J. Wood, "Invariant pattern recognition: a review", Pattern Recognition, Vol. 29, No. 1, 1996, pp. 1-17.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... response that will be a peak at the centre of the circle, the magnitude of this peak will be invariant to the size of the circle (the response will vary with circle luminance, and phase at the peak will give a measure of the circle size) For recent reviews of invariants in pattern recognition see [13, 14]. A thorough introduction to this area is given by Rubenstein et al. 15] who show that the invariance kernel for scaling in two dimensions can be derived from the scaling group: x # # #x #17# y # # #y where (x, y) is the original coordinate, x , y # ) the transformed coordinate, and # ....

J. Wood, Invariant pattern recognition: a review, Pattern Recognition 29 (1) (1996) 1--17.

.... GMD can be estimated using the Expectation Maximization (EM) algorithm [3] 2 Invariance and Tangent Distance There exists a variety of ways to achieve invariance or transformation tolerance of a classi er, including normalization, extraction of invariant features and invariant distance measures [19]. Distance measures are used for classi cation as dissimilarity measures, i.e. the distances should ideally be small for members of the same class and large for members of di erent classes. An invariant distance measure ideally takes into account transformations of the patterns, yielding small ....

J. Wood. Invariant Pattern Recognition: A Review. Pattern Recognition, 29(1):1{ 17, January 1996.

....on differentials which are implemented using derivatives of the Gaussian. The use of the proposed invariant representation is shown to yield improved correlation results in a template matching scenario. 1 Introduction Invariants are a popular concept in object recognition and image retrieval [1, 2, 7, 10, 14, 15]. They aim to provide descriptions that remain constant under certain geometric or radiometric transformations of the scene, thereby reducing the search space. They can be classified into global invariants, typically based either on a set of key points or on moments, and local invariants, ....

J. Wood. Invariant pattern recognition: A review. Pattern Recognition, 29(1):1--17, 1996.

....in comparison with a conventional GMD approach using a comparable amount of model parameters. 1. Introduction The design of a classifier that is invariant w.r.t. certain transformations is an important aspect in pattern recognition. Many approaches to invariant pattern recognition are known [1], among them an invariant distance measure called tangent distance. TD was proposed in [2, 3] and proved to be very effective in the domain of optical character recognition. Distance measures like the Euclidean distance and related ones are very sensitive to small transformations, even though ....

J. Wood, "Invariant pattern recognition: A review.," Pattern Recognition, vol. 29, pp. 1--17, Jan. 1996.

....model (IDM) and relate it to tangent distance. The IDM considerably increased performance of the classifier with and without tangent distance on a database of medical images containing 1617 radiographs coming from daily routine. Many approaches to invariant pattern recognition are known [2] and TD has been used in a variety of settings, including neural networks and memory based techniques like (k ) nearest neighbor algorithms (k NN) 3] while in our experiments KD based classifiers obtained better results. A number of solutions have been proposed for efficient implementation of ....

J. Wood. Invariant Pattern Recognition: A Review. Pattern Recognition, 29(1):1--17, January 1996.

....= 8.6 Translation, Rotation, and Scaling This very frequent distortion is described by the operator 9 0 (g) x, y) g(ax cos q ay sin q x 0 , ax sin q ay cos q y 0 ) where q is a rotation angle. There have been described lot of 9 0 invariants based on various approaches (see [30] [31] for a survey) A large group of them is based on moments. Hu derived seven moment based 9 0 invariants in his fundamental paper [32] which have been employed by many researchers. Recently, Wong [33] has proposed a method how to generate an infinite sequence of rotation moment invariants and has ....

# J. Wood, "Invariant Pattern Recognition: A Review," Pattern Recognition, vol. 29, pp. 1-17, 1996.

....the search space in retrieval applications. They can be classified into global invariants, typically based on either a set of corresponding points, curves, or moments, and local invariants, typically based on derivatives of the image function which is assumed to be continuous and differentiable. Wood (1996) reviews several invariant methods for pattern recognition. 1 E mail: siebert cs.ubc.ca Preprint submitted to Elsevier Preprint 21 September 2000 A frequently occurring illumination transformation that hasn t been considered in image retrieval systems yet is gamma correction. In cameras, gamma ....

J. Wood, 1996. Invariant Pattern Recognition: A Review, Pattern Recognition, 29(1), 1-17.

....on differentials which are implemented using derivatives of the Gaussian. The use of the proposed invariant representation is shown to yield improved correlation results in a template matching scenario. 1 Introduction Invariants are a popular concept in object recognition and image retrieval [1, 2, 7, 10, 14, 15]. They aim to provide descriptions that remain constant under certain geometric or radiometric transformations of the scene, thereby reducing the search space. They can be classified into global invariants, typically based either on a set of key points or on moments, and local invariants, ....

