: repeatability rate and information content. Repeatability rate evaluates the geometric stability under diff ..."
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Cited by 295 (7 self)
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Many different lowlevel feature detectors exist and it is widely agreed that the evaluation of detectors is important. In this paper we introduce two evaluation criteria for interest points: repeatability rate and information content. Repeatability rate evaluates the geometric stability under different transformations. Information content measures the distinctiveness of features. Different interest point detectors are compared using these two criteria. We determine which detector gives the best results and show that it satisfies the criteria well.
A Survey of Shape Analysis Techniques
 Pattern Recognition
, 1998
"... This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems. ..."
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Cited by 200 (2 self)
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This paper provides a review of shape analysis methods. Shape analysis methods play an important role in systems for object recognition, matching, registration, and analysis. Researchin shape analysis has been motivated, in part, by studies of human visual form perception systems.
Detecting Salient BlobLike Image Structures with a ScaleSpace Primal Sketch: A Method for FocusofAttention
 INT. J. COMP. VISION
, 1993
"... This article presents: (i) a multiscale representation of greylevel shape called the scalespace primal sketch, which makes explicit both features in scalespace and the relations between structures at different scales, (ii) a methodology for extracting significant bloblike image structures from ..."
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Cited by 151 (14 self)
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This article presents: (i) a multiscale representation of greylevel shape called the scalespace primal sketch, which makes explicit both features in scalespace and the relations between structures at different scales, (ii) a methodology for extracting significant bloblike image structures from this representations, and (iii) applications to edge detection, histogram analysis, and junction classification demonstrating how the proposed method can be used for guiding later stage visual processes. The representation gives a qualitative description of image structure, which allows for detection of stable scales and associated regions of interest in a solely bottomup datadriven way. In other words, it generates coarse segmentation cues, and can hence be seen as preceding further processing, which can then be properly tuned. It is argued that once such information is available, many other processing tasks can become much simpler. Experiments on real imagery demonstrate that the proposed theory gives intuitive results.
Flexible Syntactic Matching of Curves and its Application to Automatic Hierarchical Classification of Silhouettes
 IEEE Transactions on Pattern Analysis and Machine Intelligence
"... Curve matching is one instance of the fundamental correspondence problem. Our exible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations. A syntactic representation is constructed for both curves, and an edit transformation which ..."
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Cited by 113 (2 self)
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Curve matching is one instance of the fundamental correspondence problem. Our exible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations. A syntactic representation is constructed for both curves, and an edit transformation which maps one curve to the other is found using dynamic programming. We present extensive...
A Computational Approach for Corner and Vertex Detection
 International Journal of Computer Vision
, 1992
"... Corners and vertices are strong and useful features in Computer Vision for scene analysis, stereo matching and motion analysis. This paper deals with the development of a computational approach to these important features. We consider first a corner model and study analytically its behavior once it ..."
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Cited by 108 (1 self)
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Corners and vertices are strong and useful features in Computer Vision for scene analysis, stereo matching and motion analysis. This paper deals with the development of a computational approach to these important features. We consider first a corner model and study analytically its behavior once it has been smoothed using the wellknown Gaussian filter. This allows us to clarify the behavior of some well known cornerness measure based approaches used to detect these points of interest. Most of these classical approaches appear to detect points that do not correspond to the exact position of the corner. A new scalespace based approach that combines useful properties from the Laplacian and Beaudet's measure [Bea78] is then proposed in order to correct and detect exactly the corner position. An extension of this approach is then developed to solve the problem of trihedral vertex characterization and detection. In particular, it is shown that a trihedral vertex has two elliptic maxima on ...
Finding corners
 Image and Vision Computing Journal
, 1988
"... Many important image cues such as 'T','X' and 'L' junctions have a local twodimensional structure. Conventional edge detectors are designed for onedimensional 'events'. Even the best edge operators can not reliably detect these twodimensional features. This contribution proposes a solution to ..."
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Cited by 60 (0 self)
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Many important image cues such as 'T','X' and 'L' junctions have a local twodimensional structure. Conventional edge detectors are designed for onedimensional 'events'. Even the best edge operators can not reliably detect these twodimensional features. This contribution proposes a solution to the twodimensional problem. In this paper, I address the following: • 'L'junction detection. Previous attempts, relying on the second differentials of the image surface have essentially measured image curvature. Recently Harris [Harris 87] implemented a 'corner ' detector that is based only on first differentials. I provide a mathematical proof to explain how this algorithm estimates image curvature. Although this algorithm will isolate image 'L'junctions, its performance cannot be predicted for T'junctions and other higher order image structures. • Instead, an image representation is proposed that exploits the richness of the local differential geometrical 'topography ' of the intensity surface. Theoretical and experimental results are presented which demonstrate how idealised instances of twodimensional surface features such as junctions can be characterised by the differential geometry of a simple facet model. • Preliminary results are very encouraging. Current studies are concerned with the extension to real data. I am investigating statistical noise models to provide a measure of 'confidence' in the geometric labelling. The richness and sparseness of a twodimensional structure can be exploited in many highlevel vision processes. I intend to use my representation to explore some of these fields in future work.
