Matching Images to Models for Registration and Object Detection via Clustering

Stockman, George C.; Kopstein, Steven; Benett, Sanford

doi:10.1109/tpami.1982.4767240

Cited by 278 publications

(85 citation statements)

References 6 publications

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“…These range from relaxation-based methods [19,4], to cluster detection in transformation space (by computing point-to-point correspondences [20][21][22], to hierarchical decomposition of transformation space coupled with the application of a robust similarity measure [2,11,23,24]. Most of the techniques presented in these papers are computationally intensive (in a worst-case theoretical sense), or take long times to run in practice.…”

Section: Prior Workmentioning

confidence: 99%

“…Thus, by sampling a number of triples of points (or even pairs or singletons, depending on the number of degrees of freedom in the transformation space), and enumerating all possible matching triples in the other point set, one is likely to encounter a good match. This is the basis of many matching algorithms, including those of Goshtasby and others [20][21][22] and Goodrich et al [17]. and also of methods based on geometric hashing [25,26].…”

Section: Prior Workmentioning

confidence: 99%

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Efficient algorithms for robust feature matching

Mount

Netanyahu

Moigne

1999

Pattern Recognition

135

103

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One of the basic building blocks in any point-based registration scheme involves matching feature points that are extracted from a sensed image to their counterparts in a reference image. This leads to the fundamental problem of point matching: Given two sets of points, find the (affine) transformation that transforms one point set so that its distance from the other point set is minimized. Because of measurement errors and the presence of outlying data points, it is important that the distance measure between the two point sets be robust to these effects. We measure distances using the partial Hausdorff distance. Point matching can be a computationally intensive task, and a number of theoretical and applied approaches have been proposed for solving this problem. In this paper, we present two algorithmic approaches to the point matching problem, in an attempt to reduce its computational complexity, while still providing a guarantee of the quality of the final match. Our first method is an approximation algorithm, which is loosely based on a branch-andbound approach due to Huttenlocher and Rucklidge, (Technical Report 1321, Dept. of Computer Science, Cornell University, Ithaca, 1992; Proc. IEEE Conf. on Computer vision and Pattern Recognition, New York, 1993, pp. 705-706). We show that by varying the approximation error bounds, it is possible to achieve a tradeoff between the quality of the match and the running time of the algorithm. Our second method involves a Monte Carlo method for accelerating the search process used in the first algorithm. This algorithm operates within the framework of a branch-and-bound procedure, but employs point-to-point alignments to accelerate the search. We show that this combination retains many of the strengths of branch-and-bound search, but provides significantly faster search times by exploiting alignments. With high probability, this method succeeds in finding an approximately optimal match. We demonstrate the algorithms' performances on both synthetically generated data points and actual satellite images.

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Section: Prior Workmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

Efficient algorithms for robust feature matching

Mount

Netanyahu

Moigne

1999

Pattern Recognition

135

103

View full text Add to dashboard Cite

show abstract

“…First, for each point in the image identify a potential set of points in the model by matching invariant features according to Eq. (6). Second, the image transformation that maps the point in the image and the point in the model is determined by Eq.…”

Section: Generalised Invariant Htmentioning

confidence: 99%

“…(8) and (9). The reminder of this paper will be concerned with characterising the function Q and solving the analytic expression in (6) and (7) when fa is deÿned by similarity and a ne transformations.…”

Section: Generalised Invariant Htmentioning

confidence: 99%

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Invariant characterisation of the Hough transform for pose estimation of arbitrary shapes

Aguado

Montiel

Nixon

2002

Pattern Recognition

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In this paper, we develop a new formulation and methodology for including invariance in a general form of the Hough transform. Essentially, the transformations that control a shape's appearance are extracted using invariance, for arbitrary shapes with a continuous description. We ÿrst develop a formal deÿnition of the Hough transform mapping for arbitrary shapes and general transformations. We then include an invariant characterisation of shapes and develop and apply our new technique to extract shapes under similarity and a ne transformations. Our formulation and implementation is based directly on parametric curves and so avoids the use of indexed look-up tables. This confers the attributes of a continuous shape description avoiding discretisation problems inherent in earlier formulations. To obtain an invariant characterisation, each point in the model is related to a collection of other points deÿning a geometric arrangement. This characterisation does not require the computation of properties for lines or other primitives that compose the model, but is based solely on the local geometry of the points on shapes. The transformation is obtained by solving for the parameters of the curve according to an arrangement of points deÿned for a point in the image and a corresponding arrangement of points for a point in the model with the same invariant properties. The location parameters can be gathered in a 2D accumulator space independent of the transformation and of a shape's complexity. Experimental results show that the new technique is capable of extracting arbitrary shapes under occlusion and when the image contains signiÿcant noise. ?

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Automated 3-D montage synthesis from laser-scanning confocal images: Application to quantitative tissue-level cytological analysis

et al. 1996

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This paper presents a landmark based method for efficient, robust, and automated computational synthesis of high‐resolution, two‐dimensional (2‐D) or three‐dimensional (3‐D) wide‐area images of a specimen from a series of overlapping partial views. The synthesized image is the set union of the areas or volumes covered by the partial views, is called the “montage.” This technique is used not only to produce gray‐level montages, but also to montage the results of automated image analysis, such as 3‐D cell segmentation and counting, so as to generate large representations that are equivalent to processing the large wide‐area image at high resolution. The method is based on computing a concise set of feature‐tagged landmarks in each partial view, and establishing correspondences between the landmarks using a combinatorial point matching algorithm. This algorithm yields a spatial transformation linking the partial views that can be used to create the montage. Such processing can be a first step towards high‐resolution large‐scale quantitative tissue studies. A detailed example using 3‐D laser‐scanning confocal microscope images of acriflavine‐stained hippocampal sections of rat brain is presented to illustrate the method. © 1996 Wiley‐Liss, Inc.

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Matching Images to Models for Registration and Object Detection via Clustering

Cited by 278 publications

References 6 publications

Efficient algorithms for robust feature matching

Efficient algorithms for robust feature matching

Invariant characterisation of the Hough transform for pose estimation of arbitrary shapes

Automated 3-D montage synthesis from laser-scanning confocal images: Application to quantitative tissue-level cytological analysis

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