J. Wood. Invariant pattern recognition: A review. Pattern Recognition, 29(1):1--17, 1996.

....of Finland under grant 44449. y The corresponding author. Previous work on image translations and rotations has concentrated on methods that typically use template matching, cross correlation (e.g. Caelli and Liu, 1988) and FFT techniques (Castro and Morandi, 1987) For a review, see e.g. (Wood, 1996). Combinatorial techniques that resemble ours were used in (Landau and Vishkin, 1994) and in (Takaoka, 1996) Our method is a generalization of our work of exact matching reported in (Fredriksson and Ukkonen, 1998) 2 Problem definition Let pattern P = P [1: m; 1: m] be a two dimensional m ....

Wood, J. (1996). Invariant pattern recognition: a review. Pattern Recognition, 29(1):1--17.

....is combinatorial. Examples of combinatorial pattern matching algorithms that work in two dimensions, but do not allow rotations are e.g. 3, 10, 13, 14, 15, 17, 18] On the other hand, there are many non combinatorial approaches to rotation invariant pattern matching, for a review, see e.g. [4, 20]. The only combinatorial methods, that come close to us in some respects, are [14, 17] However, these do not address the pattern rotations. As stated in [2] a major open problem in two dimensional (combinatorial) pattern matching is to find the occurrences of a two dimensional pattern of size ....

J. Wood. Invariant pattern recognition: a review. Pattern Recognition, 29(1):1--17, 1996.

....fold detection, complex moment, rotationally symmetric image. F 1I NTRODUCTION ECHNIQUES on invariant pattern recognition include integral transformations, construction of algebraic moments and the use of structured neural networks [1]. The image normalization method, developed as an elegant preprocessing technique, transforms the distorted input pattern into its corresponding normal form such that it is invariant under translation, scaling, skew, and rotation. The following overview will clarify that the existing techniques do ....

J. Wood, "Invariant Pattern Recognition: A Review," Pattern Recognition, vol. 29, no. 1, pp. 1-17, 1996.

....is to discover an appropriate compromise between invariance and reliability of object recognition. The greatest strength of our approach to object or situation recognition is the ability to learn (approximate) invariants under real world changes. Usual methods for invariant pattern recognition(Wood, 1996) have the constraint, that the permitted transformations are acting on the patterns directly. As opposed to that, in the recognition of three dimensional objects one has to deal with changing view directions, view distances, object background, illuminationsand maybe further imponderable changes. ....

Wood, J. (1996). Invariant pattern recognition - a review. Pattern Recognition, 29, 1--17.

....prior to application stage is to discover an appropriate compromise between invariance and reliability of object recognition. The greatest strength of the approach is the ability to learn (approximate) invariants under real world changes. Usual methods for invariant pattern recognition (Wood [12]) have the constraint that the permitted transformations are acting directly on the patterns. As opposed to that in the recognition of three dimensional objects one has to deal with changing view directions, view distances, object background, illuminations and maybe further imponderable changes. ....

J. Wood. Invariant pattern recognition - a review. Pattern Recognition, 29(1):1--17, 1996.

....retina requires the capability of pattern recognition, independent of their sizes and locations. These are known as the problems of scaling invariant and translationinvariant recognition. The literature contains a number of approaches for neural networks to achieve invariance, recently reviewed by Wood (1996). They may be summarised as those for which the network is trained on all possible examples of inputs and those which employ weight sharing techniques. Since the first option is an inefficient strategy due to the size of the data set demanded by the training process, we turn our attention to the ....

Wood, J. (1996) Invariant pattern recognition: A review. Pattern Recognition, 29, (1), 117.

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J. Wood, "Invariant pattern recognition: a review", Pattern Recognition, Vol. 29, No. 1, 1996, pp. 1-17.

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J. Wood. Invariant Pattern Recognition: A Review. Pattern Recognition, 29(1):1-- 17, January 1996.

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J. Wood. Invariant pattern recognition: a review. Pattern Recognition, 29(1):1--17, 1996.

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Jerey Wood. Invariant pattern recognition: a review. Pattern Recognition, 29(1):1-17, 1996.

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J. Wood. Invariant pattern recognition: a review. Pattern Recognition, 29(1):1-17, 1996.

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J. Wood `Invariant Pattern Recognition: a Review' Pattern Recognition 29 pp.1--17

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J. Wood. Invariant pattern recognition: A review. Pattern Recognition, 29(1):1--17, 1996.

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J. Wood. Invariant pattern recognition: A review. Pattern Recognition, 29(1):1--17, 1996.

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Jeffrey Wood, "Invariant pattern recognition : A review," Pattern Recognition, vol. 29, pp. 1-17, 1996.

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