Classical Floorplanning Harmful?
 ISPD
, 2000
"... Classical floorplanning formulations may lead researchers to solve the wrong problems. This paper points out several examples, including (i) the preoccupation with packingdriven, as opposed to connectivitydriven, problem formulations and benchmarking standards; (ii) the preoccupation with rectangu ..."
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Cited by 38 (2 self)
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Classical floorplanning formulations may lead researchers to solve the wrong problems. This paper points out several examples, including (i) the preoccupation with packingdriven, as opposed to connectivitydriven, problem formulations and benchmarking standards; (ii) the preoccupation with rectangular (and L or T shaped) block shapes; and (iii) the lack of attention to algorithm scalability, fixeddie layout requirements, and the overall RTLdown methodology context. The right problem formulations must match the purpose and context of prevailing RTLdown design methodologies, and must be neither overconstrained nor underconstrained. The right solution ingredients are those which are scalable while delivering good solution quality according to relevant metrics. We also describe new problem formulations and solution ingredients, notably a perfect rectilinear floorplanning formulation that seeks zerowhitespace, perfectly packed rectilinear floorplans in a fixeddie regime. The paper clo...
Perspective Projection: The Wrong Imaging Model
, 1995
"... : Perspective projection is generally accepted as the ideal model of image formation. Many recent algorithms, and many recent judgements about the relative merits of different algorithms, depend on this assumption. However, perspective projection represents only the front half of the viewing sphere ..."
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Cited by 32 (2 self)
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: Perspective projection is generally accepted as the ideal model of image formation. Many recent algorithms, and many recent judgements about the relative merits of different algorithms, depend on this assumption. However, perspective projection represents only the front half of the viewing sphere and it distorts the shape and intensity of objects unless they lie near the optical axis. It is only one of several projections used in lens design and it does not accurately model the behavior of many real lenses. It works well only for narrowangle images. This paper surveys the properties of several alternative models of image formation. A model based on stereographic projection of the viewing sphere is shown to be a better generalpurpose imaging model than perspective projection. The new model can represent wider fields of view and more closely approximates real wideangle lenses. It preserves a suitable range of shape properties, including local symmetries. It approximates narrowangl...
Integral invariants for shape matching
 PAMI
, 2006
"... Abstract—For shapes represented as closed planar contours, we introduce a class of functionals which are invariant with respect to the Euclidean group and which are obtained by performing integral operations. While such integral invariants enjoy some of the desirable properties of their differential ..."
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Cited by 29 (2 self)
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Abstract—For shapes represented as closed planar contours, we introduce a class of functionals which are invariant with respect to the Euclidean group and which are obtained by performing integral operations. While such integral invariants enjoy some of the desirable properties of their differential counterparts, such as locality of computation (which allows matching under occlusions) and uniqueness of representation (asymptotically), they do not exhibit the noise sensitivity associated with differential quantities and, therefore, do not require presmoothing of the input shape. Our formulation allows the analysis of shapes at multiple scales. Based on integral invariants, we define a notion of distance between shapes. The proposed distance measure can be computed efficiently and allows warping the shape boundaries onto each other; its computation results in optimal point correspondence as an intermediate step. Numerical results on shape matching demonstrate that this framework can match shapes despite the deformation of subparts, missing parts and noise. As a quantitative analysis, we report matching scores for shape retrieval from a database. Index Terms—Integral invariants, shape, shape matching, shape distance, shape retrieval. Ç 1
Cartan's Moving Frame Method and Its Application to the Geometry and Evolution of Curves in the Euclidean, Affine and Projective Planes
, 1994
"... This article addresses the question of describing the differential properties of shapes which are invariant to the action of a group. The shapes of interest are differentiable manifolds such as curves and surfaces but can also be differentiable sets of lines such as complexes or congruences. The gro ..."
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Cited by 27 (3 self)
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This article addresses the question of describing the differential properties of shapes which are invariant to the action of a group. The shapes of interest are differentiable manifolds such as curves and surfaces but can also be differentiable sets of lines such as complexes or congruences. The groups of interest in computer vision are the euclidean, affine (or unimodular affine) and projective groups. Among the methods that can be used to obtain such descriptions there is one that clearly emerges because of its simplicity, elegance, generality, and because it is quite amenable to computer implementation. This method is known as the Cartan's moving frame method and has been developed in the first decades of this century by Elie Cartan and his students [2, 3]. The method is widely used in mathematics and physics but has not yet attracted many researchers in computer vision with the notable exception of ter Haar Romeny and his coworkers [17]. In section 2 of this article we give a detailed description of the moving frame method which is completely general and can be used (and automated) in all practical cases. This description uses the tools of the modern exterior differential calculus which were being invented at the time Cartan was developing his moving frame method and is an extended version of what can be found in [2]. We then attempt to help the reader develop some intuition about how the method actually works by using it on three simple and useful examples: plane curves subject to the action of the euclidean, affine, and projective groups. To help even further the intuition we present geometric interpretations of the affine and projective arc lengths. We also relate projective and affine invariants to the more familiar euclidean ones. We found these relations quite...