Showing papers on "Scale-invariant feature transform published in 1996"

Randomized generalized Hough transform for 2-D gray scale object detection

Abstract: This paper proposes a new algorithm for 2D object detection called randomized generalized Hough transform (RGHT). It combines the generalized Hough transform (GHT) with the randomized Hough transform (RHT). Our algorithm can detect arbitrary objects of various scales and orientations in gray level images. We also demonstrate RGHT's advantages of high speed, low storage requirement, high accuracy and arbitrary resolution through comparison with other related algorithms.

Accelerated Hough transform using rectangular image decomposition

Abstract: A novel fast method for evaluating the Hough transform is proposed, which can be used to accelerate detection of prevalent linear formations in binary images. An image is decomposed using rectangular blocks and the contribution of each whole block to the Hough transform space is evaluated, rather than the contribution of each image point. The resulting acceleration in the calculation of the Hough transform field is demonstrated in two image processing experiments related to object axis identification and skew detection of digitised documents.

Decomposition of the Hough Transform: Curve Detection with Efficient Error Propagation

Abstract: This paper describes techniques to perform fast and accurate curve detection using a variant of the Hough transform. We show that the Hough transform can be decomposed into small subproblems that examine only a subset of the parameter space. Each subproblem considers only those curves that pass through some small subset of the data points. This property allows the efficient implementation of the Hough transform with respect to both time and space, and allows the careful propagation of the effects of localization error in the detection process. The use of randomization yields an O(n) worst-case computational complexity for this method, where n is the number of data points, if we are only required to find curves that are significant with respect to the complexity of the data. In addition, this method requires little memory and can be easily parallelized.

Reducing the precision/uncertainty duality in the Hough transform

Abstract: The Hough transform is a popular method for detecting complex forms in digital images. However, the technique is not very robust since several parameters that determine the scope of the detection results, such as quantization thresholds and intervals, must first be defined. In the present paper, we propose to enhance shape detection with the Hough transform through fuzzy analysis. One chief drawback of the Hough transform, i.e. the uncertainty/precision duality, is thus reduced.

Feature tracking from an image sequence using affine invariance and Hough transform

Abstract: Feature point tracking from an image sequence is an important step in many methods of image understanding including shape from motion and mobile robot navigation. Assuming an affine camera model, this paper proposed a new tracking method using affine invariance. Any 3D feature point can have unique coordinates with reference to an affine basis and the affine coordinates are invariant to affine transformation: camera rotations and translations. The images of a set of 4 control points defining an affine basis are tracked in an image sequence using a conventional method. Under this assumption, given a feature point in any image, its locus in the first image is a straight line. The straight lines of the corresponding features from the image sequence will intersect at a point, the corresponding feature point, in the first image. A Hough transform technique is designed to detect this intersection point and track the corresponding feature points in the image sequence. This technique is suitable for tracking a large number of feature points. Its performance is practically unaffected by missing features in some images and large motion steps. Accurate and reliable results had been obtained in real experiments using the method.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Beyond the Hough Transform: Further Properties of the R-theta Mapping and Their Applications

Abstract: The Hough transform is a standard technique for finding features such as lines in images. Typically, points or edgels are mapped into a partitioned parameter or Hough space as individual votes where peaks denote the feature of interest. The standard mapping used for line detection is the Rθ mapping and the key property the Hough transform exploits is that lines in the image map to points in Hough space. In this paper we introduce and explore three further properties of the Rθ mapping and suggest applications for them. Firstly, we show that points in Hough space with maximal R forany value of θ are on the convex hull of the object in image space. It is shown that approximate hulls of 2D and 3D hulls of objects can be constructed in linear time using this approach. Secondly, it is shown that a simple relationship exists between the occluding contour of an object and the Rθ mapping and that this could in principle be used to generate approximate aspect graphs of objects whose geometry was known. Thirdly, it is shown that antipodal points on object boundaries, (which are optimal robot grasp points), can be found by translation and reflection of the Rθ representation.In addition we show the relationship between the Rθ mapping used in the Hough transform and the classical mathematical theory of duals. We use this analysis to prove formally stated properties of the Rθ mapping from image space to Hough space and in particular the relationship to the convex hull.

Towards a new framework of the Hough transform

Abstract: This paper's main contributions are three-fold. Firstly, it is shown that the two existing template matching-like definitions of the Hough transform proposed by Princen, Illingworth and Kittler (1992) and by Bergen and Shvaytser (1991) are inadequate. The principal reason behind this is that the common implicit assumption of these two definitions, that every feature point within the template associated with a given accumulator cell E/sub 0/ in Hough space votes equally to E/sub 0/, is not reasonable. Secondly, an inherent probabilistic aspect of the Hough transform embedded in the transformation process from the image space to the parameter space is clarified. It is concluded that when the Hough transform is used to detect a pattern, an appropriate curve (surface, if the number of the parameters to be detected is more than 2) density function, which depends on the parameterization of the pattern, must be implicitly or explicitly provided to eliminate the uncertainties resulting from such a probabilistic aspect. Thirdly, a new framework of the Hough transform is proposed which mainly consists of two parts, namely parameterization and associated curve (surface) density function.